system

Abstract

We provide the system. [Solution] A device attached to an animal provides means for collecting activity data and biometric information, A means for receiving and storing data transmitted from the aforementioned terminal, A means to provide a machine learning model that adaptively analyzes trends and anomalies using stored data, A means of providing notification via a communication device to propose adaptation measures based on the analysis results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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] In modern times, when raising animals, the owners are often busy and it is difficult to continuously manage the health of the animals. Also, it is not easy to detect abnormalities in the health status of animals early and take appropriate measures, and many owners feel difficulties in this process. In such a situation, there is a possibility that the health problems of animals will deteriorate, resulting in a decline in the quality of life of the animals. Therefore, there is a need for a technology that can efficiently monitor the health status of animals, detect abnormalities early, and provide appropriate care.

Means for Solving the Problems

[0005] This invention provides a system in which a terminal attached to an animal collects activity data and biometric information and transmits that data to a server. The server stores the received data and adaptively analyzes data trends and anomalies using a machine learning model. Based on the analysis results, appropriate measures are proposed to the owner via a communication device, making it possible to effectively manage the animal's health. Furthermore, the analysis model can be updated based on feedback from the owner, enabling more accurate analysis. In this way, this invention contributes to improving the efficiency and accuracy of animal health management.

[0006] A "terminal" is a device attached to an animal that collects activity data and biometric information and transmits it to a server.

[0007] "Activity data" refers to information that indicates the physical activity status of an animal, such as its exercise level and movement patterns.

[0008] "Biometric information" refers to data that indicates an animal's health indicators, including physiological information such as heart rate and body temperature.

[0009] A "server" is a device or system that receives data transmitted from a terminal, stores it, and analyzes it.

[0010] "To remember" refers to storing data in a way that allows it to be retrieved as needed.

[0011] A "machine learning model" is an algorithm or program used to identify patterns and anomalies in data, and is a learning system that automatically improves itself.

[0012] "Analysis results" refer to analytical evaluations and conclusions obtained through machine learning models based on the collected data.

[0013] A "communication device" is a hardware or software device used to send and receive data and information between a server and a user.

[0014] "Feedback" refers to information based on opinions and results provided by users, which is used to improve the system. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system consisting of a terminal attached to an animal, a server, and a user communication device, for the efficient management of animal health. Specific embodiments of this system are described below.

[0037] Device operation:

[0038] The device is attached to the animal and plays a role in collecting activity data and biometric information. For example, the device has a built-in accelerometer and heart rate sensor to measure the animal's activity level and heart rate. This data is transmitted to a server in real time. The device connects to the server via Bluetooth or Wi-Fi, allowing it to quickly transmit necessary information.

[0039] Server operation:

[0040] The server stores data received from the terminal and analyzes it using a machine learning model. For example, it uses machine learning to detect extreme decreases in activity levels compared to normal levels or abnormal heart rates. The analysis results are automatically evaluated, and abnormality warnings are generated as needed. Furthermore, based on the analysis results, the user is notified via a communication device of the animal's current health status and recommended actions.

[0041] How to use it:

[0042] Users receive notifications sent from the server via communication devices such as smartphones. Through the application, they can visually review the collected data and analysis results and adjust the animal's care plan as needed. For example, if a user receives a notification that their animal is not getting enough exercise, they can take specific actions such as increasing walks.

[0043] As a concrete example, suppose a device attached to a small dog collects the distance traveled and average heart rate during a walk that day. This data is sent to a server, and by comparing it with past data, it is analyzed that the dog's exercise level that day was significantly lower than usual. The server notifies the user of this information, and the user receives guidance through the app to increase the dog's walking time that day.

[0044] This system allows users to constantly monitor the health status of their animals and provide appropriate care. The entire system is automated, significantly reducing the burden of daily management.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The device is attached to the animal and collects activity data and biometric information. Specifically, it measures the amount of physical activity using an accelerometer and the heart rate using a heart rate sensor. This data is measured at regular intervals.

[0048] Step 2:

[0049] The device collects data and sends it to the server via Bluetooth or Wi-Fi. Transmission occurs in real time or periodically, and all data is time-stamped.

[0050] Step 3:

[0051] The server records the data it receives in the database. Data storage is optimized for efficient searching and access.

[0052] Step 4:

[0053] The server analyzes the recorded data using a machine learning model. In this step, it detects outliers (e.g., extreme lack of exercise or abnormal heart rate) by comparing them with past data.

[0054] Step 5:

[0055] Based on the analysis results, the server generates necessary actions and recommended care plans. For example, if a lack of exercise is detected, it will instruct the user to increase their exercise level.

[0056] Step 6:

[0057] The server generates a notification and sends it to the user via a communication device. The notification content arrives as a push message on the user's smartphone.

[0058] Step 7:

[0059] Users can check notifications through a smartphone app and understand care plans tailored to their animal's health condition. Based on the notifications, users can take appropriate actions for their pets.

[0060] Step 8:

[0061] Users send feedback about their actions to the server through the app. This feedback is recorded in a database and used to refine the model for future analysis.

[0062] (Example 1)

[0063] 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."

[0064] In recent years, efficiently collecting activity information and biometric data to quickly detect abnormalities and changes in health status has become crucial in animal health management. Traditional methods often involve manual data collection and analysis, leading to frequent errors and delays. As a result, immediate responses to animal health issues are difficult.

[0065] 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.

[0066] In this invention, the server includes means for a device attached to an animal to acquire activity information and biometric data, means for receiving and recording information transmitted from the device, and means for providing a machine learning method that automatically analyzes patterns and anomalies using the recorded information. This makes it possible to monitor the animal's health status in real time, detect anomalies immediately, and take countermeasures.

[0067] A "device attached to an animal" is a device that is attached to an animal's body in order to acquire information about the animal's activity and biometric data.

[0068] "Activity information and biometric data" refers to data related to the animal's health and behavior, such as its activity level and heart rate.

[0069] "Information transferred from the device" refers to activity information and biometric data acquired by the device that are sent to the server.

[0070] "Means for receiving and recording" refers to a system in which a server receives information transmitted from a device and stores it in a database or similar.

[0071] "Machine learning methods that automatically analyze patterns and anomalies" refer to algorithms or models that learn normal activity patterns based on collected data and identify events that should be detected as anomalies.

[0072] "Means of providing notifications via information terminals" refers to communication methods used to inform users of the animal's health status or any abnormalities based on the analysis results.

[0073] This invention is a system for efficiently managing the health of animals, consisting of a terminal attached to the animal, a server for analyzing the data, and a communication device for the user receiving the information. Specific embodiments for carrying out the invention are described below.

[0074] Terminal processing

[0075] The device is attached to the animal and is equipped with devices such as an accelerometer and a heart rate sensor. This allows it to collect information on the animal's activity and biometric data, specifically its exercise level and heart rate. The device transmits the collected data to a server via Bluetooth or Wi-Fi, providing information in real time. Furthermore, encryption technology is used during data transmission to ensure security.

[0076] Server Processing

[0077] The server receives information transmitted from the terminal and stores it in a database. The received data is pre-processed using signal processing techniques to remove noise and prepare it for analysis. The server then analyzes the data using machine learning methods to detect anomalies in activity patterns and changes in health status. Generative AI models are used for analysis, enabling highly accurate anomaly detection. Based on the analysis results, the server notifies the user of the detected anomalies and recommended countermeasures.

[0078] User roles

[0079] Users receive notifications from the server using communication devices such as smartphones and tablets. The application allows users to visually display information about their animals' health status and activity. This enables users to immediately plan and implement appropriate care, helping them manage their animals' health.

[0080] As a concrete example, consider a system where a device attached to a small dog detects if its exercise level decreases for the day, and the server notifies the user. In this case, the user can receive a notification such as, "Your dog hasn't exercised enough today, so we recommend giving it some extra exercise."

[0081] An example of a prompt to be input to the generative AI model is the text: "If a small dog is not meeting its daily exercise goal, suggest how to increase its exercise." Using this prompt, the server can have the generative AI model generate an appropriate action plan.

[0082] This system allows for efficient and effective management of animal health, reducing the burden on users. This automated approach is expected to make daily health management easier and more precise.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The device is attached to the animal and collects activity information and biometric data using an accelerometer and a heart rate sensor. The input is the animal's activity level and heart rate, which are obtained as numerical data. Specifically, the device measures acceleration at regular intervals and calculates the heart rate over a certain period using the heart rate sensor. This data is temporarily stored in a buffer.

[0086] Step 2:

[0087] The device transmits the collected data to the server in real time. The input is the activity information and biometric data obtained in step 1. The output is a response confirming that the data has reached the server. The device encrypts the data via Bluetooth or Wi-Fi, packets it, and sends it to the server's receiving system.

[0088] Step 3:

[0089] The server saves the received data to the database. The input is the activity information and biometric data sent in step 2. The output is a status indicating that recording to the database is complete. The server organizes the received data as time-series data, checks for missing or outlier data, and then saves it.

[0090] Step 4:

[0091] The server performs signal processing on the stored data and analyzes it using a machine learning model. The input is the data recorded in step 3, and the output is the result of anomaly detection. Specifically, the server filters the data and inputs it into a generative AI model to detect deviations from normal patterns.

[0092] Step 5:

[0093] Based on the analysis results, the server notifies the user of any anomalies if necessary. The input is the anomaly detection result obtained in step 4. The output is the notification message sent to the user's smartphone or tablet. The server generates a prompt message to inform the user of the animal's health status and recommended actions.

[0094] Step 6:

[0095] The user uses a communication device to check notifications from the server and adjust the animal care plan as needed. Input is the notification message received from the server, and output is the change in the user's action plan. The user accesses the application and makes specific decisions based on the visualized data.

[0096] This series of processes allows for real-time monitoring of the animals' health status and immediate action as needed.

[0097] (Application Example 1)

[0098] 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."

[0099] In security operations using animals, it is necessary to constantly monitor the animals' health and stress levels and respond quickly. However, with conventional methods, it is difficult to grasp changes in the animals' health in real time, and there is a possibility that security operations may continue without noticing any abnormalities. In such situations, not only is the safety of the animals compromised, but the effectiveness of security is reduced, and as a result, there is a risk of human and material losses. Therefore, there is a need to develop a system that efficiently and accurately manages the health of security animals and immediately notifies supervisors of the information.

[0100] 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.

[0101] In this invention, the server includes a device for collecting activity data and biometric information, a device for receiving and storing data transmitted from a terminal, and a device for providing a machine learning model that adaptively analyzes trends and anomalies using the stored data. This makes it possible to monitor the health status and stress levels of guard animals in real time and to immediately notify the monitor when an anomaly is detected.

[0102] A "device" is a collection of hardware and software configured to perform a specific function.

[0103] A "terminal" is a device attached to an animal to collect activity data and biometric information.

[0104] A "communication terminal" is a device used to exchange information with users and other systems.

[0105] A "machine learning model" is a collection of algorithms that learn patterns from data and perform predictions and classifications.

[0106] A "visual display terminal" is a device used to present information to users visually.

[0107] A "device for storing information" refers to memory or storage systems used to temporarily or long-term store data.

[0108] A "notification device" is a device equipped with a means of conveying information to a user.

[0109] A "feedback reflection device" is a device that makes adjustments to improve the system's operation based on user input.

[0110] The system for implementing this invention consists of a terminal attached to an animal, a server for receiving and analyzing data, and a communication terminal for the user that presents the information. The terminal is equipped with sensors that collect animal activity data and biometric information, including, for example, a heart rate sensor and an accelerometer. The data obtained from these sensors is transmitted from the terminal to the server via Bluetooth or Wi-Fi. This data collection and transmission is performed in real time.

[0111] The server includes a processing unit that stores received data in a storage device and analyzes the data using a machine learning model. The machine learning model learns the normal activity patterns and health status of animals and detects anomalies by comparing them with the collected data. For example, it identifies changes in heart rate and decreased activity levels to find signs of stress or fatigue. If an anomaly is detected, the server generates an alert and immediately notifies the user of this information.

[0112] Users receive notifications from the server using a communication terminal. This terminal can take the form of smart glasses used by security guards or a smartphone, and provides visual information. Furthermore, machine learning models can be adjusted based on user feedback, and this information can be reflected in subsequent data analyses.

[0113] As a concrete example, when this system is attached to security dogs guarding important facilities at night, the server will send a real-time alert to the security guard's smart glasses if the dog starts to show signs of fatigue or if its activity level falls below normal. This allows the security guard to quickly devise a response and allow the dog to rest.

[0114] Examples of prompts for a generative AI model are as follows:

[0115] "What procedures and technologies are necessary to develop a system that analyzes a dog's health data, identifies abnormalities, and notifies security guards in real time? Please provide an example using Python and sensors. Include specific software libraries and APIs in your answer."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The device is attached to the animal and collects activity data and biometric information. Specifically, heart rate sensors and accelerometers measure the animal's heart rate and activity level, generating this data. The input is biometric information acquired by the sensors, and the output is digital data containing this information.

