system

Abstract

We provide the system. [Solution] A means of acquiring biological information from sensors attached to animals, A means for detecting abnormal behavior in animals by analyzing acquired biological information, A means for generating earthquake prediction information based on the results of abnormal behavior analysis, A means of notifying the user of the generated prediction information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Earthquakes are difficult to predict and cause great damage when they occur. However, the current earthquake prediction technologies do not ensure sufficient accuracy and reliability. In view of such a situation, there is a need for a system that can accurately capture the precursors before an earthquake occurs and make a more rapid and reliable prediction. However, the effectiveness of many technologies is limited and they have not been put into practical use. Therefore, an object of the present invention is to provide a new means for minimizing the damage caused by earthquakes by utilizing the sensing ability of animals to predict the occurrence of earthquakes in advance.

Means for Solving the Problems

[0005] This invention provides a means for acquiring biological information using sensors attached to animals, and a means for detecting abnormal behavior in animals by analyzing that information. Furthermore, a system is proposed that includes a means for generating earthquake prediction information based on this abnormal behavior. In particular, by utilizing machine learning algorithms to improve the accuracy of the analysis and evaluating the similarity between past earthquake data and abnormal behavior, the probability of an earthquake occurring can be calculated more accurately. In addition, by immediately notifying the user of the prediction information based on this, a rapid response can be made. In this way, this invention provides a means for achieving earthquake prediction with higher accuracy than conventional technologies.

[0006] "Animals" refer to living organisms that are fitted with sensors to acquire biological information and use it to detect abnormal behavior.

[0007] A "sensor" is a device attached to an animal that detects changes in its environment and biological condition and outputs the data.

[0008] "Biometric information" refers to data obtained from an animal's body, including indicators such as heart rate, movement, and temperature.

[0009] "Abnormal behavior" refers to animal behavior patterns that differ from normal behavior, and are considered to be precursors to earthquakes, environmental changes, and other such events.

[0010] "Analysis" refers to the process of identifying abnormal behavior from biological information and evaluating the likelihood of an earthquake occurring.

[0011] A "machine learning algorithm" is a programming technique that analyzes large amounts of data and automatically learns its patterns, and is used to improve the accuracy of detecting abnormal behavior.

[0012] "Predictive information" refers to data on the likelihood of earthquakes occurring, generated based on acquired biometric information and its analysis results.

[0013] "Notification" refers to a means of communicating generated predictive information to the user to encourage prompt action. [Brief explanation of the drawing]

[0014] [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 the data processing device and 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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system that acquires biological information from animals and predicts earthquakes based on that information. The system mainly consists of three elements: a terminal, a server, and a user.

[0036] First, the device attaches sensors to the animal and acquires biometric information in real time. This information includes the animal's movement, heart rate, and body surface temperature, and plays a crucial role in detecting abnormal behavior. The device temporarily stores this data and sends it to the server at predetermined time intervals.

[0037] Next, the server receives data transmitted from the terminal and analyzes it using machine learning algorithms. If abnormal animal behavior is detected through this analysis, the pattern is evaluated and the probability of an earthquake occurring is calculated. During the analysis process, the data is also compared with past earthquake data to improve the accuracy of the prediction.

[0038] The prediction information generated by the server is immediately sent to the user's terminal. Based on this information, the user can take necessary measures. For example, if a large-scale earthquake is predicted, they can issue evacuation advisories to residents and relevant parties, or prepare an emergency response system. Furthermore, a feedback system can be introduced to verify the accuracy and practicality of the information, allowing users to incorporate their actual experiences.

[0039] As a specific example, data on abnormal behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. This allows farmers to ensure the safety of their livestock while simultaneously evacuating to a safe place themselves. In this way, the present invention provides an embodiment that prevents damage caused by earthquakes and improves people's safety.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device acquires biometric information from sensors attached to the animal. Here, data such as heart rate, body temperature, and movement patterns are collected in real time and temporarily stored in memory.

[0043] Step 2:

[0044] The device periodically (e.g., every minute) sends collected biometric information to the server. The data is encrypted and packetized to ensure secure communication.

[0045] Step 3:

[0046] The server stores the received biometric information in a database. This allows for efficient use of the data in subsequent analysis processes.

[0047] Step 4:

[0048] The server uses machine learning algorithms to analyze the stored data. Specifically, it detects abnormal behavior patterns and compares them with past earthquake data to assess the likelihood of an earthquake occurring.

[0049] Step 5:

[0050] The server generates earthquake prediction information. The generated information includes details such as the predicted magnitude of the earthquake, the area where it is expected to occur, and the time of occurrence.

[0051] Step 6:

[0052] The server generates predictive information and sends it to the user's terminal. Because timeliness is crucial for notifications, a dedicated communication protocol is used to ensure real-time information delivery.

[0053] Step 7:

[0054] The user reviews the predictive information received via their device. Based on this information, the user takes measures such as evacuating to a safe place or disseminating information to relevant parties.

[0055] Step 8:

[0056] Users input feedback into their devices describing their actual experiences after an earthquake. This feedback is sent to a server and used to improve the accuracy of future analysis models.

[0057] (Example 1)

[0058] 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."

[0059] Current earthquake prediction technology suffers from insufficient accuracy, making it difficult to adequately ensure people's safety. Furthermore, conventional methods struggle with real-time information gathering and analysis, hindering responses in situations requiring rapid action. Additionally, techniques utilizing animal behavior to predict natural phenomena are not yet fully practical, and their potential needs to be explored.

[0060] 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.

[0061] In this invention, the server includes means for acquiring biological information from a measuring device attached to an animal and transmitting the collected data at regular intervals to a central processing unit using communication technology; means for detecting unusual animal behavior using data analysis technology in the central processing unit; and means for generating prediction information for the occurrence of natural phenomena based on the analysis results of the unusual behavior. This enables highly accurate earthquake prediction using animal behavior data, allowing for a rapid and appropriate response.

[0062] An "animal" is an organism belonging to a specific biological species from which biological information is to be acquired.

[0063] A "measuring device" is a device used to detect and acquire biological information from animals.

[0064] "Biometric information" refers to physiological data such as an animal's heart rate, movement, and body surface temperature.

[0065] "Communication technology" refers to the technical means for transferring acquired data to a central processing unit.

[0066] A "central processing unit" is a computer system that receives and analyzes transmitted biometric data.

[0067] "Data analysis technology" refers to techniques used to analyze biological information and identify unique behaviors in animals.

[0068] "Unusual behavior" refers to patterns of animal movement that are not normally observed and serves as an indicator of abnormality.

[0069] "Natural phenomena" refer to phenomena that are not caused by human activity, such as earthquakes.

[0070] "Occurrence prediction information" refers to information that indicates the likelihood of future natural phenomena based on analyzed data.

[0071] A "user terminal" is a device that receives generated prediction information and notifies the user.

[0072] This invention is a system that predicts natural phenomena based on the biological information of animals, and consists of three elements: a terminal, a server, and a user. Specific embodiments of each element and the overall system are shown below.

[0073] First, the device acquires biometric information via sensors attached to the animal. These sensors measure heart rate, movement, and body surface temperature in real time, allowing for highly accurate monitoring of the animal's physiological changes. The collected data is temporarily stored on the device and transmitted to a server at predetermined time intervals using wireless communication technology. Specifically, Wi-Fi and Bluetooth are commonly used.

[0074] Next, the server receives biometric information transmitted from the terminal. Based on the received data, it performs a process to detect unusual animal behavior using data analysis techniques. Here, machine learning algorithms are used for data analysis, comparing past earthquake data with current biometric information. This allows for a highly accurate determination of whether the unusual animal behavior is a precursor to a natural phenomenon. For this analysis, software tools such as TENSORFLOW® or scikit-learn using Python are employed.

[0075] Users receive forecast information about natural phenomena transmitted from the server and check this information on their terminals. Based on the forecast information, users can take necessary measures. For example, if a large-scale natural phenomenon is predicted, users can take evacuation actions or prepare emergency response systems. In addition, users can contribute to improving the accuracy of the system by sending information based on their actual experiences using the feedback function.

[0076] As a concrete example, data on unusual behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. The introduction of this system will enable farmers to take steps to ensure their own safety and the safety of their livestock.

[0077] An example of a prompt message is, "Generate the next earthquake prediction based on abnormal cattle behavior data in rural areas." This allows the generating AI model to provide appropriate prediction information.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device continuously acquires biometric information such as heart rate, movement, and body surface temperature using sensors attached to the animal. The input biometric information is recorded in real time and subjected to initial filtering to remove outliers. This allows for the temporary storage of biometric information with less noise, improving the accuracy of the data.

[0081] Step 2:

[0082] The device packets temporarily stored biometric information at regular intervals and sends it to a server using communication technologies such as Wi-Fi or Bluetooth. The input is temporarily stored biometric information, which is then packetized according to a specific protocol. The output is the transmission of data packets to the server.

[0083] Step 3:

[0084] The server receives data packets sent from the terminal and stores them in the database. During this process, the server verifies the consistency and reliability of the data, and any inconsistent data is excluded. The received data is then ready for analysis and data processing.

[0085] Step 4:

[0086] The server inputs received biometric information into a machine learning algorithm to detect unique animal behaviors. Specifically, it uses models trained with TensorFlow or scikit-learn in Python to perform data analysis. The output is the detection result of unique behaviors and their probability scores.

[0087] Step 5:

[0088] Based on the detection results, the server calculates the probability of natural phenomena occurring by comparing them with a database of past earthquakes. This process uses statistical methods to compare the abnormal behavior patterns with past cases that are similar. Finally, prediction information about the occurrence of natural phenomena is generated.

[0089] Step 6:

[0090] The server sends the generated prediction information to the user's terminal. This information includes details of the prediction and recommended countermeasures. This allows the user to take the necessary actions based on the received information.

[0091] Step 7:

[0092] Users review the received prediction information and take appropriate action. They also use the feedback system to send information about the actual situation and the accuracy of the predictions back to the server. The server uses this feedback to further improve the accuracy of the machine learning model.

[0093] (Application Example 1)

[0094] 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."

[0095] Conventional earthquake prediction systems have the challenge of not being able to respond quickly to human casualties and ensure the safety of animals. This invention aims to improve the safety of animals and people by accurately identifying earthquake precursors using animal biological information.

[0096] 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.

[0097] In this invention, the server includes a device for acquiring biological information from animals, a device for analyzing the acquired biological information to identify abnormal behavior in animals, and a device for generating earthquake prediction information based on the analysis results of the abnormal behavior. This enables rapid measures to ensure the safety of animals and people.

[0098] "Animals" refers to animals whose behavior is monitored using biosensors.

[0099] "Biometric information" refers to data that indicates an animal's health status and behavior, including animal movement, heart rate, and body surface temperature.

[0100] "Device" refers to equipment or systems used to acquire, analyze, and transmit biological information from animals.

[0101] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and is considered a precursor to natural phenomena such as earthquakes.

[0102] "Predictive information" refers to information about the likelihood of future earthquakes, generated by analyzing acquired data.

[0103] "Users" refers to individuals or organizations that receive predictive information and take action for the safety of animals and people.

[0104] "Artificial intelligence" refers to a technology that uses machine learning algorithms to analyze the biological information of animals and identify abnormal behavior.

[0105] "Earthquake data" refers to past earthquake information and related data from the time of their occurrence, and is used as the basis for prediction algorithms.

[0106] The system for realizing this invention consists of a terminal that uses sensors to acquire biological information from animals, a server that analyzes that data, and a user terminal that receives the analysis results.