[0119] Step 2:

[0120] The device transmits the collected data to the server in real time. Specifically, it transmits digital data via Bluetooth or Wi-Fi. The input is the collected digital data, and the output is the data stream sent to the server via wireless communication.

[0121] Step 3:

[0122] The server receives data sent from the terminal and stores it in storage. Specifically, this involves receiving data and writing to the database. The input is a data stream sent from the terminal, and the output is data records stored in storage.

[0123] Step 4:

[0124] The server performs analysis using machine learning models with the accumulated data. Specifically, it preprocesses the data, generates input vectors for adaptive analysis, and detects trends and anomalies through machine learning algorithms. The input is data records stored in memory, and the output is anomaly detection information as an analysis result.

[0125] Step 5:

[0126] The server notifies the user's communication terminal of anomaly detection information based on the analysis. Specifically, it generates a warning message and sends it to the communication terminal via the network. The input is the anomaly detection information as a result of the analysis, and the output is the warning message displayed on the user's communication terminal.

[0127] Step 6:

[0128] The user receives notifications from the server via a communication device. Specifically, a warning message pops up on the user's smart glasses or smartphone, allowing them to take necessary actions based on it. The input is the warning message sent from the server, and the output is the user's prompt response and its result.

[0129] 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.

[0130] This invention is a system in which a terminal, server, emotion engine, and user communication device work together to provide advanced animal health management. By incorporating an emotion engine, it is possible to propose health care that takes the user's emotions into consideration.

[0131] Device operation:

[0132] The device is attached to the animal and is responsible for continuously collecting activity data and biometric information. This data, including exercise levels and heart rate, is acquired by multiple sensors and transmitted to a server via Bluetooth or Wi-Fi.

[0133] Server operation:

[0134] The server receives and stores data sent from the terminal. This data is analyzed by a machine learning model, and the results are used to assess the animal's health status. The assessment results are notified to the user via a communication device, and appropriate care is suggested.

[0135] How the emotion engine works:

[0136] The emotion engine uses the user's device (e.g., smartphone) camera and microphone to analyze the user's emotions from their facial expressions and voice. Based on this analysis, the server customizes care plans and notifications, providing information that takes into account the user's situation and emotions.

[0137] How to use it:

[0138] Users utilize a smartphone app to receive notifications from the server. These notifications include animal health information, which users can use to adjust their pet's care. Furthermore, the system is expected to provide more appropriate responses by offering suggestions that take the user's emotions into consideration. For example, users experiencing stress will receive suggestions for pet care that are manageable and not overwhelming.

[0139] As a concrete example, suppose a device collects data on a dog's exercise level, and the server analyzes this data to determine that the dog is less active than normal. If the emotion engine determines from the user's facial expression that the dog is tired, the server will suggest exercise within reasonable limits and send a notification to the user such as, "Let's try a short walk this weekend."

[0140] This system enables flexible care that considers not only the animal's health but also the user's mental condition. It is an implementation that supports health management while reducing the burden on both the user and the animal.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The device is attached to the animal and collects activity data and biometric information. Specifically, an accelerometer built into the device measures the amount of exercise, and a heart rate sensor records the heart rate. The data is recorded at regular intervals and converted into a format that can be transmitted to a server.

[0144] Step 2:

[0145] The device collects data and transmits it to the server in real time. It connects to the server using communication methods such as Bluetooth or Wi-Fi, and the data is transmitted encrypted. This ensures that the server always stores the latest health information.

[0146] Step 3:

[0147] The server receives the transmitted data and records it in the database. The received data is timestamped and stored efficiently. The data is then used for analysis by machine learning models.

[0148] Step 4:

[0149] The server uses a machine learning model to analyze incoming data. It compares this data with past data to assess the animal's health and detect abnormalities. For example, it generates warnings if exercise levels do not meet the standard or if the heart rate falls outside the specified range.

[0150] Step 5:

[0151] The server checks the user's emotional state via an emotion engine on the user's device. It estimates emotions from the user's facial expressions and tone of voice using the device's camera and microphone. Based on the results of the emotion analysis, it adjusts the notification content.

[0152] Step 6:

[0153] The server generates advice on animal care based on analysis results and emotional state, and notifies the user via a communication device. For example, if the user is tired, it will suggest a care plan that reduces the burden.

[0154] Step 7:

[0155] The user receives a notification via a communication device and checks its contents on a smartphone app. Based on the health information and care plan, the user takes specific care actions for their animal. This includes actions such as adjusting the frequency of walks according to the suggestions.

[0156] Step 8:

[0157] Users send feedback about care to the server through the app. This feedback includes individual impressions and information about the effectiveness of the care. The server then uses this feedback to improve its machine learning model and incorporate it into subsequent analyses.

[0158] (Example 2)

[0159] 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".

[0160] In managing the health status of animals, conventional technologies have been insufficient in collecting activity and health information, making it difficult to detect abnormalities in a timely manner and take effective action. Furthermore, because care suggestions cannot take into account the user's mental state, the users themselves experience significant emotional burden.

[0161] 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.

[0162] In this invention, the server includes means for a device attached to the animal to collect exercise and health information, means for customizing notification content using emotion analysis results, and means for presenting countermeasures via an information transmission device. This enables accurate monitoring of the animal's health status and flexible care suggestions that take into account the user's mental state.

[0163] A "device" is a piece of equipment attached to an animal to collect information on its movement and health.

[0164] "Movement information" refers to data related to an animal's movement, such as the number of steps taken and the distance traveled.

[0165] "Health information" refers to data that indicates the biological state of an animal, such as its heart rate and body temperature.

[0166] "To save" means to retain received information on a recording medium such as a database.

[0167] A "learning algorithm" is a computer program that uses collected information to analyze anomalies and assess the health status of animals.

[0168] An "information transmission device" is a means of communication used to notify users of analysis results and countermeasures.

[0169] The "emotion analysis function" is a means of determining a user's emotions from their facial expressions and voice, and is used to individually adjust notification content.

[0170] One embodiment of the present invention provides a care system that simultaneously considers the animal's health condition and the user's mental state. This system is realized by combining a device, a server, an emotion analysis function, and a user control device.

[0171] The device can be attached to animals to collect exercise and health information. Specifically, it has built-in accelerometers and heart rate sensors to collect data such as the animal's steps and heart rate. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0172] The server receives information sent from the terminal and stores it in a database. The stored information is analyzed using learning algorithms such as Python's scikit-learn and TENSORFLOW® to evaluate the animal's health status. If an abnormality is detected as a result of the evaluation, appropriate actions are suggested.

[0173] The emotion analysis function analyzes the user's facial expressions and voice through the user's device (e.g., smartphone). This is achieved using OpenCV or similar technologies to evaluate the user's mental state. The evaluation results are used to individually adjust the care plan proposed by the server.

[0174] Users receive notifications from the server via a control device and implement care plans based on the animal's health status. The notifications reflect the results of sentiment analysis, suggesting actions that will not burden the user.

[0175] As a concrete example, if the device detects that the dog is not getting enough exercise and determines that the user is stressed, the server could send a notification such as, "We recommend a short walk on the weekend to help the dog relax."

[0176] Examples of prompts include the following:

[0177] "Please explain how this system works and how to implement pet care suggestions that take the user's emotional state into account."

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The device, when attached to an animal, collects exercise and health information. Specifically, an accelerometer and heart rate sensor built into the device detect the animal's steps and heart rate. This information is temporarily stored in the device's data storage and prepared for the next transmission. The input is the animal's movement and heart rate, and the output is stored in the device as numerical data.

[0181] Step 2:

[0182] The terminal sends the collected data to the server. Communication takes place via Bluetooth or Wi-Fi, and data is sent to the server in real time or in batch processing. When the server receives the data, it is recorded in the database. The input is data packets from the terminal, and the output is the information registered in the server's database.

[0183] Step 3:

[0184] The server analyzes stored information based on a learning algorithm. It uses Python's scikit-learn and TensorFlow to detect anomalies in movement data and biological data. The data is evaluated using statistical methods and machine learning models. The input is stored animal data, and the output is evaluation results and anomaly warnings.

[0185] Step 4:

[0186] The user control device, equipped with emotion analysis capabilities, analyzes the user's facial expressions and voice. This analysis is performed using a camera and microphone built into the control device, and utilizes OpenCV or other related software. The input is the user's facial expressions and voice information, and the output is the determined emotional state.

[0187] Step 5:

[0188] The server generates a care plan by combining the analysis results and the emotional analysis results. The care plan creation takes into account the emotional analysis performed by the user and the analysis results of the animal's health status, resulting in a customized proposal. The inputs are the animal's health assessment and the user's emotional state, and the output is an individualized care proposal.

[0189] Step 6:

[0190] The server notifies the user's device of the generated care plan. Real-time notifications are sent using services such as Firebase Cloud Messaging and Apple Push Notification Service to inform the user of their health status and recommended actions. The input is a customized care plan, and the output is displayed as a push notification on the user's device.

[0191] Step 7:

[0192] The user checks notifications received through the control device and performs animal care based on the information. This allows the user to take appropriate actions according to the animal's condition. The input is the notified care information, and the output is the actual care action.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] In modern factories, there is a need to simultaneously optimize machine operating efficiency and the working environment for operators. However, conventional systems were unable to comprehensively understand the state of the machines and the emotions of the operators, and adjust the work process accordingly. As a result, problems such as decreased work efficiency and increased operator stress often occurred.

[0196] 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.

[0197] In this invention, the server includes means for collecting operational status data and ambient environmental information from devices attached to the machine, means for providing a learning model that adaptively analyzes the state of the machine using the stored data, and means for providing notifications via a communication device to analyze the operator's emotions and adjust the work process. This enables integrated optimization of the work process that takes into account both the machine's operational status and the operator's emotions.

[0198] A "device" is an instrument that, when attached to a machine, has the function of collecting operating status data and surrounding environment information.

[0199] "Data" is a general term for information collected from a device regarding the operating status of the machine and the surrounding environment.

[0200] A "learning model" is a computational model that has algorithms for adaptively analyzing the state of a machine using collected data.

[0201] "Analysis tools" refer to systems and processes designed to analyze the emotions of operators.

[0202] A "communication device" is a device used to transmit notifications from a server to an operator, and is a device that has the function of transmitting information bidirectionally.

[0203] A "feedback reflection means" is a system or process that has the function of updating the analysis model based on feedback information obtained from the operator.

[0204] A "notification" refers to a message that conveys appropriate information to the operator based on the results of the analysis.

[0205] The system implementing this invention is designed to optimize work processes by monitoring the emotional states of factory machinery and operators in real time. A server collects operational status data and ambient environmental information from devices attached to the machinery. The collected data is transmitted using communication technologies such as Bluetooth or Wi-Fi. The server stores this data and applies a learning model to analyze the machine's state.

[0206] Specifically, the server uses a learning model to analyze the machine's operating status and determine if there are any abnormalities. The learning model used is optimized based on past data. The server also evaluates emotional data acquired through the operator's smartphone or smart glasses using emotion analysis software. This allows the server to understand the operator's stress levels and fatigue, and adjust the work process accordingly.

[0207] The user (operator) receives notifications from the server via a communication device. These notifications include the machine's current operating status and recommended work adjustments, allowing the operator to take prompt action. Furthermore, the operator's feedback is used to improve the accuracy of the server's analysis model.

[0208] As a concrete example, consider a scenario where a factory operator encounters a machine malfunction. This system can simultaneously grasp machine operation data and the operator's emotions, and automatically provide appropriate countermeasures based on a prompt message such as, "Analyze the operator's current emotional state from their facial expressions and voice, and create an operational adjustment plan for the factory robot based on the results." This is expected to improve work efficiency and reduce operator stress.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server receives operating status data and ambient environment information from terminals attached to the machine. Multiple sensors attached to the terminals collect necessary data such as operating energy, temperature, and vibration from the machine in operation and transmit this data to the server via Bluetooth or Wi-Fi. The input for this step is raw data from the sensors, and the output is processed data stored in a database on the server.

[0212] Step 2:

[0213] The server uses the received data to perform analysis using a machine learning model. The stored data is preprocessed and prepared as an input dataset for executing an anomaly detection algorithm. This determines whether the machine's operating status is normal or abnormal, and also evaluates its efficiency. The input for this step is preprocessed data, and the output is the result of the machine's status evaluation.

[0214] Step 3:

[0215] The server acquires emotional data from the operator's smart device and analyzes it using an emotion engine. The user's device uses a camera and microphone to record facial expressions and voice, and this data is processed by the emotion engine. Based on this data, the emotion engine quantifies stress levels and fatigue levels. The input for this step is facial expression and voice data, and the output is an index representing the emotional state.

[0216] Step 4:

[0217] The server generates a notification to adjust the work process appropriately based on the analysis results. It considers the operator's emotional state and combines it with the machine learning analysis results to determine the optimal work adjustment plan. At this stage, a generative AI model is used to create recommendations that follow the prompt. Finally, the server sends the notification to the operator via a communication device. The inputs to this step are state evaluations and emotional indicators, and the output is a notification indicating specific work adjustment methods.

[0218] Step 5:

[0219] The user (operator) receives notifications from the server and takes appropriate action. Based on the proposed changes to the work process, they review the current situation and adjust machine operation and their own work procedures to improve work efficiency and safety. The input for this step is the notification from the communication device, and the output is the operator's response.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention is a system consisting of a terminal attached to an animal, a server, and a user communication device, for the efficient management of animal health. Specific embodiments of this system are described below.

[0237] Device operation:

[0238] The device is attached to the animal and plays a role in collecting activity data and biometric information. For example, the device has a built-in accelerometer and heart rate sensor to measure the animal's activity level and heart rate. This data is transmitted to a server in real time. The device connects to the server via Bluetooth or Wi-Fi, allowing it to quickly transmit necessary information.

[0239] Server operation:

[0240] The server stores data received from the terminal and analyzes it using a machine learning model. For example, it uses machine learning to detect extreme decreases in activity levels compared to normal levels or abnormal heart rates. The analysis results are automatically evaluated, and abnormality warnings are generated as needed. Furthermore, based on the analysis results, the user is notified via a communication device of the animal's current health status and recommended actions.

[0241] How to use it:

[0242] Users receive notifications sent from the server via communication devices such as smartphones. Through the application, they can visually review the collected data and analysis results and adjust the animal's care plan as needed. For example, if a user receives a notification that their animal is not getting enough exercise, they can take specific actions such as increasing walks.

[0243] As a concrete example, suppose a device attached to a small dog collects the distance traveled and average heart rate during a walk that day. This data is sent to a server, and by comparing it with past data, it is analyzed that the dog's exercise level that day was significantly lower than usual. The server notifies the user of this information, and the user receives guidance through the app to increase the dog's walking time that day.

[0244] This system allows users to constantly monitor the health status of their animals and provide appropriate care. The entire system is automated, significantly reducing the burden of daily management.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The device is attached to the animal and collects activity data and biometric information. Specifically, it measures the amount of physical activity using an accelerometer and the heart rate using a heart rate sensor. This data is measured at regular intervals.

[0248] Step 2:

[0249] The device collects data and sends it to the server via Bluetooth or Wi-Fi. Transmission occurs in real time or periodically, and all data is time-stamped.

[0250] Step 3:

[0251] The server records the data it receives in the database. Data storage is optimized for efficient searching and access.

[0252] Step 4:

[0253] The server analyzes the recorded data using a machine learning model. In this step, it detects outliers (e.g., extreme lack of exercise or abnormal heart rate) by comparing them with past data.

[0254] Step 5:

[0255] Based on the analysis results, the server generates necessary actions and recommended care plans. For example, if a lack of exercise is detected, it will instruct the user to increase their exercise level.

[0256] Step 6:

[0257] The server generates a notification and sends it to the user via a communication device. The notification content arrives as a push message on the user's smartphone.

[0258] Step 7:

[0259] Users can check notifications through a smartphone app and understand care plans tailored to their animal's health condition. Based on the notifications, users can take appropriate actions for their pets.

[0260] Step 8:

[0261] Users send feedback about their actions to the server through the app. This feedback is recorded in a database and used to refine the model for future analysis.

[0262] (Example 1)

[0263] 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."

[0264] In recent years, efficiently collecting activity information and biometric data to quickly detect abnormalities and changes in health status has become crucial in animal health management. Traditional methods often involve manual data collection and analysis, leading to frequent errors and delays. As a result, immediate responses to animal health issues are difficult.

[0265] 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.

[0266] In this invention, the server includes means for a device attached to an animal to acquire activity information and biometric data, means for receiving and recording information transmitted from the device, and means for providing a machine learning method that automatically analyzes patterns and anomalies using the recorded information. This makes it possible to monitor the animal's health status in real time, detect anomalies immediately, and take countermeasures.

[0267] A "device attached to an animal" is a device that is attached to an animal's body in order to acquire information about the animal's activity and biometric data.

[0268] "Activity information and biometric data" refers to data related to the animal's health and behavior, such as its activity level and heart rate.

[0269] "Information transferred from the device" refers to activity information and biometric data acquired by the device that are sent to the server.

[0270] "Means for receiving and recording" refers to a system in which a server receives information transmitted from a device and stores it in a database or similar.

[0271] "Machine learning methods that automatically analyze patterns and anomalies" refer to algorithms or models that learn normal activity patterns based on collected data and identify events that should be detected as anomalies.

[0272] "Means of providing notifications via information terminals" refers to communication methods used to inform users of the animal's health status or any abnormalities based on the analysis results.

[0273] This invention is a system for efficiently managing the health of animals, consisting of a terminal attached to the animal, a server for analyzing the data, and a communication device for the user receiving the information. Specific embodiments for carrying out the invention are described below.

[0274] Terminal processing

[0275] The device is attached to the animal and is equipped with devices such as an accelerometer and a heart rate sensor. This allows it to collect information on the animal's activity and biometric data, specifically its exercise level and heart rate. The device transmits the collected data to a server via Bluetooth or Wi-Fi, providing information in real time. Furthermore, encryption technology is used during data transmission to ensure security.

[0276] Server Processing

[0277] The server receives information transmitted from the terminal and stores it in a database. The received data is pre-processed using signal processing techniques to remove noise and prepare it for analysis. The server then analyzes the data using machine learning methods to detect anomalies in activity patterns and changes in health status. Generative AI models are used for analysis, enabling highly accurate anomaly detection. Based on the analysis results, the server notifies the user of the detected anomalies and recommended countermeasures.

[0278] User roles

[0279] Users receive notifications from the server using communication devices such as smartphones and tablets. The application allows users to visually display information about their animals' health status and activity. This enables users to immediately plan and implement appropriate care, helping them manage their animals' health.

[0280] As a concrete example, consider a system where a device attached to a small dog detects if its exercise level decreases for the day, and the server notifies the user. In this case, the user can receive a notification such as, "Your dog hasn't exercised enough today, so we recommend giving it some extra exercise."

[0281] An example of a prompt to be input to the generative AI model is the text: "If a small dog is not meeting its daily exercise goal, suggest how to increase its exercise." Using this prompt, the server can have the generative AI model generate an appropriate action plan.

[0282] This system allows for efficient and effective management of animal health, reducing the burden on users. This automated approach is expected to make daily health management easier and more precise.

[0283] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0284] Step 1:

[0285] The terminal is attached to the animal, and activity information and biological data are collected by an acceleration sensor and a heart rate sensor. The input is the momentum and heart rate of the animal, which are obtained as numerical data. Specifically, the terminal measures the acceleration at regular intervals, and the heart rate sensor calculates the heart rate over a certain period. This data is temporarily stored in a buffer.

[0286] Step 2:

[0287] The data collected by the terminal is sent to the server in real time. The input is the activity information and biological data obtained in Step 1. The output is a response to confirm that the data has reached the server. The terminal encrypts and packets the data via Bluetooth or Wi-Fi and sends it to the server's receiving system.

[0288] Step 3:

[0289] The data received by the server is stored in a database. The input is the activity information and biological data sent in Step 2. The output is a status indicating that the recording to the database has been completed. The server organizes the received data as time-series data, checks for any missing or outlier values, and then stores it.

[0290] Step 4:

[0291] Signal processing is performed on the data stored by the server and analyzed using a machine learning model. The input is the data recorded in Step 3, and the output is the result of anomaly detection. Specifically, the server filters the data and inputs it into a generative AI model to detect deviations from the normal pattern.

[0292] Step 5:

[0293] Based on the analysis results, the server notifies the user of any anomalies if necessary. The input is the anomaly detection result obtained in step 4. The output is the notification message sent to the user's smartphone or tablet. The server generates a prompt message to inform the user of the animal's health status and recommended actions.

[0294] Step 6:

[0295] The user uses a communication device to check notifications from the server and adjust the animal care plan as needed. Input is the notification message received from the server, and output is the change in the user's action plan. The user accesses the application and makes specific decisions based on the visualized data.

[0296] This series of processes allows for real-time monitoring of the animals' health status and immediate action as needed.

[0297] (Application Example 1)

[0298] 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."

[0299] In security operations using animals, it is necessary to constantly monitor the animals' health and stress levels and respond quickly. However, with conventional methods, it is difficult to grasp changes in the animals' health in real time, and there is a possibility that security operations may continue without noticing any abnormalities. In such situations, not only is the safety of the animals compromised, but the effectiveness of security is reduced, and as a result, there is a risk of human and material losses. Therefore, there is a need to develop a system that efficiently and accurately manages the health of security animals and immediately notifies supervisors of the information.

[0300] 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.

[0301] In this invention, the server includes a device for collecting activity data and biometric information, a device for receiving and storing data transmitted from a terminal, and a device for providing a machine-learning model that adaptively analyzes trends and anomalies using the stored data. Thereby, it becomes possible to monitor the health condition and stress level of a security animal in real time and to immediately notify a monitor of information when an anomaly is detected.

[0302] The "device" is a collection of hardware and software configured to realize a specific function.

[0303] The "terminal" is a device attached to an animal for collecting activity data and biometric information.

[0304] The "communication terminal" is a device for exchanging information with a user or other systems.

[0305] The "machine-learning model" is an aggregate of algorithms for learning patterns based on data and performing prediction and classification.

[0306] The "visual display terminal" is a device for visually presenting information to a user.

[0307] The "device for storing" is a memory or storage system for temporarily or long-term storing data.

[0308] The "device for notifying" is a device equipped with means for conveying information to a user.

[0309] The "feedback reflection device" is a device for making adjustments for improving the operation of a system based on an input from a user.

[0310] The system for implementing this invention consists of a terminal attached to an animal, a server for receiving and analyzing data, and a communication terminal for the user that presents the information. The terminal is equipped with sensors that collect animal activity data and biometric information, including, for example, a heart rate sensor and an accelerometer. The data obtained from these sensors is transmitted from the terminal to the server via Bluetooth or Wi-Fi. This data collection and transmission is performed in real time.

[0311] The server includes a processing unit that stores received data in a storage device and analyzes the data using a machine learning model. The machine learning model learns the normal activity patterns and health status of animals and detects anomalies by comparing them with the collected data. For example, it identifies changes in heart rate and decreased activity levels to find signs of stress or fatigue. If an anomaly is detected, the server generates an alert and immediately notifies the user of this information.

[0312] Users receive notifications from the server using a communication terminal. This terminal can take the form of smart glasses used by security guards or a smartphone, and provides visual information. Furthermore, machine learning models can be adjusted based on user feedback, and this information can be reflected in subsequent data analyses.

[0313] As a concrete example, when this system is attached to security dogs guarding important facilities at night, the server will send a real-time alert to the security guard's smart glasses if the dog starts to show signs of fatigue or if its activity level falls below normal. This allows the security guard to quickly devise a response and allow the dog to rest.

[0314] Examples of prompts for a generative AI model are as follows:

[0315] "What procedures and technologies are necessary to develop a system that analyzes a dog's health data, identifies abnormalities, and notifies security guards in real time? Please provide an example using Python and sensors. Include specific software libraries and APIs in your answer."

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The device is attached to the animal and collects activity data and biometric information. Specifically, heart rate sensors and accelerometers measure the animal's heart rate and activity level, generating this data. The input is biometric information acquired by the sensors, and the output is digital data containing this information.

[0319] Step 2:

[0320] The device transmits the collected data to the server in real time. Specifically, it transmits digital data via Bluetooth or Wi-Fi. The input is the collected digital data, and the output is the data stream sent to the server via wireless communication.

[0321] Step 3:

[0322] The server receives data sent from the terminal and stores it in storage. Specifically, this involves receiving data and writing to the database. The input is a data stream sent from the terminal, and the output is data records stored in storage.

[0323] Step 4:

[0324] The server performs analysis using machine learning models with the accumulated data. Specifically, it preprocesses the data, generates input vectors for adaptive analysis, and detects trends and anomalies through machine learning algorithms. The input is data records stored in memory, and the output is anomaly detection information as an analysis result.

[0325] Step 5:

[0326] The server notifies the user's communication terminal of anomaly detection information based on the analysis. Specifically, it generates a warning message and sends it to the communication terminal via the network. The input is the anomaly detection information as a result of the analysis, and the output is the warning message displayed on the user's communication terminal.

[0327] Step 6:

[0328] The user receives notifications from the server via a communication device. Specifically, a warning message pops up on the user's smart glasses or smartphone, allowing them to take necessary actions based on it. The input is the warning message sent from the server, and the output is the user's prompt response and its result.

[0329] 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.

[0330] This invention is a system in which a terminal, server, emotion engine, and user communication device work together to provide advanced animal health management. By incorporating an emotion engine, it is possible to propose health care that takes the user's emotions into consideration.