[0107] The terminal is connected to sensors attached to the animals, and acquires biometric information such as the animals' movements, heart rate, and body surface temperature in real time. This data is temporarily stored on the terminal and then transmitted to the server at predetermined time intervals.

[0108] The server is built on Python and uses artificial intelligence technology to analyze animal biometric information. Specifically, it uses machine learning libraries such as TensorFlow to identify patterns of abnormal behavior and evaluate the likelihood of an earthquake based on the results. During the analysis process, Firebase is used to manage data in real time and compare it with historical earthquake data to improve the accuracy of predictions.

[0109] The server sends push notifications to user terminals with earthquake prediction information as analysis results. Upon receiving this information, users can take swift action according to the situation. For example, a zoo manager can receive the notification and immediately implement an evacuation plan to ensure the safety of animals and visitors.

[0110] Furthermore, this system allows users to provide feedback on their actual experiences, which can then be used to continuously improve the predictive model through retraining.

[0111] Example of a prompt

[0112] Prompts for generative AI models:

[0113] "Please propose a method to improve the algorithm that analyzes earthquake precursors from abnormal animal behavior data collected by sensors. The server will use TensorFlow."

[0114] In this way, the present invention provides an effective means of predicting earthquakes by utilizing abnormal animal behavior and ensuring the safety of animals and people.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The device acquires biometric information in real time from sensors attached to animals. Inputs include data such as animal movement, heart rate, and body surface temperature. This data is temporarily stored on the device. The output is a temporary dataset for later transmission to a server.

[0118] Step 2:

[0119] Data is sent to the server. The terminal uploads data stored on the server at predetermined time intervals. This input data is stored in the server's real-time database and is ready for analysis. The output is formatted data for analysis.

[0120] Step 3:

[0121] The server uses TensorFlow to run a machine learning model and identify abnormal animal behavior from input data. The input data is compared with historical data of normal behavior to detect abnormal behavior patterns. The output is a flag indicating whether or not abnormal behavior was detected, along with detailed information about it.

[0122] Step 4:

[0123] The server generates earthquake prediction information based on the analysis of abnormal behavior. The probability of an earthquake occurring is evaluated by comparing it with past earthquake data. The output is data that quantifies the specific probability of an earthquake occurring, as earthquake prediction information.

[0124] Step 5:

[0125] The server sends the generated prediction information to the user's terminal. The user's terminal receives this information and displays an alert notification. The input is prediction data, and the output is a presentation of earthquake prediction information in a format understandable to the user.

[0126] Step 6:

[0127] Based on the information provided, the user considers and implements safety measures. Specifically, in the zoo example, an evacuation plan is created and a rapid response is initiated. The input is the presented predictive information, and the output is the implementation of specific response actions.

[0128] 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.

[0129] This invention combines an earthquake prediction system that utilizes abnormal animal behavior with an emotion engine that recognizes the user's emotional state to provide more appropriate information. The system mainly consists of a terminal, a server, an emotion engine, and a user.

[0130] First, the device acquires biometric information from sensors attached to the animal. This information includes the animal's heart rate, body surface temperature, and movement patterns, and is monitored in real time to serve as basic data for detecting abnormal behavior. The acquired data is sent to a server, where it is analyzed using machine learning algorithms.

[0131] The server detects abnormal behavior from the analysis results, evaluates its similarity to past earthquake data, and generates earthquake prediction information. In addition, it is equipped with an emotion engine that acquires emotional information through the user's terminal. The emotion engine analyzes the user's stress level and anxiety and adjusts the notification method accordingly. For example, for users in a high-stress state, notifications can be made gentler or additional support information can be provided to prevent excessive anxiety.

[0132] Through these notifications, users can receive earthquake prediction information and take appropriate evacuation and safety measures. Furthermore, by using the feedback function, users can input their own psychological experiences into the system, improving the accuracy of the emotion engine's analysis.

[0133] A concrete example is that, when the emotion engine is active and a large-scale earthquake is predicted, if the system determines that the user is in a higher-than-usual stress state, reassuring words may be added to the notification message. In this way, the present invention provides an embodiment that enables the provision of more effective and personalized earthquake prediction information by using animal biometric information and user emotional information in combination.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device acquires biometric information from sensors attached to the animal. Specifically, it acquires the animal's heart rate, body temperature, and movement patterns in real time and temporarily stores them in memory.

[0137] Step 2:

[0138] The device periodically (for example, every minute) sends collected biometric information to the server. The data is encrypted and transmitted over the network in a secure manner.

[0139] Step 3:

[0140] The server stores the received biometric information in a database. During storage, it verifies the consistency of the time series and converts the data to a format suitable for analysis.

[0141] Step 4:

[0142] The server uses stored biometric data and employs machine learning algorithms to perform analysis. This allows it to detect abnormal animal behavior patterns and assess the likelihood of an earthquake occurring.

[0143] Step 5:

[0144] The server utilizes an emotion engine to analyze emotional information obtained from the user's device. It evaluates the user's stress level and anxiety state, and determines the optimal method of providing information.

[0145] Step 6:

[0146] The server generates earthquake prediction information and sends it to the user. Based on the sentiment analysis results, the content and wording of the notification message are individually customized. For example, a reassuring message is added for users with high stress levels.

[0147] Step 7:

[0148] The user reviews the received earthquake prediction information. If necessary, they initiate safety measures or evacuation actions. Furthermore, providing feedback on the received information can contribute to improving the system's accuracy.

[0149] Step 8:

[0150] User feedback is sent to the server and used to improve the emotion engine and machine learning models. This allows the system to continuously improve the accuracy of its predictions and the quality of its user interactions.

[0151] (Example 2)

[0152] 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".

[0153] Conventional earthquake prediction systems are based on abnormal animal behavior and historical data, and have limitations in providing information that takes into account the individual emotional state of users. This has led to problems such as prediction information causing unnecessary stress to users and a lack of motivation to take practical action. Therefore, there is a need to provide more accurate and user-adapted countermeasures.

[0154] 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.

[0155] In this invention, the server includes means for acquiring and analyzing animal biological information, means for acquiring and analyzing the user's emotional state and adjusting notification content, and means for providing the generated prediction information and adjusted notifications to the user. This makes it possible to provide earthquake prediction information that integrates abnormal animal behavior and the user's emotional state.

[0156] "Animal sensors" are devices attached to animals to acquire biological information.

[0157] "Biometric information" refers to data that represents the physiological state of an animal, such as its heart rate, body surface temperature, and movement patterns.

[0158] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and serves as an indicator for earthquake prediction.

[0159] "Earthquake prediction information" refers to information about the likelihood of an earthquake occurring, generated based on the analysis of abnormal behavior.

[0160] "User emotional state" refers to data that indicates the user's stress level and level of anxiety.

[0161] "Means for adjusting notification content" refers to processing means for changing the format and expression of information provided in accordance with the results of user sentiment analysis.

[0162] "Feedback" refers to the user's reactions and evaluations of a system.

[0163] "Means of improving analytical accuracy" refer to methods of improving the quality of system predictions and notifications by utilizing collected data and feedback.

[0164] This invention relates to a system that predicts earthquake occurrences based on abnormal animal behavior and provides information while also considering the user's emotional state. This system consists of a terminal, a server, an emotion engine, and a user component.

[0165] The device acquires biometric information such as heart rate, body surface temperature, and movement patterns in real time from sensors attached to the animal. This data is transmitted to a server using communication methods such as Bluetooth.

[0166] The server is a computing device that analyzes received biometric information using machine learning algorithms. It uses Python and other data analysis software to detect abnormal animal behavior. By comparing the results of this abnormal behavior analysis with past earthquake data, it generates earthquake prediction information.

[0167] Furthermore, the server collects the user's emotional state through an emotion engine. This information is obtained using smartphones or wearable devices. Based on the user's emotional state, the server can adjust notification content, providing the user with gentler messages or additional support information.

[0168] Users can receive earthquake prediction information through notifications and take appropriate evacuation and safety measures based on their situation. Users can also provide feedback to the system, which will be used to improve the accuracy of the system's analysis.

[0169] For example, if the system detects abnormal animal behavior and predicts a possible earthquake, and the emotion engine detects the user is in a high-stress state, it will add reassuring words to the notification message, such as "Please stay calm. Evacuating to a safe place is important," and provide a link to detailed evacuation information. In this way, the user can receive information to take more appropriate action.

[0170] An example of a prompt that optimizes user notifications using a generative AI model is, "How can earthquake prediction notifications be optimized based on the user's emotional state?" This prompt allows the system to formulate appropriate responses tailored to the individual user's situation.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device acquires biometric information from sensors attached to the animal. Inputs include the animal's heart rate, body surface temperature, and movement patterns, and this data is monitored in real time. The data is acquired by the device via Bluetooth communication and then transmitted to a server. The output is a collection of biometric data from the sensors.

[0174] Step 2:

[0175] The server receives biometric information transmitted from the terminal and begins analysis. The input is biometric information obtained in real time. The server executes a machine learning algorithm using Python to perform pattern recognition to detect abnormal behavior. Specifically, the algorithm compares each dataset with known abnormal patterns. The output is the analysis result indicating whether or not abnormal behavior occurred.

[0176] Step 3:

[0177] The server compares the results of the abnormal behavior analysis with past earthquake data. The inputs are the analysis results and past earthquake data stored in the database. The server performs a statistical similarity analysis to assess the probability of an earthquake occurring. The algorithm then refines the prediction model based on similar past cases. The output is earthquake prediction information.

[0178] Step 4:

[0179] The server acquires the user's emotional state via an emotion engine. The input consists of user stress level and anxiety data collected from smartphones and wearable devices. The server analyzes this input data and performs specific actions to understand the user's emotional state. The output is the analysis result regarding the user's emotional state.

[0180] Step 5:

[0181] The server adjusts the notification content based on the acquired emotional state. The input consists of the user's emotional analysis results and earthquake prediction information. The server utilizes a generative AI model to generate prompt messages and optimize the message the user receives. Specific actions include, for example, including gentler messages or additional safety information. The output is the adjusted notification message.

[0182] Step 6:

[0183] The user receives notifications from the server via their device. The input is information provided as a coordinated notification message. The user reviews the message content and takes action to make specific decisions regarding earthquake preparations and evacuation. The output is the appropriate safety measures taken by the user.

[0184] Step 7:

[0185] Users provide feedback after receiving a notification. The input consists of the user's own reaction and evaluation of the notification's effectiveness. This user feedback is sent to the server, and specific actions are taken to improve future notifications. The output is feedback data that helps improve the accuracy of system analysis.

[0186] (Application Example 2)

[0187] 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".

[0188] In recent years, the importance of improving the accuracy of natural disaster predictions has increased. However, conventional prediction systems do not take into account the emotional state of users, and the information they provide may increase anxiety and stress. Furthermore, optimizing the customer experience in physical stores requires a flexible approach that combines disaster information with the emotional state of customers. Solving these challenges is essential.

[0189] 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.

[0190] In this invention, the server includes means for analyzing biometric information acquired from a detection device to detect abnormal behavior, a decision device for generating predictive information and providing information based on the user's emotional state, and means for adjusting the customer experience. This makes it possible to adjust predictive information about natural disasters according to the user's emotional state and optimize the customer experience in physical stores.

[0191] A "detection device" is a device attached to an animal to acquire biological information.

[0192] "Biometric information" refers to data such as an animal's heart rate, body surface temperature, and movement patterns.