[0331] Device operation:

[0332] The device is attached to the animal and is responsible for continuously collecting activity data and biometric information. This data, including exercise levels and heart rate, is acquired by multiple sensors and transmitted to a server via Bluetooth or Wi-Fi.

[0333] Server operation:

[0334] The server receives and stores data sent from the terminal. This data is analyzed by a machine learning model, and the results are used to assess the animal's health status. The assessment results are notified to the user via a communication device, and appropriate care is suggested.

[0335] How the emotion engine works:

[0336] The emotion engine uses the user's device (e.g., smartphone) camera and microphone to analyze the user's emotions from their facial expressions and voice. Based on this analysis, the server customizes care plans and notifications, providing information that takes into account the user's situation and emotions.

[0337] How to use it:

[0338] Users utilize a smartphone app to receive notifications from the server. These notifications include animal health information, which users can use to adjust their pet's care. Furthermore, the system is expected to provide more appropriate responses by offering suggestions that take the user's emotions into consideration. For example, users experiencing stress will receive suggestions for pet care that are manageable and not overwhelming.

[0339] As a concrete example, suppose a device collects data on a dog's exercise level, and the server analyzes this data to determine that the dog is less active than normal. If the emotion engine determines from the user's facial expression that the dog is tired, the server will suggest exercise within reasonable limits and send a notification to the user such as, "Let's try a short walk this weekend."

[0340] This system enables flexible care that considers not only the animal's health but also the user's mental condition. It is an implementation that supports health management while reducing the burden on both the user and the animal.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The device is attached to the animal and collects activity data and biometric information. Specifically, an accelerometer built into the device measures the amount of exercise, and a heart rate sensor records the heart rate. The data is recorded at regular intervals and converted into a format that can be transmitted to a server.

[0344] Step 2:

[0345] The device collects data and transmits it to the server in real time. It connects to the server using communication methods such as Bluetooth or Wi-Fi, and the data is transmitted encrypted. This ensures that the server always stores the latest health information.

[0346] Step 3:

[0347] The server receives the transmitted data and records it in the database. The received data is timestamped and stored efficiently. The data is then used for analysis by machine learning models.

[0348] Step 4:

[0349] The server uses a machine learning model to analyze incoming data. It compares this data with past data to assess the animal's health and detect abnormalities. For example, it generates warnings if exercise levels do not meet the standard or if the heart rate falls outside the specified range.

[0350] Step 5:

[0351] The server checks the user's emotional state via an emotion engine on the user's device. It estimates emotions from the user's facial expressions and tone of voice using the device's camera and microphone. Based on the results of the emotion analysis, it adjusts the notification content.

[0352] Step 6:

[0353] The server generates advice on animal care based on analysis results and emotional state, and notifies the user via a communication device. For example, if the user is tired, it will suggest a care plan that reduces the burden on the animal.

[0354] Step 7:

[0355] The user receives a notification via a communication device and checks its contents on a smartphone app. Based on the health information and care plan, the user takes specific care actions for their animal. This includes actions such as adjusting the frequency of walks according to the suggestions.

[0356] Step 8:

[0357] Users send feedback about care to the server through the app. This feedback includes individual impressions and information about the effectiveness of the care. The server then uses this feedback to improve its machine learning model and incorporate it into subsequent analyses.

[0358] (Example 2)

[0359] 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".

[0360] In managing the health status of animals, conventional technologies have been insufficient in collecting activity and health information, making it difficult to detect abnormalities in a timely manner and take effective action. Furthermore, the inability to propose care that takes into account the user's mental state resulted in a significant mental burden on the user themselves.

[0361] 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.

[0362] In this invention, the server includes means for a device attached to the animal to collect exercise and health information, means for customizing notification content using emotion analysis results, and means for presenting countermeasures via an information transmission device. This enables accurate monitoring of the animal's health status and flexible care suggestions that take into account the user's mental state.

[0363] A "device" is a piece of equipment attached to an animal to collect information on its movement and health.

[0364] "Movement information" refers to data related to an animal's movement, such as the number of steps taken and the distance traveled.

[0365] "Health information" refers to data that indicates the biological state of an animal, such as its heart rate and body temperature.

[0366] "To save" means to retain received information on a recording medium such as a database.

[0367] A "learning algorithm" is a computer program that uses collected information to analyze anomalies and assess the health status of animals.

[0368] An "information transmission device" is a means of communication used to notify users of analysis results and countermeasures.

[0369] The "emotion analysis function" is a means of determining a user's emotions from their facial expressions and voice, and is used to individually adjust notification content.

[0370] One embodiment of the present invention provides a care system that simultaneously considers the animal's health condition and the user's mental state. This system is realized by combining a device, a server, an emotion analysis function, and a user control device.

[0371] The device can be attached to animals to collect exercise and health information. Specifically, it has built-in accelerometers and heart rate sensors to collect data such as the animal's steps and heart rate. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0372] The server receives information sent from the terminal and stores it in a database. The stored information is analyzed using learning algorithms such as Python's scikit-learn and TensorFlow to evaluate the animal's health status. If an abnormality is detected as a result of the evaluation, appropriate actions are suggested.

[0373] The emotion analysis function analyzes the user's facial expressions and voice through the user's device (e.g., smartphone). This is achieved using OpenCV or similar technologies to evaluate the user's mental state. The evaluation results are used to individually adjust the care plan proposed by the server.

[0374] Users receive notifications from the server via a control device and implement care plans based on the animal's health status. The notifications reflect the results of sentiment analysis, suggesting actions that will not burden the user.

[0375] As a concrete example, if the device detects that the dog is not getting enough exercise and determines that the user is stressed, the server could send a notification such as, "We recommend a short walk on the weekend to help the dog relax."

[0376] Examples of prompts include the following:

[0377] "Please explain how this system works and how to implement pet care suggestions that take the user's emotional state into account."

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The device, when attached to an animal, collects exercise and health information. Specifically, an accelerometer and heart rate sensor built into the device detect the animal's steps and heart rate. This information is temporarily stored in the device's data storage and prepared for the next transmission. The input is the animal's movement and heart rate, and the output is stored in the device as numerical data.

[0381] Step 2:

[0382] The terminal sends the collected data to the server. Communication takes place via Bluetooth or Wi-Fi, and data is sent to the server in real time or in batch processing. When the server receives the data, it is recorded in the database. The input is data packets from the terminal, and the output is the information registered in the server's database.

[0383] Step 3:

[0384] The server analyzes stored information based on a learning algorithm. It uses Python's scikit-learn and TensorFlow to detect anomalies in movement data and biological data. The data is evaluated using statistical methods and machine learning models. The input is stored animal data, and the output is evaluation results and anomaly warnings.

[0385] Step 4:

[0386] The user control device, equipped with emotion analysis capabilities, analyzes the user's facial expressions and voice. This analysis is performed using a camera and microphone built into the control device, and utilizes OpenCV or other related software. The input is the user's facial expressions and voice information, and the output is the determined emotional state.

[0387] Step 5:

[0388] The server generates a care plan by combining the analysis results and the emotional analysis results. The care plan creation takes into account the emotional analysis performed by the user and the analysis results of the animal's health status, resulting in a customized proposal. The inputs are the animal's health assessment and the user's emotional state, and the output is an individualized care proposal.

[0389] Step 6:

[0390] The server notifies the user's device of the generated care plan. Real-time notifications are sent using services such as Firebase Cloud Messaging and Apple Push Notification Service to inform the user of their health status and recommended actions. The input is a customized care plan, and the output is displayed as a push notification on the user's device.

[0391] Step 7:

[0392] The user checks notifications received through the control device and performs animal care based on the information. This allows the user to take appropriate actions according to the animal's condition. The input is the notified care information, and the output is the actual care action.

[0393] (Application Example 2)

[0394] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0395] In modern factories, there is a need to simultaneously optimize machine operating efficiency and the working environment for operators. However, conventional systems were unable to comprehensively understand the state of the machines and the emotions of the operators, and adjust the work process accordingly. As a result, problems such as decreased work efficiency and increased operator stress often occurred.

[0396] 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.

[0397] In this invention, the server includes means for collecting operational status data and ambient environmental information from devices attached to the machine, means for providing a learning model that adaptively analyzes the state of the machine using the stored data, and means for providing notifications via a communication device to analyze the operator's emotions and adjust the work process. This enables integrated optimization of the work process that takes into account both the machine's operational status and the operator's emotions.

[0398] A "device" is an instrument that, when attached to a machine, has the function of collecting operating status data and surrounding environment information.

[0399] "Data" is a general term for information collected from a device regarding the operating status of the machine and the surrounding environment.

[0400] A "learning model" is a computational model that has algorithms for adaptively analyzing the state of a machine using collected data.

[0401] "Analysis tools" refer to systems and processes designed to analyze the emotions of operators.

[0402] A "communication device" is a device used to transmit notifications from a server to an operator, and is a device that has the function of transmitting information bidirectionally.

[0403] A "feedback reflection means" is a system or process that has the function of updating the analysis model based on feedback information obtained from the operator.

[0404] A "notification" refers to a message that conveys appropriate information to the operator based on the results of the analysis.

[0405] The system implementing this invention is designed to optimize work processes by monitoring the emotional states of factory machinery and operators in real time. A server collects operational status data and ambient environmental information from devices attached to the machinery. The collected data is transmitted using communication technologies such as Bluetooth or Wi-Fi. The server stores this data and applies a learning model to analyze the machine's state.

[0406] Specifically, the server uses a learning model to analyze the machine's operating status and determine if there are any abnormalities. The learning model used is optimized based on past data. The server also evaluates emotional data acquired through the operator's smartphone or smart glasses using emotion analysis software. This allows the server to understand the operator's stress levels and fatigue, and adjust the work process accordingly.

[0407] The user (operator) receives notifications from the server via a communication device. These notifications include the machine's current operating status and recommended work adjustments, allowing the operator to take prompt action. Furthermore, the operator's feedback is used to improve the accuracy of the server's analysis model.

[0408] As a concrete example, consider a scenario where a factory operator encounters a machine malfunction. This system can simultaneously grasp machine operation data and the operator's emotions, and automatically provide appropriate countermeasures based on a prompt message such as, "Analyze the operator's current emotional state from their facial expressions and voice, and create an operational adjustment plan for the factory robot based on the results." This is expected to improve work efficiency and reduce operator stress.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The server receives operating status data and ambient environment information from terminals attached to the machine. Multiple sensors attached to the terminals collect necessary data such as operating energy, temperature, and vibration from the machine in operation and transmit this data to the server via Bluetooth or Wi-Fi. The input for this step is raw data from the sensors, and the output is processed data stored in a database on the server.

[0412] Step 2:

[0413] The server uses the received data to perform analysis using a machine learning model. The stored data is preprocessed and prepared as an input dataset for executing an anomaly detection algorithm. This determines whether the machine's operating status is normal or abnormal, and also evaluates its efficiency. The input for this step is preprocessed data, and the output is the result of the machine's status evaluation.

[0414] Step 3:

[0415] The server acquires emotional data from the operator's smart device and analyzes it using an emotion engine. The user's device uses a camera and microphone to record facial expressions and voice, and this data is processed by the emotion engine. Based on this data, the emotion engine quantifies stress levels and fatigue levels. The input for this step is facial expression and voice data, and the output is an index representing the emotional state.

[0416] Step 4:

[0417] The server generates a notification to adjust the work process appropriately based on the analysis results. It considers the operator's emotional state and combines it with the machine learning analysis results to determine the optimal work adjustment plan. At this stage, a generative AI model is used to create recommendations that follow the prompt. Finally, the server sends the notification to the operator via a communication device. The inputs to this step are state evaluations and emotional indicators, and the output is a notification indicating specific work adjustment methods.

[0418] Step 5:

[0419] The user (operator) receives notifications from the server and takes appropriate action. Based on the proposed changes to the work process, they review the current situation and adjust machine operation and their own work procedures to improve work efficiency and safety. The input for this step is the notification from the communication device, and the output is the operator's response.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] This invention is a system consisting of a terminal attached to an animal, a server, and a user communication device, for the efficient management of animal health. Specific embodiments of this system are described below.

[0437] Device operation:

[0438] The device is attached to the animal and plays a role in collecting activity data and biometric information. For example, the device has a built-in accelerometer and heart rate sensor to measure the animal's activity level and heart rate. This data is transmitted to a server in real time. The device connects to the server via Bluetooth or Wi-Fi, allowing it to quickly transmit necessary information.

[0439] Server operation:

[0440] The server stores data received from the terminal and analyzes it using a machine learning model. For example, it uses machine learning to detect extreme decreases in activity levels compared to normal levels or abnormal heart rates. The analysis results are automatically evaluated, and abnormality warnings are generated as needed. Furthermore, based on the analysis results, the user is notified via a communication device of the animal's current health status and recommended actions.