[0193] "Abnormal behavior" refers to behaviors in animals that are different from the norm and may be a precursor to an earthquake.

[0194] "Predictive information" refers to information about predicting the occurrence of natural disasters, generated based on the results of an analysis of abnormal behavior.

[0195] A "decision-making device" refers to an analytical tool that analyzes the user's emotional state and provides appropriate information.

[0196] "Emotional state" refers to the user's psychological state, such as their stress level or anxiety level.

[0197] "Customer experience" refers to the customer's purchasing and service usage experience at physical stores.

[0198] "Information provision" refers to the act of conveying predictive information, reassuring messages, or advice to users.

[0199] The system for implementing the present invention consists of a detection device attached to an animal, a server, a user terminal, and a display device. The server acquires biological information such as the animal's heart rate, body surface temperature, and movement patterns, and analyzes it using a machine learning algorithm. Based on the analyzed data, the server detects abnormal behavior and generates predictive information by comparing it with past natural disaster data.

[0200] A key element of this system is the emotion engine, which analyzes the user's emotional state in real time. The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes this data to understand their current emotional state. The emotional state quantifies psychological factors such as stress and anxiety, and based on this, the server optimizes the information it conveys to the user. For example, it adjusts the tone and content of notifications regarding predicted natural disasters to match the user's emotional state. This allows users to receive information with greater peace of mind.

[0201] Furthermore, in physical stores, the system uses earthquake probability information and customer sentiment data to display personalized messages to customers. This provides a sense of security while encouraging safe behavior. For example, if a store is crowded and the system detects that a customer looks anxious, it will display a message such as, "This is the area where you can enjoy shopping with peace of mind."

[0202] An example of a prompt message to use when using a generative AI model is: "When the system detects that the user is feeling anxious, generate relaxing recommendations and reassuring messages." This allows the system to provide a more personalized user experience.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The terminal acquires biometric information such as heart rate, body surface temperature, and movement patterns from detection devices attached to animals. The input is biometric data from sensors, and by collecting this data in real time, it outputs it as biometric information.

[0206] Step 2:

[0207] The server applies machine learning algorithms based on biometric information received from the terminal to detect abnormal animal behavior. The input is biometric information, and by performing data analysis on this information, it outputs patterns of abnormal animal behavior.

[0208] Step 3:

[0209] The server generates earthquake prediction information by comparing detected abnormal behavior patterns with past natural disaster data. The input consists of abnormal behavior patterns and historical data, and the prediction information is obtained by matching and analyzing them.

[0210] Step 4:

[0211] The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state. The input is video and audio data, which is used for emotion analysis, and the emotional state is output.

[0212] Step 5:

[0213] The server considers the generated predictive information and the user's emotional state to provide optimized information to the user. The input consists of predictive information and emotional state; based on these, the server generates notification content and outputs it to the user.

[0214] Step 6:

[0215] In physical stores, display devices show personalized messages to customers based on notifications from the server. The input is the notified information, and accordingly, the system outputs a message designed to reassure the customer.

[0216] Step 7:

[0217] Users contribute to improving the accuracy of the emotion engine by providing feedback to the system after receiving a notification. The input is feedback information, and reflecting this leads to improvements in the system.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system that acquires biological information from animals and predicts earthquakes based on that information. The system mainly consists of three elements: a terminal, a server, and a user.

[0235] First, the device attaches sensors to the animal and acquires biometric information in real time. This information includes the animal's movement, heart rate, and body surface temperature, and plays a crucial role in detecting abnormal behavior. The device temporarily stores this data and sends it to the server at predetermined time intervals.

[0236] Next, the server receives data transmitted from the terminal and analyzes it using machine learning algorithms. If abnormal animal behavior is detected through this analysis, the pattern is evaluated and the probability of an earthquake occurring is calculated. During the analysis process, the data is also compared with past earthquake data to improve the accuracy of the prediction.

[0237] The prediction information generated by the server is immediately sent to the user's terminal. Based on this information, the user can take necessary measures. For example, if a large-scale earthquake is predicted, they can issue evacuation advisories to residents and relevant parties, or prepare an emergency response system. Furthermore, a feedback system can be introduced to verify the accuracy and practicality of the information, allowing users to incorporate their actual experiences.

[0238] As a specific example, data on abnormal behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. This allows farmers to ensure the safety of their livestock while simultaneously evacuating to a safe place themselves. In this way, the present invention provides an embodiment that prevents damage caused by earthquakes and improves people's safety.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The device acquires biometric information from sensors attached to the animal. Here, data such as heart rate, body temperature, and movement patterns are collected in real time and temporarily stored in memory.

[0242] Step 2:

[0243] The device periodically (e.g., every minute) sends collected biometric information to the server. The data is encrypted and packetized to ensure secure communication.

[0244] Step 3:

[0245] The server stores the received biometric information in a database. This allows for efficient use of the data in subsequent analysis processes.

[0246] Step 4:

[0247] The server uses machine learning algorithms to analyze the stored data. Specifically, it detects abnormal behavior patterns and compares them with past earthquake data to assess the likelihood of an earthquake occurring.

[0248] Step 5:

[0249] The server generates earthquake prediction information. The generated information includes details such as the predicted magnitude of the earthquake, the area where it is expected to occur, and the time of occurrence.

[0250] Step 6:

[0251] The server generates predictive information and sends it to the user's terminal. Because timeliness is crucial for notifications, a dedicated communication protocol is used to ensure real-time information delivery.

[0252] Step 7:

[0253] The user reviews the predictive information received via their device. Based on this information, the user takes measures such as evacuating to a safe place or disseminating information to relevant parties.

[0254] Step 8:

[0255] Users input feedback into their devices describing their actual experiences after an earthquake. This feedback is sent to a server and used to improve the accuracy of future analysis models.

[0256] (Example 1)

[0257] 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."

[0258] Current earthquake prediction technology suffers from insufficient accuracy, making it difficult to adequately ensure people's safety. Furthermore, conventional methods struggle with real-time information gathering and analysis, hindering responses in situations requiring rapid action. Additionally, techniques utilizing animal behavior to predict natural phenomena are not yet fully practical, and their potential needs to be explored.

[0259] 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.

[0260] In this invention, the server includes means for acquiring biological information from a measuring device attached to an animal and transmitting the collected data at regular intervals to a central processing unit using communication technology; means for detecting unusual animal behavior using data analysis technology in the central processing unit; and means for generating prediction information for the occurrence of natural phenomena based on the analysis results of the unusual behavior. This enables highly accurate earthquake prediction using animal behavior data, allowing for a rapid and appropriate response.

[0261] An "animal" is an organism belonging to a specific biological species from which biological information is to be acquired.

[0262] A "measuring device" is a device used to detect and acquire biological information from animals.

[0263] "Biometric information" refers to physiological data such as an animal's heart rate, movement, and body surface temperature.

[0264] "Communication technology" refers to the technical means for transferring acquired data to a central processing unit.

[0265] A "central processing unit" is a computer system that receives and analyzes transmitted biometric data.

[0266] "Data analysis technology" refers to techniques used to analyze biological information and identify unique behaviors in animals.

[0267] "Unusual behavior" refers to patterns of animal movement that are not normally observed and serves as an indicator of abnormality.

[0268] "Natural phenomena" refer to phenomena that are not caused by human activity, such as earthquakes.

[0269] "Occurrence prediction information" refers to information that indicates the likelihood of future natural phenomena based on analyzed data.

[0270] A "user terminal" is a device that receives generated prediction information and notifies the user.

[0271] This invention is a system that predicts natural phenomena based on the biological information of animals, and consists of three elements: a terminal, a server, and a user. Specific embodiments of each element and the overall system are shown below.

[0272] First, the device acquires biometric information via sensors attached to the animal. These sensors measure heart rate, movement, and body surface temperature in real time, allowing for highly accurate monitoring of the animal's physiological changes. The collected data is temporarily stored on the device and transmitted to a server at predetermined time intervals using wireless communication technology. Specifically, Wi-Fi and Bluetooth are commonly used.

[0273] Next, the server receives biometric information transmitted from the terminal. Based on the received data, it performs a process to detect unusual animal behavior using data analysis techniques. Here, machine learning algorithms are used for data analysis, comparing past earthquake data with current biometric information. This allows for a highly accurate determination of whether the unusual animal behavior is a precursor to a natural phenomenon. For this analysis, software tools such as TensorFlow or scikit-learn using Python are employed.

[0274] Users receive forecast information about natural phenomena transmitted from the server and check this information on their terminals. Based on the forecast information, users can take necessary measures. For example, if a large-scale natural phenomenon is predicted, users can take evacuation actions or prepare emergency response systems. In addition, users can contribute to improving the accuracy of the system by sending information based on their actual experiences using the feedback function.

[0275] As a concrete example, data on unusual behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. The introduction of this system will enable farmers to take steps to ensure their own safety and the safety of their livestock.

[0276] An example of a prompt message is, "Generate the next earthquake prediction based on abnormal cattle behavior data in rural areas." This allows the generating AI model to provide appropriate prediction information.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The device continuously acquires biometric information such as heart rate, movement, and body surface temperature using sensors attached to the animal. The input biometric information is recorded in real time and subjected to initial filtering to remove outliers. This allows for the temporary storage of biometric information with less noise, improving the accuracy of the data.

[0280] Step 2:

[0281] The device packets temporarily stored biometric information at regular intervals and sends it to a server using communication technologies such as Wi-Fi or Bluetooth. The input is temporarily stored biometric information, which is then packetized according to a specific protocol. The output is the transmission of data packets to the server.

[0282] Step 3:

[0283] The server receives the data packets sent from the terminal and stores them in the database. At this time, the consistency and reliability of the data are confirmed, and inconsistent data is excluded. The received data becomes the object of analysis, and preparations for data processing are completed.

[0284] Step 4:

[0285] The server inputs the received biological information into a machine learning algorithm to detect the specific behaviors of animals. Specifically, a model trained with TensorFlow or scikit-learn using Python is utilized for data analysis. The output is the detection result of specific behaviors and their probability scores.

[0286] Step 5:

[0287] Based on the detection results, the server compares with the past earthquake database to calculate the likelihood of natural phenomena occurring. In this process, statistical methods are used to compare with past cases similar to the abnormal behavior pattern. Finally, the generation prediction information of natural phenomena is generated.

[0288] Step 6:

[0289] The server sends the generated occurrence prediction information to the user's terminal. The information sent includes the details of the prediction and recommended countermeasures. Thereby, the user can take necessary measures based on the received information.

[0290] Step 7:

[0291] The user checks the received prediction information and implements appropriate responses. Also, using the feedback system, the user returns information regarding the actual situation and the accuracy of the prediction to the server. The server refers to this feedback and uses it to further improve the accuracy of the machine learning model.

[0292] (Application Example 1)

[0293] 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."

[0294] Conventional earthquake prediction systems have the challenge of not being able to respond quickly to human casualties and ensure the safety of animals. This invention aims to improve the safety of animals and people by accurately identifying earthquake precursors using animal biological information.

[0295] 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.

[0296] In this invention, the server includes a device for acquiring biological information from animals, a device for analyzing the acquired biological information to identify abnormal behavior in animals, and a device for generating earthquake prediction information based on the analysis results of the abnormal behavior. This enables rapid measures to ensure the safety of animals and people.

[0297] "Animals" refers to animals whose behavior is monitored using biosensors.

[0298] "Biometric information" refers to data that indicates an animal's health status and behavior, including animal movement, heart rate, and body surface temperature.