[0441] How to use it:

[0442] Users receive notifications sent from the server via communication devices such as smartphones. Through the application, they can visually review the collected data and analysis results and adjust the animal's care plan as needed. For example, if a user receives a notification that their animal is not getting enough exercise, they can take specific actions such as increasing walks.

[0443] As a concrete example, suppose a device attached to a small dog collects the distance traveled and average heart rate during a walk that day. This data is sent to a server, and by comparing it with past data, it is analyzed that the dog's exercise level that day was significantly lower than usual. The server notifies the user of this information, and the user receives guidance through the app to increase the dog's walking time that day.

[0444] This system allows users to constantly monitor the health status of their animals and provide appropriate care. The entire system is automated, significantly reducing the burden of daily management.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The device is attached to the animal and collects activity data and biometric information. Specifically, it measures the amount of physical activity using an accelerometer and the heart rate using a heart rate sensor. This data is measured at regular intervals.

[0448] Step 2:

[0449] The device collects data and sends it to the server via Bluetooth or Wi-Fi. Transmission occurs in real time or periodically, and all data is time-stamped.

[0450] Step 3:

[0451] The server records the data it receives in the database. Data storage is optimized for efficient searching and access.

[0452] Step 4:

[0453] The server analyzes the recorded data using a machine learning model. In this step, it detects outliers (e.g., extreme lack of exercise or abnormal heart rate) by comparing them with past data.

[0454] Step 5:

[0455] Based on the analysis results, the server generates necessary actions and recommended care plans. For example, if a lack of exercise is detected, it will instruct the user to increase their exercise level.

[0456] Step 6:

[0457] The server generates a notification and sends it to the user via a communication device. The notification content arrives as a push message on the user's smartphone.

[0458] Step 7:

[0459] Users can check notifications through a smartphone app and understand care plans tailored to their animal's health condition. Based on the notifications, users can take appropriate actions for their pets.

[0460] Step 8:

[0461] Users send feedback about their actions to the server through the app. This feedback is recorded in a database and used to refine the model for future analysis.

[0462] (Example 1)

[0463] 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."

[0464] In recent years, efficiently collecting activity information and biometric data to quickly detect abnormalities and changes in health status has become crucial in animal health management. Traditional methods often involve manual data collection and analysis, leading to frequent errors and delays. As a result, immediate responses to animal health issues are difficult.

[0465] 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.

[0466] In this invention, the server includes means for a device attached to an animal to acquire activity information and biometric data, means for receiving and recording information transmitted from the device, and means for providing a machine learning method that automatically analyzes patterns and anomalies using the recorded information. This makes it possible to monitor the animal's health status in real time, detect anomalies immediately, and take countermeasures.

[0467] A "device attached to an animal" is a device that is attached to an animal's body in order to acquire information about the animal's activity and biometric data.

[0468] "Activity information and biometric data" refers to data related to the animal's health and behavior, such as its activity level and heart rate.

[0469] "Information transferred from the device" refers to activity information and biometric data acquired by the device that are sent to the server.

[0470] "Means for receiving and recording" refers to a system in which a server receives information transmitted from a device and stores it in a database or similar.

[0471] "Machine learning methods that automatically analyze patterns and anomalies" refer to algorithms or models that learn normal activity patterns based on collected data and identify events that should be detected as anomalies.

[0472] "Means of providing notifications via information terminals" refers to communication methods used to inform users of the animal's health status or any abnormalities based on the analysis results.

[0473] This invention is a system for efficiently managing the health of animals, consisting of a terminal attached to the animal, a server for analyzing the data, and a communication device for the user receiving the information. Specific embodiments for carrying out the invention are described below.

[0474] Terminal processing

[0475] The device is attached to the animal and is equipped with devices such as an accelerometer and a heart rate sensor. This allows it to collect information on the animal's activity and biometric data, specifically its exercise level and heart rate. The device transmits the collected data to a server via Bluetooth or Wi-Fi, providing information in real time. Furthermore, encryption technology is used during data transmission to ensure security.

[0476] Server Processing

[0477] The server receives information transmitted from the terminal and stores it in a database. The received data is pre-processed using signal processing techniques to remove noise and prepare it for analysis. The server then analyzes the data using machine learning methods to detect anomalies in activity patterns and changes in health status. Generative AI models are used for analysis, enabling highly accurate anomaly detection. Based on the analysis results, the server notifies the user of the detected anomalies and recommended countermeasures.

[0478] User roles

[0479] Users receive notifications from the server using communication devices such as smartphones and tablets. The application allows users to visually display information about their animals' health status and activity. This enables users to immediately plan and implement appropriate care, helping them manage their animals' health.

[0480] As a concrete example, consider a system where a device attached to a small dog detects if its exercise level decreases for the day, and the server notifies the user. In this case, the user can receive a notification such as, "Your dog hasn't exercised enough today, so we recommend giving it some extra exercise."

[0481] An example of a prompt to be input to the generative AI model is the text: "If a small dog is not meeting its daily exercise goal, suggest how to increase its exercise." Using this prompt, the server can have the generative AI model generate an appropriate action plan.

[0482] This system allows for efficient and effective management of animal health, reducing the burden on users. This automated approach is expected to make daily health management easier and more precise.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The device is attached to the animal and collects activity information and biometric data using an accelerometer and a heart rate sensor. The input is the animal's activity level and heart rate, which are obtained as numerical data. Specifically, the device measures acceleration at regular intervals and calculates the heart rate over a certain period using the heart rate sensor. This data is temporarily stored in a buffer.

[0486] Step 2:

[0487] The device transmits the collected data to the server in real time. The input is the activity information and biometric data obtained in step 1. The output is a response confirming that the data has reached the server. The device encrypts the data via Bluetooth or Wi-Fi, packets it, and sends it to the server's receiving system.

[0488] Step 3:

[0489] The server saves the received data to the database. The input is the activity information and biometric data sent in step 2. The output is a status indicating that recording to the database is complete. The server organizes the received data as time-series data, checks for missing or outlier data, and then saves it.

[0490] Step 4:

[0491] The server performs signal processing on the stored data and analyzes it using a machine learning model. The input is the data recorded in step 3, and the output is the result of anomaly detection. Specifically, the server filters the data and inputs it into a generative AI model to detect deviations from normal patterns.

[0492] Step 5:

[0493] Based on the analysis results, the server notifies the user of any anomalies if necessary. The input is the anomaly detection result obtained in step 4. The output is the notification message sent to the user's smartphone or tablet. The server generates a prompt message to inform the user of the animal's health status and recommended actions.

[0494] Step 6:

[0495] The user uses a communication device to check notifications from the server and adjust the animal care plan as needed. Input is the notification message received from the server, and output is the change in the user's action plan. The user accesses the application and makes specific decisions based on the visualized data.

[0496] This series of processes allows for real-time monitoring of the animals' health status and immediate action as needed.

[0497] (Application Example 1)

[0498] 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."

[0499] In security operations using animals, it is necessary to constantly monitor the animals' health and stress levels and respond quickly. However, with conventional methods, it is difficult to grasp changes in the animals' health in real time, and there is a possibility that security operations may continue without noticing any abnormalities. In such situations, not only is the safety of the animals compromised, but the effectiveness of security is reduced, and as a result, there is a risk of human and material losses. Therefore, there is a need to develop a system that efficiently and accurately manages the health of security animals and immediately notifies supervisors of the information.

[0500] 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.

[0501] In this invention, the server includes a device for collecting activity data and biometric information, a device for receiving and storing data transmitted from a terminal, and a device for providing a machine learning model that adaptively analyzes trends and anomalies using the stored data. This makes it possible to monitor the health status and stress levels of guard animals in real time and to immediately notify the monitor when an anomaly is detected.

[0502] A "device" is a collection of hardware and software configured to perform a specific function.

[0503] A "terminal" is a device attached to an animal to collect activity data and biometric information.

[0504] A "communication terminal" is a device used to exchange information with users and other systems.

[0505] A "machine learning model" is a collection of algorithms that learn patterns from data and perform predictions and classifications.

[0506] A "visual display terminal" is a device used to present information to users visually.

[0507] A "device for storing information" refers to memory or storage systems used to temporarily or long-term store data.

[0508] A "notification device" is a device equipped with a means of conveying information to a user.

[0509] A "feedback reflection device" is a device that makes adjustments to improve the system's operation based on user input.

[0510] The system for implementing this invention consists of a terminal attached to an animal, a server for receiving and analyzing data, and a communication terminal for the user that presents the information. The terminal is equipped with sensors that collect animal activity data and biometric information, including, for example, a heart rate sensor and an accelerometer. The data obtained from these sensors is transmitted from the terminal to the server via Bluetooth or Wi-Fi. This data collection and transmission is performed in real time.

[0511] The server includes a processing unit that stores received data in a storage device and analyzes the data using a machine learning model. The machine learning model learns the normal activity patterns and health status of animals and detects anomalies by comparing them with the collected data. For example, it identifies changes in heart rate and decreased activity levels to find signs of stress or fatigue. If an anomaly is detected, the server generates an alert and immediately notifies the user of this information.

[0512] Users receive notifications from the server using a communication terminal. This terminal can take the form of smart glasses used by security guards or a smartphone, and provides visual information. Furthermore, machine learning models can be adjusted based on user feedback, and this information can be reflected in subsequent data analyses.

[0513] As a concrete example, when this system is attached to security dogs guarding important facilities at night, the server will send a real-time alert to the security guard's smart glasses if the dog starts to show signs of fatigue or if its activity level falls below normal. This allows the security guard to quickly devise a response and allow the dog to rest.

[0514] Examples of prompts for a generative AI model are as follows:

[0515] "What procedures and technologies are necessary to develop a system that analyzes a dog's health data, identifies abnormalities, and notifies security guards in real time? Please provide an example using Python and sensors. Include specific software libraries and APIs in your answer."

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The device is attached to the animal and collects activity data and biometric information. Specifically, heart rate sensors and accelerometers measure the animal's heart rate and activity level, generating this data. The input is biometric information acquired by the sensors, and the output is digital data containing this information.

[0519] Step 2:

[0520] The device transmits the collected data to the server in real time. Specifically, it transmits digital data via Bluetooth or Wi-Fi. The input is the collected digital data, and the output is the data stream sent to the server via wireless communication.

[0521] Step 3:

[0522] The server receives data sent from the terminal and stores it in storage. Specifically, this involves receiving data and writing to the database. The input is a data stream sent from the terminal, and the output is data records stored in storage.

[0523] Step 4:

[0524] The server performs analysis using machine learning models with the accumulated data. Specifically, it preprocesses the data, generates input vectors for adaptive analysis, and detects trends and anomalies through machine learning algorithms. The input is data records stored in memory, and the output is anomaly detection information as an analysis result.

[0525] Step 5:

[0526] The server notifies the user's communication terminal of anomaly detection information based on the analysis. Specifically, it generates a warning message and sends it to the communication terminal via the network. The input is the anomaly detection information as a result of the analysis, and the output is the warning message displayed on the user's communication terminal.

[0527] Step 6:

[0528] The user receives notifications from the server via a communication device. Specifically, a warning message pops up on the user's smart glasses or smartphone, allowing them to take necessary actions based on it. The input is the warning message sent from the server, and the output is the user's prompt response and its result.

[0529] 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.

[0530] This invention is a system in which a terminal, server, emotion engine, and user communication device work together to provide advanced animal health management. By incorporating an emotion engine, it is possible to propose health care that takes the user's emotions into consideration.

[0531] Device operation:

[0532] The device is attached to the animal and is responsible for continuously collecting activity data and biometric information. This data, including exercise levels and heart rate, is acquired by multiple sensors and transmitted to a server via Bluetooth or Wi-Fi.

[0533] Server operation:

[0534] The server receives and stores data sent from the terminal. This data is analyzed by a machine learning model, and the results are used to assess the animal's health status. The assessment results are notified to the user via a communication device, and appropriate care is suggested.

[0535] How the emotion engine works:

[0536] The emotion engine uses the user's device (e.g., smartphone) camera and microphone to analyze the user's emotions from their facial expressions and voice. Based on this analysis, the server customizes care plans and notifications, providing information that takes into account the user's situation and emotions.

[0537] How to use it:

[0538] Users utilize a smartphone app to receive notifications from the server. These notifications include animal health information, which users can use to adjust their pet's care. Furthermore, the system is expected to provide more appropriate responses by offering suggestions that take the user's emotions into consideration. For example, users experiencing stress will receive suggestions for pet care that are manageable and not overwhelming.

[0539] As a concrete example, suppose a device collects data on a dog's exercise level, and the server analyzes this data to determine that the dog is less active than normal. If the emotion engine determines from the user's facial expression that the dog is tired, the server will suggest exercise within reasonable limits and send a notification to the user such as, "Let's try a short walk this weekend."

[0540] This system enables flexible care that considers not only the animal's health but also the user's mental condition. It is an implementation that supports health management while reducing the burden on both the user and the animal.

[0541] The following describes the processing flow.