[0299] "Device" refers to equipment or systems used to acquire, analyze, and transmit biological information from animals.

[0300] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and is considered a precursor to natural phenomena such as earthquakes.

[0301] "Predictive information" refers to information about the likelihood of future earthquakes, generated by analyzing acquired data.

[0302] "User" refers to a person or group who receives prediction information and takes actions for the safety of animals and humans.

[0303] "Artificial intelligence" refers to a technology that analyzes the biological information of animals using machine learning algorithms and identifies abnormal behaviors.

[0304] "Earthquake data" refers to past earthquake information and related data at the time of its occurrence, which is used as the basis for prediction algorithms.

[0305] The system for realizing this invention is composed of a terminal using sensors to acquire biological information from animals, a server for analyzing the data, and a user terminal for receiving the analysis results.

[0306] A sensor attached to the animal is connected to the terminal, which acquires biological information such as the movement, heart rate, and body surface temperature of the animal in real time. These data are temporarily stored in the terminal and then transmitted to the server at predetermined time intervals.

[0307] The server is built based on Python and uses artificial intelligence technology to analyze the biological information of animals. Specifically, it uses machine learning libraries such as TensorFlow to identify patterns of abnormal behaviors and evaluate the possibility of earthquake occurrence based on the results. In the process of analysis, Firebase is used to manage data in real time and compare it with past earthquake data to improve the accuracy of prediction.

[0308] The user terminal receives push notifications of earthquake prediction information as the analysis result from the server. The user who receives this information can take prompt actions according to the situation. For example, the administrator of a zoo can immediately execute an evacuation plan upon receiving the notification to ensure the safety of animals and visitors.

[0309] Furthermore, this system allows users to provide feedback on their actual experiences, which can then be used to continuously improve the predictive model through retraining.

[0310] Example of a prompt

[0311] Prompts for generative AI models:

[0312] "Please propose a method to improve the algorithm that analyzes earthquake precursors from abnormal animal behavior data collected by sensors. The server will use TensorFlow."

[0313] In this way, the present invention provides an effective means of predicting earthquakes by utilizing abnormal animal behavior and ensuring the safety of animals and people.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The device acquires biometric information in real time from sensors attached to animals. Inputs include data such as animal movement, heart rate, and body surface temperature. This data is temporarily stored on the device. The output is a temporary dataset for later transmission to a server.

[0317] Step 2:

[0318] Data is sent to the server. The terminal uploads data stored on the server at predetermined time intervals. This input data is stored in the server's real-time database and is ready for analysis. The output is formatted data for analysis.

[0319] Step 3:

[0320] The server uses TensorFlow to run a machine learning model and identify abnormal animal behavior from input data. The input data is compared with historical data of normal behavior to detect abnormal behavior patterns. The output is a flag indicating whether or not abnormal behavior was detected, along with detailed information about it.

[0321] Step 4:

[0322] The server generates earthquake prediction information based on the analysis of abnormal behavior. The probability of an earthquake occurring is evaluated by comparing it with past earthquake data. The output is data that quantifies the specific probability of an earthquake occurring, as earthquake prediction information.

[0323] Step 5:

[0324] The server sends the generated prediction information to the user's terminal. The user's terminal receives this information and displays an alert notification. The input is prediction data, and the output is a presentation of earthquake prediction information in a format understandable to the user.

[0325] Step 6:

[0326] Based on the information provided, the user considers and implements safety measures. Specifically, in the zoo example, an evacuation plan is created and a rapid response is initiated. The input is the presented predictive information, and the output is the implementation of specific response actions.

[0327] 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.

[0328] This invention combines an earthquake prediction system that utilizes abnormal animal behavior with an emotion engine that recognizes the user's emotional state to provide more appropriate information. The system mainly consists of a terminal, a server, an emotion engine, and a user.

[0329] First, the device acquires biometric information from sensors attached to the animal. This information includes the animal's heart rate, body surface temperature, and movement patterns, and is monitored in real time to serve as basic data for detecting abnormal behavior. The acquired data is sent to a server, where it is analyzed using machine learning algorithms.

[0330] The server detects abnormal behavior from the analysis results, evaluates its similarity to past earthquake data, and generates earthquake prediction information. In addition, it is equipped with an emotion engine that acquires emotional information through the user's terminal. The emotion engine analyzes the user's stress level and anxiety and adjusts the notification method accordingly. For example, for users in a high-stress state, notifications can be made gentler or additional support information can be provided to prevent excessive anxiety.

[0331] Through these notifications, users can receive earthquake prediction information and take appropriate evacuation and safety measures. Furthermore, by using the feedback function, users can input their own psychological experiences into the system, improving the accuracy of the emotion engine's analysis.

[0332] A concrete example is that, when the emotion engine is active and a large-scale earthquake is predicted, if the system determines that the user is in a higher-than-usual stress state, reassuring words may be added to the notification message. In this way, the present invention provides an embodiment that enables the provision of more effective and personalized earthquake prediction information by using animal biometric information and user emotional information in combination.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The device acquires biometric information from sensors attached to the animal. Specifically, it acquires the animal's heart rate, body temperature, and movement patterns in real time and temporarily stores them in memory.

[0336] Step 2:

[0337] The device periodically (for example, every minute) sends collected biometric information to the server. The data is encrypted and transmitted over the network in a secure manner.

[0338] Step 3:

[0339] The server stores the received biometric information in a database. During storage, it verifies the consistency of the time series and converts the data to a format suitable for analysis.

[0340] Step 4:

[0341] The server uses stored biometric data and employs machine learning algorithms to perform analysis. This allows it to detect abnormal animal behavior patterns and assess the likelihood of an earthquake occurring.

[0342] Step 5:

[0343] The server utilizes an emotion engine to analyze emotional information obtained from the user's device. It evaluates the user's stress level and anxiety state, and determines the optimal method of providing information.

[0344] Step 6:

[0345] The server generates earthquake prediction information and sends it to the user. Based on the sentiment analysis results, the content and wording of the notification message are individually customized. For example, a reassuring message is added for users with high stress levels.

[0346] Step 7:

[0347] The user reviews the received earthquake prediction information. If necessary, they initiate safety measures or evacuation actions. Furthermore, providing feedback on the received information can contribute to improving the system's accuracy.

[0348] Step 8:

[0349] User feedback is sent to the server and used to improve the emotion engine and machine learning models. This allows the system to continuously improve the accuracy of its predictions and the quality of its user interactions.

[0350] (Example 2)

[0351] 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".

[0352] Conventional earthquake prediction systems are based on abnormal animal behavior and historical data, and have limitations in providing information that takes into account the individual emotional state of users. This has led to problems such as prediction information causing unnecessary stress to users and a lack of motivation to take practical action. Therefore, there is a need to provide more accurate and user-adapted countermeasures.

[0353] 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.

[0354] In this invention, the server includes means for acquiring and analyzing animal biological information, means for acquiring and analyzing the user's emotional state and adjusting notification content, and means for providing the generated prediction information and adjusted notifications to the user. This makes it possible to provide earthquake prediction information that integrates abnormal animal behavior and the user's emotional state.

[0355] "Animal sensors" are devices attached to animals to acquire biological information.

[0356] "Biometric information" refers to data that represents the physiological state of an animal, such as its heart rate, body surface temperature, and movement patterns.

[0357] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and serves as an indicator for earthquake prediction.

[0358] "Earthquake prediction information" refers to information about the likelihood of an earthquake occurring, generated based on the analysis of abnormal behavior.

[0359] "User emotional state" refers to data that indicates the user's stress level and level of anxiety.

[0360] "Means for adjusting notification content" refers to processing means for changing the format and expression of information provided in accordance with the results of user sentiment analysis.

[0361] "Feedback" refers to the user's reactions and evaluations of a system.

[0362] "Means of improving analytical accuracy" refer to methods of improving the quality of system predictions and notifications by utilizing collected data and feedback.

[0363] This invention relates to a system that predicts earthquake occurrences based on abnormal animal behavior and provides information while also considering the user's emotional state. This system consists of a terminal, a server, an emotion engine, and a user component.

[0364] The device acquires biometric information such as heart rate, body surface temperature, and movement patterns in real time from sensors attached to the animal. This data is transmitted to a server using communication methods such as Bluetooth.

[0365] The server is a computing device that analyzes received biometric information using machine learning algorithms. It uses Python and other data analysis software to detect abnormal animal behavior. By comparing the results of this abnormal behavior analysis with past earthquake data, it generates earthquake prediction information.

[0366] Furthermore, the server collects the user's emotional state through an emotion engine. This information is obtained using smartphones or wearable devices. Based on the user's emotional state, the server can adjust notification content, providing the user with gentler messages or additional support information.

[0367] Users can receive earthquake prediction information through notifications and take appropriate evacuation and safety measures based on their situation. Users can also provide feedback to the system, which will be used to improve the accuracy of the system's analysis.

[0368] For example, if the system detects abnormal animal behavior and predicts a possible earthquake, and the emotion engine detects the user is in a high-stress state, it will add reassuring words to the notification message, such as "Please stay calm. Evacuating to a safe place is important," and provide a link to detailed evacuation information. In this way, the user can receive information to take more appropriate action.

[0369] An example of a prompt that optimizes user notifications using a generative AI model is, "How can earthquake prediction notifications be optimized based on the user's emotional state?" This prompt allows the system to formulate appropriate responses tailored to the individual user's situation.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The device acquires biometric information from sensors attached to the animal. Inputs include the animal's heart rate, body surface temperature, and movement patterns, and this data is monitored in real time. The data is acquired by the device via Bluetooth communication and then transmitted to a server. The output is a collection of biometric data from the sensors.

[0373] Step 2:

[0374] The server receives biometric information transmitted from the terminal and begins analysis. The input is biometric information obtained in real time. The server executes a machine learning algorithm using Python to perform pattern recognition to detect abnormal behavior. Specifically, the algorithm compares each dataset with known abnormal patterns. The output is the analysis result indicating whether or not abnormal behavior occurred.

[0375] Step 3:

[0376] The server compares the results of the abnormal behavior analysis with past earthquake data. The inputs are the analysis results and past earthquake data stored in the database. The server performs a statistical similarity analysis to assess the probability of an earthquake occurring. The algorithm then refines the prediction model based on similar past cases. The output is earthquake prediction information.

[0377] Step 4:

[0378] The server acquires the user's emotional state via an emotion engine. The input consists of user stress level and anxiety data collected from smartphones and wearable devices. The server analyzes this input data and performs specific actions to understand the user's emotional state. The output is the analysis result regarding the user's emotional state.

[0379] Step 5:

[0380] The server adjusts the notification content based on the acquired emotional state. The input consists of the user's emotional analysis results and earthquake prediction information. The server utilizes a generative AI model to generate prompt messages and optimize the message the user receives. Specific actions include, for example, including gentler messages or additional safety information. The output is the adjusted notification message.

[0381] Step 6:

[0382] The user receives notifications from the server via their device. The input is information provided as a coordinated notification message. The user reviews the message content and takes action to make specific decisions regarding earthquake preparations and evacuation. The output is the appropriate safety measures taken by the user.

[0383] Step 7:

[0384] Users provide feedback after receiving a notification. The input consists of the user's own reaction and evaluation of the notification's effectiveness. This user feedback is sent to the server, and specific actions are taken to improve future notifications. The output is feedback data that helps improve the accuracy of system analysis.

[0385] (Application Example 2)

[0386] 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."

[0387] In recent years, the importance of improving the accuracy of natural disaster predictions has increased. However, conventional prediction systems do not take into account the emotional state of users, and the information they provide may increase anxiety and stress. Furthermore, optimizing the customer experience in physical stores requires a flexible approach that combines disaster information with the emotional state of customers. Solving these challenges is essential.

[0388] 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.

[0389] In this invention, the server includes means for analyzing biometric information acquired from a detection device to detect abnormal behavior, a decision device for generating predictive information and providing information based on the user's emotional state, and means for adjusting the customer experience. This makes it possible to adjust predictive information about natural disasters according to the user's emotional state and optimize the customer experience in physical stores.

[0390] A "detection device" is a device attached to an animal to acquire biological information.

[0391] "Biometric information" refers to data such as an animal's heart rate, body surface temperature, and movement patterns.

[0392] "Abnormal behavior" refers to behaviors in animals that are different from the norm and may be a precursor to an earthquake.

[0393] "Predictive information" refers to information about predicting the occurrence of natural disasters, generated based on the results of an analysis of abnormal behavior.

[0394] A "decision-making device" refers to an analytical tool that analyzes the user's emotional state and provides appropriate information.

[0395] "Emotional state" refers to the user's psychological state, such as their stress level or anxiety level.

[0396] "Customer experience" refers to the customer's purchasing and service usage experience at physical stores.

[0397] "Information provision" refers to the act of conveying predictive information, reassuring messages, or advice to users.

[0398] The system for implementing the present invention consists of a detection device attached to an animal, a server, a user terminal, and a display device. The server acquires biological information such as the animal's heart rate, body surface temperature, and movement patterns, and analyzes it using a machine learning algorithm. Based on the analyzed data, the server detects abnormal behavior and generates predictive information by comparing it with past natural disaster data.

[0399] A key element of this system is the emotion engine, which analyzes the user's emotional state in real time. The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes this data to understand their current emotional state. The emotional state quantifies psychological factors such as stress and anxiety, and based on this, the server optimizes the information it conveys to the user. For example, it adjusts the tone and content of notifications regarding predicted natural disasters to match the user's emotional state. This allows users to receive information with greater peace of mind.

[0400] Furthermore, in physical stores, the system uses earthquake probability information and customer sentiment data to display personalized messages to customers. This provides a sense of security while encouraging safe behavior. For example, if a store is crowded and the system detects that a customer looks anxious, it will display a message such as, "This is the area where you can enjoy shopping with peace of mind."

[0401] An example of a prompt message to use when using a generative AI model is: "When the system detects that the user is feeling anxious, generate relaxing recommendations and reassuring messages." This allows the system to provide a more personalized user experience.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The terminal acquires biometric information such as heart rate, body surface temperature, and movement patterns from detection devices attached to animals. The input is biometric data from sensors, and by collecting this data in real time, it outputs it as biometric information.

[0405] Step 2:

[0406] The server applies machine learning algorithms based on biometric information received from the terminal to detect abnormal animal behavior. The input is biometric information, and by performing data analysis on this information, it outputs patterns of abnormal animal behavior.

[0407] Step 3:

[0408] The server generates earthquake prediction information by comparing detected abnormal behavior patterns with past natural disaster data. The input consists of abnormal behavior patterns and historical data, and the prediction information is obtained by matching and analyzing them.

[0409] Step 4:

[0410] The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state. The input is video and audio data, which is used for emotion analysis, and the emotional state is output.

[0411] Step 5:

[0412] The server considers the generated predictive information and the user's emotional state to provide optimized information to the user. The input consists of predictive information and emotional state; based on these, the server generates notification content and outputs it to the user.

[0413] Step 6:

[0414] In physical stores, display devices show personalized messages to customers based on notifications from the server. The input is the notified information, and accordingly, the system outputs a message designed to reassure the customer.

[0415] Step 7:

[0416] Users contribute to improving the accuracy of the emotion engine by providing feedback to the system after receiving a notification. The input is feedback information, and reflecting this leads to improvements in the system.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system that acquires biological information from animals and predicts earthquakes based on that information. The system mainly consists of three elements: a terminal, a server, and a user.

[0434] First, the device attaches sensors to the animal and acquires biometric information in real time. This information includes the animal's movement, heart rate, and body surface temperature, and plays a crucial role in detecting abnormal behavior. The device temporarily stores this data and sends it to the server at predetermined time intervals.

[0435] Next, the server receives data transmitted from the terminal and analyzes it using machine learning algorithms. If abnormal animal behavior is detected through this analysis, the pattern is evaluated and the probability of an earthquake occurring is calculated. During the analysis process, the data is also compared with past earthquake data to improve the accuracy of the prediction.

[0436] The prediction information generated by the server is immediately sent to the user's terminal. Based on this information, the user can take necessary measures. For example, if a large-scale earthquake is predicted, they can issue evacuation advisories to residents and relevant parties, or prepare an emergency response system. Furthermore, a feedback system can be introduced to verify the accuracy and practicality of the information, allowing users to incorporate their actual experiences.

[0437] As a specific example, data on abnormal behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. This allows farmers to ensure the safety of their livestock while simultaneously evacuating to a safe place themselves. In this way, the present invention provides an embodiment that prevents damage caused by earthquakes and improves people's safety.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The device acquires biometric information from sensors attached to the animal. Here, data such as heart rate, body temperature, and movement patterns are collected in real time and temporarily stored in memory.

[0441] Step 2:

[0442] The device periodically (e.g., every minute) sends collected biometric information to the server. The data is encrypted and packetized to ensure secure communication.

[0443] Step 3:

[0444] The server stores the received biometric information in a database. This allows for efficient use of the data in subsequent analysis processes.

[0445] Step 4:

[0446] The server uses machine learning algorithms to analyze the stored data. Specifically, it detects abnormal behavior patterns and compares them with past earthquake data to assess the likelihood of an earthquake occurring.

[0447] Step 5:

[0448] The server generates earthquake prediction information. The generated information includes details such as the predicted magnitude of the earthquake, the area where it is expected to occur, and the time of occurrence.

[0449] Step 6:

[0450] The server generates predictive information and sends it to the user's terminal. Because timeliness is crucial for notifications, a dedicated communication protocol is used to ensure real-time information delivery.

[0451] Step 7:

[0452] The user reviews the predictive information received via their device. Based on this information, the user takes measures such as evacuating to a safe place or disseminating information to relevant parties.

[0453] Step 8:

[0454] Users input feedback into their devices describing their actual experiences after an earthquake. This feedback is sent to a server and used to improve the accuracy of future analysis models.

[0455] (Example 1)

[0456] 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."

[0457] Current earthquake prediction technology suffers from insufficient accuracy, making it difficult to adequately ensure people's safety. Furthermore, conventional methods struggle with real-time information gathering and analysis, hindering responses in situations requiring rapid action. Additionally, techniques utilizing animal behavior to predict natural phenomena are not yet fully practical, and their potential needs to be explored.

[0458] 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.

[0459] In this invention, the server includes means for acquiring biological information from a measuring device attached to an animal and transmitting the collected data at regular intervals to a central processing unit using communication technology; means for detecting unusual animal behavior using data analysis technology in the central processing unit; and means for generating prediction information for the occurrence of natural phenomena based on the analysis results of the unusual behavior. This enables highly accurate earthquake prediction using animal behavior data, allowing for a rapid and appropriate response.

[0460] An "animal" is an organism belonging to a specific biological species from which biological information is to be acquired.

[0461] A "measuring device" is a device used to detect and acquire biological information from animals.

[0462] "Biometric information" refers to physiological data such as an animal's heart rate, movement, and body surface temperature.

[0463] "Communication technology" refers to the technical means for transferring acquired data to a central processing unit.

[0464] A "central processing unit" is a computer system that receives and analyzes transmitted biometric data.

[0465] "Data analysis technology" refers to techniques used to analyze biological information and identify unique behaviors in animals.

[0466] "Unusual behavior" refers to patterns of animal movement that are not normally observed and serves as an indicator of abnormality.

[0467] "Natural phenomena" refer to phenomena that are not caused by human activity, such as earthquakes.

[0468] "Occurrence prediction information" refers to information that indicates the likelihood of future natural phenomena based on analyzed data.

[0469] A "user terminal" is a device that receives generated prediction information and notifies the user.

[0470] This invention is a system that predicts natural phenomena based on the biological information of animals, and consists of three elements: a terminal, a server, and a user. Specific embodiments of each element and the overall system are shown below.

[0471] First, the device acquires biometric information via sensors attached to the animal. These sensors measure heart rate, movement, and body surface temperature in real time, allowing for highly accurate monitoring of the animal's physiological changes. The collected data is temporarily stored on the device and transmitted to a server at predetermined time intervals using wireless communication technology. Specifically, Wi-Fi and Bluetooth are commonly used.

[0472] Next, the server receives biometric information transmitted from the terminal. Based on the received data, it performs a process to detect unusual animal behavior using data analysis techniques. Here, machine learning algorithms are used for data analysis, comparing past earthquake data with current biometric information. This allows for a highly accurate determination of whether the unusual animal behavior is a precursor to a natural phenomenon. For this analysis, software tools such as TensorFlow or scikit-learn using Python are employed.

[0473] Users receive forecast information about natural phenomena transmitted from the server and check this information on their terminals. Based on the forecast information, users can take necessary measures. For example, if a large-scale natural phenomenon is predicted, users can take evacuation actions or prepare emergency response systems. In addition, users can contribute to improving the accuracy of the system by sending information based on their actual experiences using the feedback function.

[0474] As a concrete example, data on unusual behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. The introduction of this system will enable farmers to take steps to ensure their own safety and the safety of their livestock.

[0475] An example of a prompt message is, "Generate the next earthquake prediction based on abnormal cattle behavior data in rural areas." This allows the generating AI model to provide appropriate prediction information.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device continuously acquires biometric information such as heart rate, movement, and body surface temperature using sensors attached to the animal. The input biometric information is recorded in real time and subjected to initial filtering to remove outliers. This allows for the temporary storage of biometric information with less noise, improving the accuracy of the data.

[0479] Step 2:

[0480] The device packets temporarily stored biometric information at regular intervals and sends it to a server using communication technologies such as Wi-Fi or Bluetooth. The input is temporarily stored biometric information, which is then packetized according to a specific protocol. The output is the transmission of data packets to the server.

[0481] Step 3:

[0482] The server receives data packets sent from the terminal and stores them in the database. During this process, the server verifies the consistency and reliability of the data, and any inconsistent data is excluded. The received data is then ready for analysis and data processing.

[0483] Step 4:

[0484] The server inputs received biometric information into a machine learning algorithm to detect unique animal behaviors. Specifically, it uses models trained with TensorFlow or scikit-learn in Python to perform data analysis. The output is the detection result of unique behaviors and their probability scores.

[0485] Step 5:

[0486] Based on the detection results, the server calculates the probability of natural phenomena occurring by comparing them with a database of past earthquakes. This process uses statistical methods to compare the abnormal behavior patterns with past cases that are similar. Finally, prediction information about the occurrence of natural phenomena is generated.

[0487] Step 6:

[0488] The server sends the generated prediction information to the user's terminal. This information includes details of the prediction and recommended countermeasures. This allows the user to take the necessary actions based on the received information.

[0489] Step 7:

[0490] Users review the received prediction information and take appropriate action. They also use the feedback system to send information about the actual situation and the accuracy of the predictions back to the server. The server uses this feedback to further improve the accuracy of the machine learning model.

[0491] (Application Example 1)

[0492] 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."