[0542] Step 1:

[0543] The device is attached to the animal and collects activity data and biometric information. Specifically, an accelerometer built into the device measures the amount of exercise, and a heart rate sensor records the heart rate. The data is recorded at regular intervals and converted into a format that can be transmitted to a server.

[0544] Step 2:

[0545] The device collects data and transmits it to the server in real time. It connects to the server using communication methods such as Bluetooth or Wi-Fi, and the data is transmitted encrypted. This ensures that the server always stores the latest health information.

[0546] Step 3:

[0547] The server receives the transmitted data and records it in the database. The received data is timestamped and stored efficiently. The data is then used for analysis by machine learning models.

[0548] Step 4:

[0549] The server uses a machine learning model to analyze incoming data. It compares this data with past data to assess the animal's health and detect abnormalities. For example, it generates warnings if exercise levels do not meet the standard or if the heart rate falls outside the specified range.

[0550] Step 5:

[0551] The server checks the user's emotional state via an emotion engine on the user's device. It estimates emotions from the user's facial expressions and tone of voice using the device's camera and microphone. Based on the results of the emotion analysis, it adjusts the notification content.

[0552] Step 6:

[0553] The server generates advice on animal care based on analysis results and emotional state, and notifies the user via a communication device. For example, if the user is tired, it will suggest a care plan that reduces the burden on the animal.

[0554] Step 7:

[0555] The user receives a notification via a communication device and checks its contents on a smartphone app. Based on the health information and care plan, the user takes specific care actions for their animal. This includes actions such as adjusting the frequency of walks according to the suggestions.

[0556] Step 8:

[0557] Users send feedback about care to the server through the app. This feedback includes individual impressions and information about the effectiveness of the care. The server then uses this feedback to improve its machine learning model and incorporate it into subsequent analyses.

[0558] (Example 2)

[0559] 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."

[0560] In managing the health status of animals, conventional technologies have been insufficient in collecting activity and health information, making it difficult to detect abnormalities in a timely manner and take effective action. Furthermore, the inability to propose care that takes into account the user's mental state resulted in a significant mental burden on the user themselves.

[0561] 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.

[0562] In this invention, the server includes means for a device attached to the animal to collect exercise and health information, means for customizing notification content using emotion analysis results, and means for presenting countermeasures via an information transmission device. This enables accurate monitoring of the animal's health status and flexible care suggestions that take into account the user's mental state.

[0563] A "device" is a piece of equipment attached to an animal to collect information on its movement and health.

[0564] "Movement information" refers to data related to an animal's movement, such as the number of steps taken and the distance traveled.

[0565] "Health information" refers to data that indicates the biological state of an animal, such as its heart rate and body temperature.

[0566] "To save" means to retain received information on a recording medium such as a database.

[0567] A "learning algorithm" is a computer program that uses collected information to analyze anomalies and assess the health status of animals.

[0568] An "information transmission device" is a means of communication used to notify users of analysis results and countermeasures.

[0569] The "emotion analysis function" is a means of determining a user's emotions from their facial expressions and voice, and is used to individually adjust notification content.

[0570] One embodiment of the present invention provides a care system that simultaneously considers the animal's health condition and the user's mental state. This system is realized by combining a device, a server, an emotion analysis function, and a user control device.

[0571] The device can be attached to animals to collect exercise and health information. Specifically, it has built-in accelerometers and heart rate sensors to collect data such as the animal's steps and heart rate. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0572] The server receives information sent from the terminal and stores it in a database. The stored information is analyzed using learning algorithms such as Python's scikit-learn and TensorFlow to evaluate the animal's health status. If an abnormality is detected as a result of the evaluation, appropriate actions are suggested.

[0573] The emotion analysis function analyzes the user's facial expressions and voice through the user's device (e.g., smartphone). This is achieved using OpenCV or similar technologies to evaluate the user's mental state. The evaluation results are used to individually adjust the care plan proposed by the server.

[0574] Users receive notifications from the server via a control device and implement care plans based on the animal's health status. The notifications reflect the results of sentiment analysis, suggesting actions that will not burden the user.

[0575] As a concrete example, if the device detects that the dog is not getting enough exercise and determines that the user is stressed, the server could send a notification such as, "We recommend a short walk on the weekend to help the dog relax."

[0576] Examples of prompts include the following:

[0577] "Please explain how this system works and how to implement pet care suggestions that take the user's emotional state into account."

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The device, when attached to an animal, collects exercise and health information. Specifically, an accelerometer and heart rate sensor built into the device detect the animal's steps and heart rate. This information is temporarily stored in the device's data storage and prepared for the next transmission. The input is the animal's movement and heart rate, and the output is stored in the device as numerical data.

[0581] Step 2:

[0582] The terminal sends the collected data to the server. Communication takes place via Bluetooth or Wi-Fi, and data is sent to the server in real time or in batch processing. When the server receives the data, it is recorded in the database. The input is data packets from the terminal, and the output is the information registered in the server's database.

[0583] Step 3:

[0584] The server analyzes stored information based on a learning algorithm. It uses Python's scikit-learn and TensorFlow to detect anomalies in movement data and biological data. The data is evaluated using statistical methods and machine learning models. The input is stored animal data, and the output is evaluation results and anomaly warnings.

[0585] Step 4:

[0586] The user control device, equipped with emotion analysis capabilities, analyzes the user's facial expressions and voice. This analysis is performed using a camera and microphone built into the control device, and utilizes OpenCV or other related software. The input is the user's facial expressions and voice information, and the output is the determined emotional state.

[0587] Step 5:

[0588] The server generates a care plan by combining the analysis results and the emotional analysis results. The care plan creation takes into account the emotional analysis performed by the user and the analysis results of the animal's health status, resulting in a customized proposal. The inputs are the animal's health assessment and the user's emotional state, and the output is an individualized care proposal.

[0589] Step 6:

[0590] The server notifies the user's device of the generated care plan. Real-time notifications are sent using services such as Firebase Cloud Messaging and Apple Push Notification Service to inform the user of their health status and recommended actions. The input is a customized care plan, and the output is displayed as a push notification on the user's device.

[0591] Step 7:

[0592] The user checks notifications received through the control device and performs animal care based on the information. This allows the user to take appropriate actions according to the animal's condition. The input is the notified care information, and the output is the actual care action.

[0593] (Application Example 2)

[0594] 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."

[0595] In modern factories, there is a need to simultaneously optimize machine operating efficiency and the working environment for operators. However, conventional systems were unable to comprehensively understand the state of the machines and the emotions of the operators, and adjust the work process accordingly. As a result, problems such as decreased work efficiency and increased operator stress often occurred.

[0596] 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.

[0597] In this invention, the server includes means for collecting operational status data and ambient environmental information from devices attached to the machine, means for providing a learning model that adaptively analyzes the state of the machine using the stored data, and means for providing notifications via a communication device to analyze the operator's emotions and adjust the work process. This enables integrated optimization of the work process that takes into account both the machine's operational status and the operator's emotions.

[0598] A "device" is an instrument that, when attached to a machine, has the function of collecting operating status data and surrounding environment information.

[0599] "Data" is a general term for information collected from a device regarding the operating status of the machine and the surrounding environment.

[0600] A "learning model" is a computational model that has algorithms for adaptively analyzing the state of a machine using collected data.

[0601] "Analysis tools" refer to systems and processes designed to analyze the emotions of operators.

[0602] A "communication device" is a device used to transmit notifications from a server to an operator, and is a device that has the function of transmitting information bidirectionally.

[0603] A "feedback reflection means" is a system or process that has the function of updating the analysis model based on feedback information obtained from the operator.

[0604] A "notification" refers to a message that conveys appropriate information to the operator based on the results of the analysis.

[0605] The system implementing this invention is designed to optimize work processes by monitoring the emotional states of factory machinery and operators in real time. A server collects operational status data and ambient environmental information from devices attached to the machinery. The collected data is transmitted using communication technologies such as Bluetooth or Wi-Fi. The server stores this data and applies a learning model to analyze the machine's state.

[0606] Specifically, the server uses a learning model to analyze the machine's operating status and determine if there are any abnormalities. The learning model used is optimized based on past data. The server also evaluates emotional data acquired through the operator's smartphone or smart glasses using emotion analysis software. This allows the server to understand the operator's stress levels and fatigue, and adjust the work process accordingly.

[0607] The user (operator) receives notifications from the server via a communication device. These notifications include the machine's current operating status and recommended work adjustments, allowing the operator to take prompt action. Furthermore, the operator's feedback is used to improve the accuracy of the server's analysis model.

[0608] As a concrete example, consider a scenario where a factory operator encounters a machine malfunction. This system can simultaneously grasp machine operation data and the operator's emotions, and automatically provide appropriate countermeasures based on a prompt message such as, "Analyze the operator's current emotional state from their facial expressions and voice, and create an operational adjustment plan for the factory robot based on the results." This is expected to improve work efficiency and reduce operator stress.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The server receives operating status data and ambient environment information from terminals attached to the machine. Multiple sensors attached to the terminals collect necessary data such as operating energy, temperature, and vibration from the machine in operation and transmit this data to the server via Bluetooth or Wi-Fi. The input for this step is raw data from the sensors, and the output is processed data stored in a database on the server.

[0612] Step 2:

[0613] The server uses the received data to perform analysis using a machine learning model. The stored data is preprocessed and prepared as an input dataset for executing an anomaly detection algorithm. This determines whether the machine's operating status is normal or abnormal, and also evaluates its efficiency. The input for this step is preprocessed data, and the output is the result of the machine's status evaluation.

[0614] Step 3:

[0615] The server acquires emotional data from the operator's smart device and analyzes it using an emotion engine. The user's device uses a camera and microphone to record facial expressions and voice, and this data is processed by the emotion engine. Based on this data, the emotion engine quantifies stress levels and fatigue levels. The input for this step is facial expression and voice data, and the output is an index representing the emotional state.

[0616] Step 4:

[0617] The server generates a notification to adjust the work process appropriately based on the analysis results. It considers the operator's emotional state and combines it with the machine learning analysis results to determine the optimal work adjustment plan. At this stage, a generative AI model is used to create recommendations that follow the prompt. Finally, the server sends the notification to the operator via a communication device. The inputs to this step are state evaluations and emotional indicators, and the output is a notification indicating specific work adjustment methods.

[0618] Step 5:

[0619] The user (operator) receives notifications from the server and takes appropriate action. Based on the proposed changes to the work process, they review the current situation and adjust machine operation and their own work procedures to improve work efficiency and safety. The input for this step is the notification from the communication device, and the output is the operator's response.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] This invention is a system consisting of a terminal attached to an animal, a server, and a user communication device, for the efficient management of animal health. Specific embodiments of this system are described below.

[0638] Device operation:

[0639] The device is attached to the animal and plays a role in collecting activity data and biometric information. For example, the device has a built-in accelerometer and heart rate sensor to measure the animal's activity level and heart rate. This data is transmitted to a server in real time. The device connects to the server via Bluetooth or Wi-Fi, allowing it to quickly transmit necessary information.

[0640] Server operation:

[0641] The server stores data received from the terminal and analyzes it using a machine learning model. For example, it uses machine learning to detect extreme decreases in activity levels compared to normal levels or abnormal heart rates. The analysis results are automatically evaluated, and abnormality warnings are generated as needed. Furthermore, based on the analysis results, the user is notified via a communication device of the animal's current health status and recommended actions.

[0642] How to use it:

[0643] Users receive notifications sent from the server via communication devices such as smartphones. Through the application, they can visually review the collected data and analysis results and adjust the animal's care plan as needed. For example, if a user receives a notification that their animal is not getting enough exercise, they can take specific actions such as increasing walks.

[0644] As a concrete example, suppose a device attached to a small dog collects the distance traveled and average heart rate during a walk that day. This data is sent to a server, and by comparing it with past data, it is analyzed that the dog's exercise level that day was significantly lower than usual. The server notifies the user of this information, and the user receives guidance through the app to increase the dog's walking time that day.

[0645] This system allows users to constantly monitor the health status of their animals and provide appropriate care. The entire system is automated, significantly reducing the burden of daily management.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The device is attached to the animal and collects activity data and biometric information. Specifically, it measures the amount of physical activity using an accelerometer and the heart rate using a heart rate sensor. This data is measured at regular intervals.

[0649] Step 2:

[0650] The device collects data and sends it to the server via Bluetooth or Wi-Fi. Transmission occurs in real time or periodically, and all data is time-stamped.

[0651] Step 3:

[0652] The server records the data it receives in the database. Data storage is optimized for efficient searching and access.

[0653] Step 4:

[0654] The server analyzes the recorded data using a machine learning model. In this step, it detects outliers (e.g., extreme lack of exercise or abnormal heart rate) by comparing them with past data.

[0655] Step 5:

[0656] Based on the analysis results, the server generates necessary actions and recommended care plans. For example, if a lack of exercise is detected, it will instruct the user to increase their exercise level.

[0657] Step 6:

[0658] The server generates a notification and sends it to the user via a communication device. The notification content arrives as a push message on the user's smartphone.

[0659] Step 7:

[0660] Users can check notifications through a smartphone app and understand care plans tailored to their animal's health condition. Based on the notifications, users can take appropriate actions for their pets.

[0661] Step 8:

[0662] Users send feedback about their actions to the server through the app. This feedback is recorded in a database and used to refine the model for future analysis.

[0663] (Example 1)

[0664] 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".

[0665] In recent years, efficiently collecting activity information and biometric data to quickly detect abnormalities and changes in health status has become crucial in animal health management. Traditional methods often involve manual data collection and analysis, leading to frequent errors and delays. As a result, immediate responses to animal health issues are difficult.

[0666] 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.

[0667] In this invention, the server includes means for a device attached to an animal to acquire activity information and biometric data, means for receiving and recording information transmitted from the device, and means for providing a machine learning method that automatically analyzes patterns and anomalies using the recorded information. This makes it possible to monitor the animal's health status in real time, detect anomalies immediately, and take countermeasures.

[0668] A "device attached to an animal" is a device that is attached to an animal's body in order to acquire information about the animal's activity and biometric data.

[0669] "Activity information and biometric data" refers to data related to the animal's health and behavior, such as its activity level and heart rate.

[0670] "Information transferred from the device" refers to activity information and biometric data acquired by the device that are sent to the server.

[0671] "Means for receiving and recording" refers to a system in which a server receives information transmitted from a device and stores it in a database or similar.

[0672] "Machine learning methods that automatically analyze patterns and anomalies" refer to algorithms or models that learn normal activity patterns based on collected data and identify events that should be detected as anomalies.

[0673] "Means of providing notifications via information terminals" refers to communication methods used to inform users of the animal's health status or any abnormalities based on the analysis results.

[0674] This invention is a system for efficiently managing the health of animals, consisting of a terminal attached to the animal, a server for analyzing the data, and a communication device for the user receiving the information. Specific embodiments for carrying out the invention are described below.

[0675] Terminal processing

[0676] The device is attached to the animal and is equipped with devices such as an accelerometer and a heart rate sensor. This allows it to collect information on the animal's activity and biometric data, specifically its exercise level and heart rate. The device transmits the collected data to a server via Bluetooth or Wi-Fi, providing information in real time. Furthermore, encryption technology is used during data transmission to ensure security.

[0677] Server Processing

[0678] The server receives information transmitted from the terminal and stores it in a database. The received data is pre-processed using signal processing techniques to remove noise and prepare it for analysis. The server then analyzes the data using machine learning methods to detect anomalies in activity patterns and changes in health status. Generative AI models are used for analysis, enabling highly accurate anomaly detection. Based on the analysis results, the server notifies the user of the detected anomalies and recommended countermeasures.

[0679] User roles

[0680] Users receive notifications from the server using communication devices such as smartphones and tablets. The application allows users to visually display information about their animals' health status and activity. This enables users to immediately plan and implement appropriate care, helping them manage their animals' health.

[0681] As a concrete example, consider a system where a device attached to a small dog detects if its exercise level decreases for the day, and the server notifies the user. In this case, the user can receive a notification such as, "Your dog hasn't exercised enough today, so we recommend giving it some extra exercise."

[0682] An example of a prompt to be input to the generative AI model is the text: "If a small dog is not meeting its daily exercise goal, suggest how to increase its exercise." Using this prompt, the server can have the generative AI model generate an appropriate action plan.

[0683] This system allows for efficient and effective management of animal health, reducing the burden on users. This automated approach is expected to make daily health management easier and more precise.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] The device is attached to the animal and collects activity information and biometric data using an accelerometer and a heart rate sensor. The input is the animal's activity level and heart rate, which are obtained as numerical data. Specifically, the device measures acceleration at regular intervals and calculates the heart rate over a certain period using the heart rate sensor. This data is temporarily stored in a buffer.

[0687] Step 2:

[0688] The device transmits the collected data to the server in real time. The input is the activity information and biometric data obtained in step 1. The output is a response confirming that the data has reached the server. The device encrypts the data via Bluetooth or Wi-Fi, packets it, and sends it to the server's receiving system.

[0689] Step 3:

[0690] The server saves the received data to the database. The input is the activity information and biometric data sent in step 2. The output is a status indicating that recording to the database is complete. The server organizes the received data as time-series data, checks for missing or outlier data, and then saves it.

[0691] Step 4:

[0692] The server performs signal processing on the stored data and analyzes it using a machine learning model. The input is the data recorded in step 3, and the output is the result of anomaly detection. Specifically, the server filters the data and inputs it into a generative AI model to detect deviations from normal patterns.

[0693] Step 5:

[0694] Based on the analysis results, the server notifies the user of any anomalies if necessary. The input is the anomaly detection result obtained in step 4. The output is the notification message sent to the user's smartphone or tablet. The server generates a prompt message to inform the user of the animal's health status and recommended actions.

[0695] Step 6:

[0696] The user uses a communication device to check notifications from the server and adjust the animal care plan as needed. Input is the notification message received from the server, and output is the change in the user's action plan. The user accesses the application and makes specific decisions based on the visualized data.

[0697] This series of processes allows for real-time monitoring of the animals' health status and immediate action as needed.

[0698] (Application Example 1)

[0699] 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".

[0700] In security operations using animals, it is necessary to constantly monitor the animals' health and stress levels and respond quickly. However, with conventional methods, it is difficult to grasp changes in the animals' health in real time, and there is a possibility that security operations may continue without noticing any abnormalities. In such situations, not only is the safety of the animals compromised, but the effectiveness of security is reduced, and as a result, there is a risk of human and material losses. Therefore, there is a need to develop a system that efficiently and accurately manages the health of security animals and immediately notifies supervisors of the information.

[0701] 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.

[0702] In this invention, the server includes a device for collecting activity data and biometric information, a device for receiving and storing data transmitted from a terminal, and a device for providing a machine learning model that adaptively analyzes trends and anomalies using the stored data. This makes it possible to monitor the health status and stress levels of guard animals in real time and to immediately notify the monitor when an anomaly is detected.

[0703] A "device" is a collection of hardware and software configured to perform a specific function.

[0704] A "terminal" is a device attached to an animal to collect activity data and biometric information.

[0705] A "communication terminal" is a device used to exchange information with users and other systems.

[0706] A "machine learning model" is a collection of algorithms that learn patterns from data and perform predictions and classifications.

[0707] A "visual display terminal" is a device used to present information to users visually.

[0708] A "device for storing information" refers to memory or storage systems used to temporarily or long-term store data.

[0709] A "notification device" is a device equipped with a means of conveying information to a user.

[0710] A "feedback reflection device" is a device that makes adjustments to improve the system's operation based on user input.

[0711] The system for implementing this invention consists of a terminal attached to an animal, a server for receiving and analyzing data, and a communication terminal for the user that presents the information. The terminal is equipped with sensors that collect animal activity data and biometric information, including, for example, a heart rate sensor and an accelerometer. The data obtained from these sensors is transmitted from the terminal to the server via Bluetooth or Wi-Fi. This data collection and transmission is performed in real time.

[0712] The server includes a processing unit that stores received data in a storage device and analyzes the data using a machine learning model. The machine learning model learns the normal activity patterns and health status of animals and detects anomalies by comparing them with the collected data. For example, it identifies changes in heart rate and decreased activity levels to find signs of stress or fatigue. If an anomaly is detected, the server generates an alert and immediately notifies the user of this information.

[0713] Users receive notifications from the server using a communication terminal. This terminal can take the form of smart glasses used by security guards or a smartphone, and provides visual information. Furthermore, machine learning models can be adjusted based on user feedback, and this information can be reflected in subsequent data analyses.

[0714] As a concrete example, when this system is attached to security dogs guarding important facilities at night, the server will send a real-time alert to the security guard's smart glasses if the dog starts to show signs of fatigue or if its activity level falls below normal. This allows the security guard to quickly devise a response and allow the dog to rest.

[0715] Examples of prompts for a generative AI model are as follows:

[0716] "What procedures and technologies are necessary to develop a system that analyzes a dog's health data, identifies abnormalities, and notifies security guards in real time? Please provide an example using Python and sensors. Include specific software libraries and APIs in your answer."

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The device is attached to the animal and collects activity data and biometric information. Specifically, heart rate sensors and accelerometers measure the animal's heart rate and activity level, generating this data. The input is biometric information acquired by the sensors, and the output is digital data containing this information.

[0720] Step 2:

[0721] The device transmits the collected data to the server in real time. Specifically, it transmits digital data via Bluetooth or Wi-Fi. The input is the collected digital data, and the output is the data stream sent to the server via wireless communication.

[0722] Step 3:

[0723] The server receives data sent from the terminal and stores it in storage. Specifically, this involves receiving data and writing to the database. The input is a data stream sent from the terminal, and the output is data records stored in storage.

[0724] Step 4:

[0725] The server performs analysis using machine learning models with the accumulated data. Specifically, it preprocesses the data, generates input vectors for adaptive analysis, and detects trends and anomalies through machine learning algorithms. The input is data records stored in memory, and the output is anomaly detection information as an analysis result.

[0726] Step 5:

[0727] The server notifies the user's communication terminal of anomaly detection information based on the analysis. Specifically, it generates a warning message and sends it to the communication terminal via the network. The input is the anomaly detection information as a result of the analysis, and the output is the warning message displayed on the user's communication terminal.

[0728] Step 6:

[0729] The user receives notifications from the server via a communication device. Specifically, a warning message pops up on the user's smart glasses or smartphone, allowing them to take necessary actions based on it. The input is the warning message sent from the server, and the output is the user's prompt response and its result.

[0730] 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.

[0731] This invention is a system in which a terminal, server, emotion engine, and user communication device work together to provide advanced animal health management. By incorporating an emotion engine, it is possible to propose health care that takes the user's emotions into consideration.

[0732] Device operation:

[0733] The device is attached to the animal and is responsible for continuously collecting activity data and biometric information. This data, including exercise levels and heart rate, is acquired by multiple sensors and transmitted to a server via Bluetooth or Wi-Fi.

[0734] Server operation:

[0735] The server receives and stores data sent from the terminal. This data is analyzed by a machine learning model, and the results are used to assess the animal's health status. The assessment results are notified to the user via a communication device, and appropriate care is suggested.

[0736] How the emotion engine works:

[0737] The emotion engine uses the user's device (e.g., smartphone) camera and microphone to analyze the user's emotions from their facial expressions and voice. Based on this analysis, the server customizes care plans and notifications, providing information that takes into account the user's situation and emotions.

[0738] How to use it:

[0739] Users utilize a smartphone app to receive notifications from the server. These notifications include animal health information, which users can use to adjust their pet's care. Furthermore, the system is expected to provide more appropriate responses by offering suggestions that take the user's emotions into consideration. For example, users experiencing stress will receive suggestions for pet care that are manageable and not overwhelming.

[0740] As a concrete example, suppose a device collects data on a dog's exercise level, and the server analyzes this data to determine that the dog is less active than normal. If the emotion engine determines from the user's facial expression that the dog is tired, the server will suggest exercise within reasonable limits and send a notification to the user such as, "Let's try a short walk this weekend."

[0741] This system enables flexible care that considers not only the animal's health but also the user's mental condition. It is an implementation that supports health management while reducing the burden on both the user and the animal.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] The device is attached to the animal and collects activity data and biometric information. Specifically, an accelerometer built into the device measures the amount of exercise, and a heart rate sensor records the heart rate. The data is recorded at regular intervals and converted into a format that can be transmitted to a server.

[0745] Step 2:

[0746] The device collects data and transmits it to the server in real time. It connects to the server using communication methods such as Bluetooth or Wi-Fi, and the data is transmitted encrypted. This ensures that the server always stores the latest health information.

[0747] Step 3:

[0748] The server receives the transmitted data and records it in the database. The received data is timestamped and stored efficiently. The data is then used for analysis by machine learning models.

[0749] Step 4:

[0750] The server uses a machine learning model to analyze incoming data. It compares this data with past data to assess the animal's health and detect abnormalities. For example, it generates warnings if exercise levels do not meet the standard or if the heart rate falls outside the specified range.

[0751] Step 5:

[0752] The server checks the user's emotional state via an emotion engine on the user's device. It estimates emotions from the user's facial expressions and tone of voice using the device's camera and microphone. Based on the results of the emotion analysis, it adjusts the notification content.

[0753] Step 6:

[0754] The server generates advice on animal care based on analysis results and emotional state, and notifies the user via a communication device. For example, if the user is tired, it will suggest a care plan that reduces the burden.

[0755] Step 7:

[0756] The user receives a notification via a communication device and checks its contents on a smartphone app. Based on the health information and care plan, the user takes specific care actions for their animal. This includes actions such as adjusting the frequency of walks according to the suggestions.