[0493] Conventional earthquake prediction systems have the challenge of not being able to respond quickly to human casualties and ensure the safety of animals. This invention aims to improve the safety of animals and people by accurately identifying earthquake precursors using animal biological information.

[0494] 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.

[0495] In this invention, the server includes a device for acquiring biological information from animals, a device for analyzing the acquired biological information to identify abnormal behavior in animals, and a device for generating earthquake prediction information based on the analysis results of the abnormal behavior. This enables rapid measures to ensure the safety of animals and people.

[0496] "Animals" refers to animals whose behavior is monitored using biosensors.

[0497] "Biometric information" refers to data that indicates an animal's health status and behavior, including animal movement, heart rate, and body surface temperature.

[0498] "Device" refers to equipment or systems used to acquire, analyze, and transmit biological information from animals.

[0499] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and is considered a precursor to natural phenomena such as earthquakes.

[0500] "Predictive information" refers to information about the likelihood of future earthquakes, generated by analyzing acquired data.

[0501] "Users" refers to individuals or organizations that receive predictive information and take action for the safety of animals and people.

[0502] "Artificial intelligence" refers to a technology that uses machine learning algorithms to analyze the biological information of animals and identify abnormal behavior.

[0503] "Earthquake data" refers to past earthquake information and related data from the time of their occurrence, and is used as the basis for prediction algorithms.

[0504] The system for realizing this invention consists of a terminal that uses sensors to acquire biological information from animals, a server that analyzes that data, and a user terminal that receives the analysis results.

[0505] The terminal is connected to sensors attached to the animals, and acquires biometric information such as the animals' movements, heart rate, and body surface temperature in real time. This data is temporarily stored on the terminal and then transmitted to the server at predetermined time intervals.

[0506] The server is built on Python and uses artificial intelligence technology to analyze animal biometric information. Specifically, it uses machine learning libraries such as TensorFlow to identify patterns of abnormal behavior and evaluate the likelihood of an earthquake based on the results. During the analysis process, Firebase is used to manage data in real time and compare it with historical earthquake data to improve the accuracy of predictions.

[0507] The server sends push notifications to user terminals with earthquake prediction information as analysis results. Upon receiving this information, users can take swift action according to the situation. For example, a zoo manager can receive the notification and immediately implement an evacuation plan to ensure the safety of animals and visitors.

[0508] Furthermore, this system allows users to provide feedback on their actual experiences, which can then be used to continuously improve the predictive model through retraining.

[0509] Example of a prompt

[0510] Prompts for generative AI models:

[0511] "Please propose a method to improve the algorithm that analyzes earthquake precursors from abnormal animal behavior data collected by sensors. The server will use TensorFlow."

[0512] In this way, the present invention provides an effective means of predicting earthquakes by utilizing abnormal animal behavior and ensuring the safety of animals and people.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The device acquires biometric information in real time from sensors attached to animals. Inputs include data such as animal movement, heart rate, and body surface temperature. This data is temporarily stored on the device. The output is a temporary dataset for later transmission to a server.

[0516] Step 2:

[0517] Data is sent to the server. The terminal uploads data stored on the server at predetermined time intervals. This input data is stored in the server's real-time database and is ready for analysis. The output is formatted data for analysis.

[0518] Step 3:

[0519] The server uses TensorFlow to run a machine learning model and identify abnormal animal behavior from input data. The input data is compared with historical data of normal behavior to detect abnormal behavior patterns. The output is a flag indicating whether or not abnormal behavior was detected, along with detailed information about it.

[0520] Step 4:

[0521] The server generates earthquake prediction information based on the analysis of abnormal behavior. The probability of an earthquake occurring is evaluated by comparing it with past earthquake data. The output is data that quantifies the specific probability of an earthquake occurring, as earthquake prediction information.

[0522] Step 5:

[0523] The server sends the generated prediction information to the user's terminal. The user's terminal receives this information and displays an alert notification. The input is prediction data, and the output is a presentation of earthquake prediction information in a format understandable to the user.

[0524] Step 6:

[0525] Based on the information provided, the user considers and implements safety measures. Specifically, in the zoo example, an evacuation plan is created and a rapid response is initiated. The input is the presented predictive information, and the output is the implementation of specific response actions.

[0526] 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.

[0527] This invention combines an earthquake prediction system that utilizes abnormal animal behavior with an emotion engine that recognizes the user's emotional state to provide more appropriate information. The system mainly consists of a terminal, a server, an emotion engine, and a user.

[0528] First, the device acquires biometric information from sensors attached to the animal. This information includes the animal's heart rate, body surface temperature, and movement patterns, and is monitored in real time to serve as basic data for detecting abnormal behavior. The acquired data is sent to a server, where it is analyzed using machine learning algorithms.

[0529] The server detects abnormal behavior from the analysis results, evaluates its similarity to past earthquake data, and generates earthquake prediction information. In addition, it is equipped with an emotion engine that acquires emotional information through the user's terminal. The emotion engine analyzes the user's stress level and anxiety and adjusts the notification method accordingly. For example, for users in a high-stress state, notifications can be made gentler or additional support information can be provided to prevent excessive anxiety.

[0530] Through these notifications, users can receive earthquake prediction information and take appropriate evacuation and safety measures. Furthermore, by using the feedback function, users can input their own psychological experiences into the system, improving the accuracy of the emotion engine's analysis.

[0531] A concrete example is that, when the emotion engine is active and a large-scale earthquake is predicted, if the system determines that the user is in a higher-than-usual stress state, reassuring words may be added to the notification message. In this way, the present invention provides an embodiment that enables the provision of more effective and personalized earthquake prediction information by using animal biometric information and user emotional information in combination.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The device acquires biometric information from sensors attached to the animal. Specifically, it acquires the animal's heart rate, body temperature, and movement patterns in real time and temporarily stores them in memory.

[0535] Step 2:

[0536] The device periodically (for example, every minute) sends collected biometric information to the server. The data is encrypted and transmitted over the network in a secure manner.

[0537] Step 3:

[0538] The server stores the received biometric information in a database. During storage, it verifies the consistency of the time series and converts the data to a format suitable for analysis.

[0539] Step 4:

[0540] The server uses stored biometric data and employs machine learning algorithms to perform analysis. This allows it to detect abnormal animal behavior patterns and assess the likelihood of an earthquake occurring.

[0541] Step 5:

[0542] The server utilizes an emotion engine to analyze emotional information obtained from the user's device. It evaluates the user's stress level and anxiety state, and determines the optimal method of providing information.

[0543] Step 6:

[0544] The server generates earthquake prediction information and sends it to the user. Based on the sentiment analysis results, the content and wording of the notification message are individually customized. For example, a reassuring message is added for users with high stress levels.

[0545] Step 7:

[0546] The user reviews the received earthquake prediction information. If necessary, they initiate safety measures or evacuation actions. Furthermore, providing feedback on the received information can contribute to improving the system's accuracy.

[0547] Step 8:

[0548] User feedback is sent to the server and used to improve the emotion engine and machine learning models. This allows the system to continuously improve the accuracy of its predictions and the quality of its user interactions.

[0549] (Example 2)

[0550] 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."

[0551] Conventional earthquake prediction systems are based on abnormal animal behavior and historical data, and have limitations in providing information that takes into account the individual emotional state of users. This has led to problems such as prediction information causing unnecessary stress to users and a lack of motivation to take practical action. Therefore, there is a need to provide more accurate and user-adapted countermeasures.

[0552] 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.

[0553] In this invention, the server includes means for acquiring and analyzing animal biological information, means for acquiring and analyzing the user's emotional state and adjusting notification content, and means for providing the generated prediction information and adjusted notifications to the user. This makes it possible to provide earthquake prediction information that integrates abnormal animal behavior and the user's emotional state.

[0554] "Animal sensors" are devices attached to animals to acquire biological information.

[0555] "Biometric information" refers to data that represents the physiological state of an animal, such as its heart rate, body surface temperature, and movement patterns.

[0556] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and serves as an indicator for earthquake prediction.

[0557] "Earthquake prediction information" refers to information about the likelihood of an earthquake occurring, generated based on the analysis of abnormal behavior.

[0558] "User emotional state" refers to data that indicates the user's stress level and level of anxiety.

[0559] "Means for adjusting notification content" refers to processing means for changing the format and expression of information provided in accordance with the results of user sentiment analysis.

[0560] "Feedback" refers to the user's reactions and evaluations of a system.

[0561] "Means of improving analytical accuracy" refer to methods of improving the quality of system predictions and notifications by utilizing collected data and feedback.

[0562] This invention relates to a system that predicts earthquake occurrences based on abnormal animal behavior and provides information while also considering the user's emotional state. This system consists of a terminal, a server, an emotion engine, and a user component.

[0563] The device acquires biometric information such as heart rate, body surface temperature, and movement patterns in real time from sensors attached to the animal. This data is transmitted to a server using communication methods such as Bluetooth.

[0564] The server is a computing device that analyzes received biometric information using machine learning algorithms. It uses Python and other data analysis software to detect abnormal animal behavior. By comparing the results of this abnormal behavior analysis with past earthquake data, it generates earthquake prediction information.

[0565] Furthermore, the server collects the user's emotional state through an emotion engine. This information is obtained using smartphones or wearable devices. Based on the user's emotional state, the server can adjust notification content, providing the user with gentler messages or additional support information.

[0566] Users can receive earthquake prediction information through notifications and take appropriate evacuation and safety measures based on their situation. Users can also provide feedback to the system, which will be used to improve the accuracy of the system's analysis.

[0567] For example, if the system detects abnormal animal behavior and predicts a possible earthquake, and the emotion engine detects the user is in a high-stress state, it will add reassuring words to the notification message, such as "Please stay calm. Evacuating to a safe place is important," and provide a link to detailed evacuation information. In this way, the user can receive information to take more appropriate action.

[0568] An example of a prompt that optimizes user notifications using a generative AI model is, "How can earthquake prediction notifications be optimized based on the user's emotional state?" This prompt allows the system to formulate appropriate responses tailored to the individual user's situation.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The device acquires biometric information from sensors attached to the animal. Inputs include the animal's heart rate, body surface temperature, and movement patterns, and this data is monitored in real time. The data is acquired by the device via Bluetooth communication and then transmitted to a server. The output is a collection of biometric data from the sensors.

[0572] Step 2:

[0573] The server receives biometric information transmitted from the terminal and begins analysis. The input is biometric information obtained in real time. The server executes a machine learning algorithm using Python to perform pattern recognition to detect abnormal behavior. Specifically, the algorithm compares each dataset with known abnormal patterns. The output is the analysis result indicating whether or not abnormal behavior occurred.

[0574] Step 3:

[0575] The server compares the results of the abnormal behavior analysis with past earthquake data. The inputs are the analysis results and past earthquake data stored in the database. The server performs a statistical similarity analysis to assess the probability of an earthquake occurring. The algorithm then refines the prediction model based on similar past cases. The output is earthquake prediction information.

[0576] Step 4:

[0577] The server acquires the user's emotional state via an emotion engine. The input consists of user stress level and anxiety data collected from smartphones and wearable devices. The server analyzes this input data and performs specific actions to understand the user's emotional state. The output is the analysis result regarding the user's emotional state.

[0578] Step 5:

[0579] The server adjusts the notification content based on the acquired emotional state. The input consists of the user's emotional analysis results and earthquake prediction information. The server utilizes a generative AI model to generate prompt messages and optimize the message the user receives. Specific actions include, for example, including gentler messages or additional safety information. The output is the adjusted notification message.