[0757] Step 8:

[0758] Users send feedback about care to the server through the app. This feedback includes individual impressions and information about the effectiveness of the care. The server then uses this feedback to improve its machine learning model and incorporate it into subsequent analyses.

[0759] (Example 2)

[0760] 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".

[0761] In managing the health status of animals, conventional technologies have been insufficient in collecting activity and health information, making it difficult to detect abnormalities in a timely manner and take effective action. Furthermore, because care suggestions cannot take into account the user's mental state, the users themselves experience significant emotional burden.

[0762] 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.

[0763] In this invention, the server includes means for a device attached to the animal to collect exercise and health information, means for customizing notification content using emotion analysis results, and means for presenting countermeasures via an information transmission device. This enables accurate monitoring of the animal's health status and flexible care suggestions that take into account the user's mental state.

[0764] A "device" is a piece of equipment attached to an animal to collect information on its movement and health.

[0765] "Movement information" refers to data related to an animal's movement, such as the number of steps taken and the distance traveled.

[0766] "Health information" refers to data that indicates the biological state of an animal, such as its heart rate and body temperature.

[0767] "To save" means to retain received information on a recording medium such as a database.

[0768] A "learning algorithm" is a computer program that uses collected information to analyze anomalies and assess the health status of animals.

[0769] An "information transmission device" is a means of communication used to notify users of analysis results and countermeasures.

[0770] The "emotion analysis function" is a means of determining a user's emotions from their facial expressions and voice, and is used to individually adjust notification content.

[0771] One embodiment of the present invention provides a care system that simultaneously considers the animal's health condition and the user's mental state. This system is realized by combining a device, a server, an emotion analysis function, and a user control device.

[0772] The device can be attached to animals to collect exercise and health information. Specifically, it has built-in accelerometers and heart rate sensors to collect data such as the animal's steps and heart rate. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0773] The server receives information sent from the terminal and stores it in a database. The stored information is analyzed using learning algorithms such as Python's scikit-learn and TensorFlow to evaluate the animal's health status. If an abnormality is detected as a result of the evaluation, appropriate actions are suggested.

[0774] The emotion analysis function analyzes the user's facial expressions and voice through the user's device (e.g., smartphone). This is achieved using OpenCV or similar technologies to evaluate the user's mental state. The evaluation results are used to individually adjust the care plan proposed by the server.

[0775] Users receive notifications from the server via a control device and implement care plans based on the animal's health status. The notifications reflect the results of sentiment analysis, suggesting actions that will not burden the user.

[0776] As a concrete example, if the device detects that the dog is not getting enough exercise and determines that the user is stressed, the server could send a notification such as, "We recommend a short walk on the weekend to help the dog relax."

[0777] Examples of prompts include the following:

[0778] "Please explain how this system works and how to implement pet care suggestions that take the user's emotional state into account."

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The device, when attached to an animal, collects exercise and health information. Specifically, an accelerometer and heart rate sensor built into the device detect the animal's steps and heart rate. This information is temporarily stored in the device's data storage and prepared for the next transmission. The input is the animal's movement and heart rate, and the output is stored in the device as numerical data.

[0782] Step 2:

[0783] The terminal sends the collected data to the server. Communication takes place via Bluetooth or Wi-Fi, and data is sent to the server in real time or in batch processing. When the server receives the data, it is recorded in the database. The input is data packets from the terminal, and the output is the information registered in the server's database.

[0784] Step 3:

[0785] The server analyzes stored information based on a learning algorithm. It uses Python's scikit-learn and TensorFlow to detect anomalies in movement data and biological data. The data is evaluated using statistical methods and machine learning models. The input is stored animal data, and the output is evaluation results and anomaly warnings.

[0786] Step 4:

[0787] The user control device, equipped with emotion analysis capabilities, analyzes the user's facial expressions and voice. This analysis is performed using a camera and microphone built into the control device, and utilizes OpenCV or other related software. The input is the user's facial expressions and voice information, and the output is the determined emotional state.

[0788] Step 5:

[0789] The server generates a care plan by combining the analysis results and the emotional analysis results. The care plan creation takes into account the emotional analysis performed by the user and the analysis results of the animal's health status, resulting in a customized proposal. The inputs are the animal's health assessment and the user's emotional state, and the output is an individualized care proposal.

[0790] Step 6:

[0791] The server notifies the user's device of the generated care plan. Real-time notifications are sent using services such as Firebase Cloud Messaging and Apple Push Notification Service to inform the user of their health status and recommended actions. The input is a customized care plan, and the output is displayed as a push notification on the user's device.

[0792] Step 7:

[0793] The user checks notifications received through the control device and performs animal care based on the information. This allows the user to take appropriate actions according to the animal's condition. The input is the notified care information, and the output is the actual care action.

[0794] (Application Example 2)

[0795] 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".

[0796] In modern factories, there is a need to simultaneously optimize machine operating efficiency and the working environment for operators. However, conventional systems were unable to comprehensively understand the state of the machines and the emotions of the operators, and adjust the work process accordingly. As a result, problems such as decreased work efficiency and increased operator stress often occurred.

[0797] 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.

[0798] In this invention, the server includes means for collecting operational status data and ambient environmental information from devices attached to the machine, means for providing a learning model that adaptively analyzes the state of the machine using the stored data, and means for providing notifications via a communication device to analyze the operator's emotions and adjust the work process. This enables integrated optimization of the work process that takes into account both the machine's operational status and the operator's emotions.

[0799] A "device" is an instrument that, when attached to a machine, has the function of collecting operating status data and surrounding environment information.

[0800] "Data" is a general term for information collected from a device regarding the operating status of the machine and the surrounding environment.

[0801] A "learning model" is a computational model that has algorithms for adaptively analyzing the state of a machine using collected data.

[0802] "Analysis tools" refer to systems and processes designed to analyze the emotions of operators.

[0803] A "communication device" is a device used to transmit notifications from a server to an operator, and is a device that has the function of transmitting information bidirectionally.

[0804] A "feedback reflection means" is a system or process that has the function of updating the analysis model based on feedback information obtained from the operator.

[0805] A "notification" refers to a message that conveys appropriate information to the operator based on the results of the analysis.

[0806] The system implementing this invention is designed to optimize work processes by monitoring the emotional states of factory machinery and operators in real time. A server collects operational status data and ambient environmental information from devices attached to the machinery. The collected data is transmitted using communication technologies such as Bluetooth or Wi-Fi. The server stores this data and applies a learning model to analyze the machine's state.

[0807] Specifically, the server uses a learning model to analyze the machine's operating status and determine if there are any abnormalities. The learning model used is optimized based on past data. The server also evaluates emotional data acquired through the operator's smartphone or smart glasses using emotion analysis software. This allows the server to understand the operator's stress levels and fatigue, and adjust the work process accordingly.

[0808] The user (operator) receives notifications from the server via a communication device. These notifications include the machine's current operating status and recommended work adjustments, allowing the operator to take prompt action. Furthermore, the operator's feedback is used to improve the accuracy of the server's analysis model.

[0809] As a concrete example, consider a scenario where a factory operator encounters a machine malfunction. This system can simultaneously grasp machine operation data and the operator's emotions, and automatically provide appropriate countermeasures based on a prompt message such as, "Analyze the operator's current emotional state from their facial expressions and voice, and create an operational adjustment plan for the factory robot based on the results." This is expected to improve work efficiency and reduce operator stress.

[0810] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0811] Step 1:

[0812] The server receives operating status data and ambient environment information from terminals attached to the machine. Multiple sensors attached to the terminals collect necessary data such as operating energy, temperature, and vibration from the machine in operation and transmit this data to the server via Bluetooth or Wi-Fi. The input for this step is raw data from the sensors, and the output is processed data stored in a database on the server.

[0813] Step 2:

[0814] The server uses the received data to perform analysis using a machine learning model. The stored data is preprocessed and prepared as an input dataset for executing an anomaly detection algorithm. This determines whether the machine's operating status is normal or abnormal, and also evaluates its efficiency. The input for this step is preprocessed data, and the output is the result of the machine's status evaluation.

[0815] Step 3:

[0816] The server acquires emotional data from the operator's smart device and analyzes it using an emotion engine. The user's device uses a camera and microphone to record facial expressions and voice, and this data is processed by the emotion engine. Based on this data, the emotion engine quantifies stress levels and fatigue levels. The input for this step is facial expression and voice data, and the output is an index representing the emotional state.

[0817] Step 4:

[0818] The server generates a notification to adjust the work process appropriately based on the analysis results. It considers the operator's emotional state and combines it with the machine learning analysis results to determine the optimal work adjustment plan. At this stage, a generative AI model is used to create recommendations that follow the prompt. Finally, the server sends the notification to the operator via a communication device. The inputs to this step are state evaluations and emotional indicators, and the output is a notification indicating specific work adjustment methods.

[0819] Step 5:

[0820] The user (operator) receives notifications from the server and takes appropriate action. Based on the proposed changes to the work process, they review the current situation and adjust machine operation and their own work procedures to improve work efficiency and safety. The input for this step is the notification from the communication device, and the output is the operator's response.

[0821] 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.

[0822] 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.

[0823] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0824] 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.

[0825] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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."

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0842] The following is further disclosed regarding the embodiments described above.

[0843] (Claim 1)

[0844] A device attached to an animal provides means for collecting activity data and biometric information,

[0845] A means for receiving and storing data transmitted from the aforementioned terminal,

[0846] A means to provide a machine learning model that adaptively analyzes trends and anomalies using stored data,

[0847] A means of providing notification via a communication device to propose adaptation measures based on the analysis results,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising means for continuously monitoring changes in the health status of the animal and notifying the user of the analysis results in real time.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising a feedback reflection means for updating an analysis model based on feedback obtained from a user using the aforementioned communication device.

[0853] "Example 1"

[0854] (Claim 1)

[0855] A device attached to an animal provides means for acquiring activity information and biometric data,

[0856] means for receiving and recording information transferred from the aforementioned device,

[0857] A means to provide a machine learning method that automatically analyzes patterns and anomalies using recorded information,

[0858] A means of notifying users via information terminals to show implementation measures based on the analysis results,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising means for continuously monitoring changes in the health status of the animal and immediately notifying the user of the analysis results.

[0862] (Claim 3)

[0863] The system according to claim 1, including a means for reflecting opinions to update the analysis method based on opinions obtained from users through the aforementioned information terminal.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A device attached to an animal is used to collect activity data and biometric information.

[0867] A device for receiving and storing data transmitted from the aforementioned terminal,

[0868] A device that provides a machine learning model that adaptively analyzes trends and anomalies using stored data,

[0869] A device that notifies via a communication terminal to suggest adaptation measures based on the analysis results,

[0870] A visual notification device using a visual display terminal to monitor the health status and stress levels of guard animals and to present information to the supervisor in real time,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, comprising means for detecting changes in the health status of the animal and notifying a monitor in real time of an abnormality.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising a feedback reflection device for dynamically updating an analysis model based on feedback obtained from a monitor using the aforementioned communication terminal.

[0876] "Example 2 of combining an emotion engine"

[0877] (Claim 1)

[0878] A device attached to an animal provides means for collecting exercise information and health information,

[0879] means for receiving and storing information transmitted from the aforementioned device,

[0880] A means for providing a learning algorithm for analyzing anomalies using stored information,

[0881] A means of providing notification via an information transmission device to present countermeasures based on the analysis results,

[0882] A means of providing an emotion analysis function for obtaining the user's emotional state,

[0883] A means for customizing notification content using the aforementioned sentiment analysis results,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, comprising means for continuously monitoring changes in the health indicators of the animals and transmitting individual countermeasures to the user in real time according to the analysis results.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising a response reflection function for updating a learning algorithm based on a response provided by a user via the information transmission device.

[0889] "Application example 2 when combining with an emotional engine"

[0890] (Claim 1)

[0891] A device attached to a machine includes means for collecting operating status data and surrounding environment information,

[0892] means for receiving and storing data transmitted from the aforementioned device,

[0893] A means for providing a learning model that adaptively analyzes the state of a machine using stored data,

[0894] Analytical tools for analyzing the emotions of operators,

[0895] A means of providing notifications via a communication device to adjust the work process based on the analysis results,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for continuously monitoring changes in the operating status of the machine and notifying the operator of the analysis results in real time.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising a feedback reflection means for updating an analysis model based on feedback obtained from an operator using the aforementioned communication device. [Explanation of Symbols]

[0901] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device attached to an animal provides means for collecting activity data and biometric information, A means for receiving and storing data transmitted from the aforementioned terminal, A means to provide a machine learning model that adaptively analyzes trends and anomalies using stored data, A means of providing notification via a communication device to propose adaptation measures based on the analysis results, A system that includes this.

2. The system according to claim 1, comprising means for continuously monitoring changes in the health status of the animal and notifying the user of the analysis results in real time.

3. The system according to claim 1, further comprising a feedback reflection means for updating an analysis model based on feedback obtained from a user using the aforementioned communication device.

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