[0580] Step 6:

[0581] The user receives notifications from the server via their device. The input is information provided as a coordinated notification message. The user reviews the message content and takes action to make specific decisions regarding earthquake preparations and evacuation. The output is the appropriate safety measures taken by the user.

[0582] Step 7:

[0583] Users provide feedback after receiving a notification. The input consists of the user's own reaction and evaluation of the notification's effectiveness. This user feedback is sent to the server, and specific actions are taken to improve future notifications. The output is feedback data that helps improve the accuracy of system analysis.

[0584] (Application Example 2)

[0585] 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."

[0586] In recent years, the importance of improving the accuracy of natural disaster predictions has increased. However, conventional prediction systems do not take into account the emotional state of users, and the information they provide may increase anxiety and stress. Furthermore, optimizing the customer experience in physical stores requires a flexible approach that combines disaster information with the emotional state of customers. Solving these challenges is essential.

[0587] 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.

[0588] In this invention, the server includes means for analyzing biometric information acquired from a detection device to detect abnormal behavior, a decision device for generating predictive information and providing information based on the user's emotional state, and means for adjusting the customer experience. This makes it possible to adjust predictive information about natural disasters according to the user's emotional state and optimize the customer experience in physical stores.

[0589] A "detection device" is a device attached to an animal to acquire biological information.

[0590] "Biometric information" refers to data such as an animal's heart rate, body surface temperature, and movement patterns.

[0591] "Abnormal behavior" refers to behaviors in animals that are different from the norm and may be a precursor to an earthquake.

[0592] "Predictive information" refers to information about predicting the occurrence of natural disasters, generated based on the results of an analysis of abnormal behavior.

[0593] A "decision-making device" refers to an analytical tool that analyzes the user's emotional state and provides appropriate information.

[0594] "Emotional state" refers to the user's psychological state, such as their stress level or anxiety level.

[0595] "Customer experience" refers to the customer's purchasing and service usage experience at physical stores.

[0596] "Information provision" refers to the act of conveying predictive information, reassuring messages, or advice to users.

[0597] The system for implementing the present invention consists of a detection device attached to an animal, a server, a user terminal, and a display device. The server acquires biological information such as the animal's heart rate, body surface temperature, and movement patterns, and analyzes it using a machine learning algorithm. Based on the analyzed data, the server detects abnormal behavior and generates predictive information by comparing it with past natural disaster data.

[0598] A key element of this system is the emotion engine, which analyzes the user's emotional state in real time. The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes this data to understand their current emotional state. The emotional state quantifies psychological factors such as stress and anxiety, and based on this, the server optimizes the information it conveys to the user. For example, it adjusts the tone and content of notifications regarding predicted natural disasters to match the user's emotional state. This allows users to receive information with greater peace of mind.

[0599] Furthermore, in physical stores, the system uses earthquake probability information and customer sentiment data to display personalized messages to customers. This provides a sense of security while encouraging safe behavior. For example, if a store is crowded and the system detects that a customer looks anxious, it will display a message such as, "This is the area where you can enjoy shopping with peace of mind."

[0600] An example of a prompt message to use when using a generative AI model is: "When the system detects that the user is feeling anxious, generate relaxing recommendations and reassuring messages." This allows the system to provide a more personalized user experience.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The terminal acquires biometric information such as heart rate, body surface temperature, and movement patterns from detection devices attached to animals. The input is biometric data from sensors, and by collecting this data in real time, it outputs it as biometric information.

[0604] Step 2:

[0605] The server applies machine learning algorithms based on biometric information received from the terminal to detect abnormal animal behavior. The input is biometric information, and by performing data analysis on this information, it outputs patterns of abnormal animal behavior.

[0606] Step 3:

[0607] The server generates earthquake prediction information by comparing detected abnormal behavior patterns with past natural disaster data. The input consists of abnormal behavior patterns and historical data, and the prediction information is obtained by matching and analyzing them.

[0608] Step 4:

[0609] The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state. The input is video and audio data, which is used for emotion analysis, and the emotional state is output.

[0610] Step 5:

[0611] The server considers the generated predictive information and the user's emotional state to provide optimized information to the user. The input consists of predictive information and emotional state; based on these, the server generates notification content and outputs it to the user.

[0612] Step 6:

[0613] In physical stores, display devices show personalized messages to customers based on notifications from the server. The input is the notified information, and accordingly, the system outputs a message designed to reassure the customer.

[0614] Step 7:

[0615] Users contribute to improving the accuracy of the emotion engine by providing feedback to the system after receiving a notification. The input is feedback information, and reflecting this leads to improvements in the system.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] [Fourth Embodiment]

[0620] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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".

[0633] This invention is a system that acquires biological information from animals and predicts earthquakes based on that information. The system mainly consists of three elements: a terminal, a server, and a user.

[0634] First, the device attaches sensors to the animal and acquires biometric information in real time. This information includes the animal's movement, heart rate, and body surface temperature, and plays a crucial role in detecting abnormal behavior. The device temporarily stores this data and sends it to the server at predetermined time intervals.

[0635] Next, the server receives data transmitted from the terminal and analyzes it using machine learning algorithms. If abnormal animal behavior is detected through this analysis, the pattern is evaluated and the probability of an earthquake occurring is calculated. During the analysis process, the data is also compared with past earthquake data to improve the accuracy of the prediction.

[0636] The prediction information generated by the server is immediately sent to the user's terminal. Based on this information, the user can take necessary measures. For example, if a large-scale earthquake is predicted, they can issue evacuation advisories to residents and relevant parties, or prepare an emergency response system. Furthermore, a feedback system can be introduced to verify the accuracy and practicality of the information, allowing users to incorporate their actual experiences.

[0637] As a specific example, data on abnormal behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. This allows farmers to ensure the safety of their livestock while simultaneously evacuating to a safe place themselves. In this way, the present invention provides an embodiment that prevents damage caused by earthquakes and improves people's safety.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The device acquires biometric information from sensors attached to the animal. Here, data such as heart rate, body temperature, and movement patterns are collected in real time and temporarily stored in memory.

[0641] Step 2:

[0642] The device periodically (e.g., every minute) sends collected biometric information to the server. The data is encrypted and packetized to ensure secure communication.

[0643] Step 3:

[0644] The server stores the received biometric information in a database. This allows for efficient use of the data in subsequent analysis processes.

[0645] Step 4:

[0646] The server uses machine learning algorithms to analyze the stored data. Specifically, it detects abnormal behavior patterns and compares them with past earthquake data to assess the likelihood of an earthquake occurring.

[0647] Step 5:

[0648] The server generates earthquake prediction information. The generated information includes details such as the predicted magnitude of the earthquake, the area where it is expected to occur, and the time of occurrence.

[0649] Step 6:

[0650] The server generates predictive information and sends it to the user's terminal. Because timeliness is crucial for notifications, a dedicated communication protocol is used to ensure real-time information delivery.

[0651] Step 7:

[0652] The user reviews the predictive information received via their device. Based on this information, the user takes measures such as evacuating to a safe place or disseminating information to relevant parties.

[0653] Step 8:

[0654] Users input feedback into their devices describing their actual experiences after an earthquake. This feedback is sent to a server and used to improve the accuracy of future analysis models.

[0655] (Example 1)

[0656] 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".

[0657] Current earthquake prediction technology suffers from insufficient accuracy, making it difficult to adequately ensure people's safety. Furthermore, conventional methods struggle with real-time information gathering and analysis, hindering responses in situations requiring rapid action. Additionally, techniques utilizing animal behavior to predict natural phenomena are not yet fully practical, and their potential needs to be explored.

[0658] 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.

[0659] In this invention, the server includes means for acquiring biological information from a measuring device attached to an animal and transmitting the collected data at regular intervals to a central processing unit using communication technology; means for detecting unusual animal behavior using data analysis technology in the central processing unit; and means for generating prediction information for the occurrence of natural phenomena based on the analysis results of the unusual behavior. This enables highly accurate earthquake prediction using animal behavior data, allowing for a rapid and appropriate response.

[0660] An "animal" is an organism belonging to a specific biological species from which biological information is to be acquired.

[0661] A "measuring device" is a device used to detect and acquire biological information from animals.

[0662] "Biometric information" refers to physiological data such as an animal's heart rate, movement, and body surface temperature.

[0663] "Communication technology" refers to the technical means for transferring acquired data to a central processing unit.

[0664] A "central processing unit" is a computer system that receives and analyzes transmitted biometric data.

[0665] "Data analysis technology" refers to techniques used to analyze biological information and identify unique behaviors in animals.

[0666] "Unusual behavior" refers to patterns of animal movement that are not normally observed and serves as an indicator of abnormality.

[0667] "Natural phenomena" refer to phenomena that are not caused by human activity, such as earthquakes.

[0668] "Occurrence prediction information" refers to information that indicates the likelihood of future natural phenomena based on analyzed data.

[0669] A "user terminal" is a device that receives generated prediction information and notifies the user.

[0670] This invention is a system that predicts natural phenomena based on the biological information of animals, and consists of three elements: a terminal, a server, and a user. Specific embodiments of each element and the overall system are shown below.

[0671] First, the device acquires biometric information via sensors attached to the animal. These sensors measure heart rate, movement, and body surface temperature in real time, allowing for highly accurate monitoring of the animal's physiological changes. The collected data is temporarily stored on the device and transmitted to a server at predetermined time intervals using wireless communication technology. Specifically, Wi-Fi and Bluetooth are commonly used.

[0672] Next, the server receives biometric information transmitted from the terminal. Based on the received data, it performs a process to detect unusual animal behavior using data analysis techniques. Here, machine learning algorithms are used for data analysis, comparing past earthquake data with current biometric information. This allows for a highly accurate determination of whether the unusual animal behavior is a precursor to a natural phenomenon. For this analysis, software tools such as TensorFlow or scikit-learn using Python are employed.

[0673] Users receive forecast information about natural phenomena transmitted from the server and check this information on their terminals. Based on the forecast information, users can take necessary measures. For example, if a large-scale natural phenomenon is predicted, users can take evacuation actions or prepare emergency response systems. In addition, users can contribute to improving the accuracy of the system by sending information based on their actual experiences using the feedback function.

[0674] As a concrete example, data on unusual behavior of cattle in rural areas is sent to a server, and if analysis determines that it is a precursor to an earthquake, the user is promptly notified. The introduction of this system will enable farmers to take steps to ensure their own safety and the safety of their livestock.

[0675] An example of a prompt message is, "Generate the next earthquake prediction based on abnormal cattle behavior data in rural areas." This allows the generating AI model to provide appropriate prediction information.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The device continuously acquires biometric information such as heart rate, movement, and body surface temperature using sensors attached to the animal. The input biometric information is recorded in real time and subjected to initial filtering to remove outliers. This allows for the temporary storage of biometric information with less noise, improving the accuracy of the data.

[0679] Step 2:

[0680] The device packets temporarily stored biometric information at regular intervals and sends it to a server using communication technologies such as Wi-Fi or Bluetooth. The input is temporarily stored biometric information, which is then packetized according to a specific protocol. The output is the transmission of data packets to the server.

[0681] Step 3:

[0682] The server receives data packets sent from the terminal and stores them in the database. During this process, the server verifies the consistency and reliability of the data, and any inconsistent data is excluded. The received data is then ready for analysis and data processing.

[0683] Step 4:

[0684] The server inputs received biometric information into a machine learning algorithm to detect unique animal behaviors. Specifically, it uses models trained with TensorFlow or scikit-learn in Python to perform data analysis. The output is the detection result of unique behaviors and their probability scores.

[0685] Step 5:

[0686] Based on the detection results, the server calculates the probability of natural phenomena occurring by comparing them with a database of past earthquakes. This process uses statistical methods to compare the abnormal behavior patterns with past cases that are similar. Finally, prediction information about the occurrence of natural phenomena is generated.

[0687] Step 6:

[0688] The server sends the generated prediction information to the user's terminal. This information includes details of the prediction and recommended countermeasures. This allows the user to take the necessary actions based on the received information.

[0689] Step 7:

[0690] Users review the received prediction information and take appropriate action. They also use the feedback system to send information about the actual situation and the accuracy of the predictions back to the server. The server uses this feedback to further improve the accuracy of the machine learning model.

[0691] (Application Example 1)

[0692] 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".

[0693] Conventional earthquake prediction systems have the challenge of not being able to respond quickly to human casualties and ensure the safety of animals. This invention aims to improve the safety of animals and people by accurately identifying earthquake precursors using animal biological information.

[0694] 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.

[0695] In this invention, the server includes a device for acquiring biological information from animals, a device for analyzing the acquired biological information to identify abnormal behavior in animals, and a device for generating earthquake prediction information based on the analysis results of the abnormal behavior. This enables rapid measures to ensure the safety of animals and people.

[0696] "Animals" refers to animals whose behavior is monitored using biosensors.

[0697] "Biometric information" refers to data that indicates an animal's health status and behavior, including animal movement, heart rate, and body surface temperature.

[0698] "Device" refers to equipment or systems used to acquire, analyze, and transmit biological information from animals.

[0699] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and is considered a precursor to natural phenomena such as earthquakes.

[0700] "Predictive information" refers to information about the likelihood of future earthquakes, generated by analyzing acquired data.

[0701] "Users" refers to individuals or organizations that receive predictive information and take action for the safety of animals and people.

[0702] "Artificial intelligence" refers to a technology that uses machine learning algorithms to analyze the biological information of animals and identify abnormal behavior.

[0703] "Earthquake data" refers to past earthquake information and related data from the time of their occurrence, and is used as the basis for prediction algorithms.

[0704] The system for realizing this invention consists of a terminal that uses sensors to acquire biological information from animals, a server that analyzes that data, and a user terminal that receives the analysis results.

[0705] The terminal is connected to sensors attached to the animals, and acquires biometric information such as the animals' movements, heart rate, and body surface temperature in real time. This data is temporarily stored on the terminal and then transmitted to the server at predetermined time intervals.

[0706] The server is built on Python and uses artificial intelligence technology to analyze animal biometric information. Specifically, it uses machine learning libraries such as TensorFlow to identify patterns of abnormal behavior and evaluate the likelihood of an earthquake based on the results. During the analysis process, Firebase is used to manage data in real time and compare it with historical earthquake data to improve the accuracy of predictions.

[0707] The server sends push notifications to user terminals with earthquake prediction information as analysis results. Upon receiving this information, users can take swift action according to the situation. For example, a zoo manager can receive the notification and immediately implement an evacuation plan to ensure the safety of animals and visitors.

[0708] Furthermore, this system allows users to provide feedback on their actual experiences, which can then be used to continuously improve the predictive model through retraining.

[0709] Example of a prompt

[0710] Prompts for generative AI models:

[0711] "Please propose a method to improve the algorithm that analyzes earthquake precursors from abnormal animal behavior data collected by sensors. The server will use TensorFlow."

[0712] In this way, the present invention provides an effective means of predicting earthquakes by utilizing abnormal animal behavior and ensuring the safety of animals and people.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The device acquires biometric information in real time from sensors attached to animals. Inputs include data such as animal movement, heart rate, and body surface temperature. This data is temporarily stored on the device. The output is a temporary dataset for later transmission to a server.

[0716] Step 2:

[0717] Data is sent to the server. The terminal uploads data stored on the server at predetermined time intervals. This input data is stored in the server's real-time database and is ready for analysis. The output is formatted data for analysis.

[0718] Step 3:

[0719] The server uses TensorFlow to run a machine learning model and identify abnormal animal behavior from input data. The input data is compared with historical data of normal behavior to detect abnormal behavior patterns. The output is a flag indicating whether or not abnormal behavior was detected, along with detailed information about it.

[0720] Step 4:

[0721] The server generates earthquake prediction information based on the analysis of abnormal behavior. The probability of an earthquake occurring is evaluated by comparing it with past earthquake data. The output is data that quantifies the specific probability of an earthquake occurring, as earthquake prediction information.

[0722] Step 5:

[0723] The server sends the generated prediction information to the user's terminal. The user's terminal receives this information and displays an alert notification. The input is prediction data, and the output is a presentation of earthquake prediction information in a format understandable to the user.

[0724] Step 6:

[0725] Based on the information provided, the user considers and implements safety measures. Specifically, in the zoo example, an evacuation plan is created and a rapid response is initiated. The input is the presented predictive information, and the output is the implementation of specific response actions.

[0726] 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.

[0727] This invention combines an earthquake prediction system that utilizes abnormal animal behavior with an emotion engine that recognizes the user's emotional state to provide more appropriate information. The system mainly consists of a terminal, a server, an emotion engine, and a user.

[0728] First, the device acquires biometric information from sensors attached to the animal. This information includes the animal's heart rate, body surface temperature, and movement patterns, and is monitored in real time to serve as basic data for detecting abnormal behavior. The acquired data is sent to a server, where it is analyzed using machine learning algorithms.

[0729] The server detects abnormal behavior from the analysis results, evaluates its similarity to past earthquake data, and generates earthquake prediction information. In addition, it is equipped with an emotion engine that acquires emotional information through the user's terminal. The emotion engine analyzes the user's stress level and anxiety and adjusts the notification method accordingly. For example, for users in a high-stress state, notifications can be made gentler or additional support information can be provided to prevent excessive anxiety.

[0730] Through these notifications, users can receive earthquake prediction information and take appropriate evacuation and safety measures. Furthermore, by using the feedback function, users can input their own psychological experiences into the system, improving the accuracy of the emotion engine's analysis.

[0731] A concrete example is that, when the emotion engine is active and a large-scale earthquake is predicted, if the system determines that the user is in a higher-than-usual stress state, reassuring words may be added to the notification message. In this way, the present invention provides an embodiment that enables the provision of more effective and personalized earthquake prediction information by using animal biometric information and user emotional information in combination.

[0732] The following describes the processing flow.

[0733] Step 1:

[0734] The device acquires biometric information from sensors attached to the animal. Specifically, it acquires the animal's heart rate, body temperature, and movement patterns in real time and temporarily stores them in memory.

[0735] Step 2:

[0736] The device periodically (for example, every minute) sends collected biometric information to the server. The data is encrypted and transmitted over the network in a secure manner.

[0737] Step 3:

[0738] The server stores the received biometric information in a database. During storage, it verifies the consistency of the time series and converts the data to a format suitable for analysis.

[0739] Step 4:

[0740] The server uses stored biometric data and employs machine learning algorithms to perform analysis. This allows it to detect abnormal animal behavior patterns and assess the likelihood of an earthquake occurring.

[0741] Step 5:

[0742] The server utilizes an emotion engine to analyze emotional information obtained from the user's device. It evaluates the user's stress level and anxiety state, and determines the optimal method of providing information.

[0743] Step 6:

[0744] The server generates earthquake prediction information and sends it to the user. Based on the sentiment analysis results, the content and wording of the notification message are individually customized. For example, a reassuring message is added for users with high stress levels.

[0745] Step 7:

[0746] The user reviews the received earthquake prediction information. If necessary, they initiate safety measures or evacuation actions. Furthermore, providing feedback on the received information can contribute to improving the system's accuracy.

[0747] Step 8:

[0748] User feedback is sent to the server and used to improve the emotion engine and machine learning models. This allows the system to continuously improve the accuracy of its predictions and the quality of its user interactions.

[0749] (Example 2)

[0750] 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".

[0751] Conventional earthquake prediction systems are based on abnormal animal behavior and historical data, and have limitations in providing information that takes into account the individual emotional state of users. This has led to problems such as prediction information causing unnecessary stress to users and a lack of motivation to take practical action. Therefore, there is a need to provide more accurate and user-adapted countermeasures.

[0752] 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.

[0753] In this invention, the server includes means for acquiring and analyzing animal biological information, means for acquiring and analyzing the user's emotional state and adjusting notification content, and means for providing the generated prediction information and adjusted notifications to the user. This makes it possible to provide earthquake prediction information that integrates abnormal animal behavior and the user's emotional state.

[0754] "Animal sensors" are devices attached to animals to acquire biological information.

[0755] "Biometric information" refers to data that represents the physiological state of an animal, such as its heart rate, body surface temperature, and movement patterns.

[0756] "Abnormal behavior" refers to animal behavior that deviates from normal behavioral patterns and serves as an indicator for earthquake prediction.

[0757] "Earthquake prediction information" refers to information about the likelihood of an earthquake occurring, generated based on the analysis of abnormal behavior.

[0758] "User emotional state" refers to data that indicates the user's stress level and level of anxiety.

[0759] "Means for adjusting notification content" refers to processing means for changing the format and expression of information provided in accordance with the results of user sentiment analysis.

[0760] "Feedback" refers to the user's reactions and evaluations of a system.

[0761] "Means of improving analytical accuracy" refer to methods of improving the quality of system predictions and notifications by utilizing collected data and feedback.

[0762] This invention relates to a system that predicts earthquake occurrences based on abnormal animal behavior and provides information while also considering the user's emotional state. This system consists of a terminal, a server, an emotion engine, and a user component.

[0763] The device acquires biometric information such as heart rate, body surface temperature, and movement patterns in real time from sensors attached to the animal. This data is transmitted to a server using communication methods such as Bluetooth.

[0764] The server is a computing device that analyzes received biometric information using machine learning algorithms. It uses Python and other data analysis software to detect abnormal animal behavior. By comparing the results of this abnormal behavior analysis with past earthquake data, it generates earthquake prediction information.

[0765] Furthermore, the server collects the user's emotional state through an emotion engine. This information is obtained using smartphones or wearable devices. Based on the user's emotional state, the server can adjust notification content, providing the user with gentler messages or additional support information.

[0766] Users can receive earthquake prediction information through notifications and take appropriate evacuation and safety measures based on their situation. Users can also provide feedback to the system, which will be used to improve the accuracy of the system's analysis.

[0767] For example, if the system detects abnormal animal behavior and predicts a possible earthquake, and the emotion engine detects the user is in a high-stress state, it will add reassuring words to the notification message, such as "Please stay calm. Evacuating to a safe place is important," and provide a link to detailed evacuation information. In this way, the user can receive information to take more appropriate action.

[0768] An example of a prompt that optimizes user notifications using a generative AI model is, "How can earthquake prediction notifications be optimized based on the user's emotional state?" This prompt allows the system to formulate appropriate responses tailored to the individual user's situation.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The device acquires biometric information from sensors attached to the animal. Inputs include the animal's heart rate, body surface temperature, and movement patterns, and this data is monitored in real time. The data is acquired by the device via Bluetooth communication and then transmitted to a server. The output is a collection of biometric data from the sensors.

[0772] Step 2:

[0773] The server receives biometric information transmitted from the terminal and begins analysis. The input is biometric information obtained in real time. The server executes a machine learning algorithm using Python to perform pattern recognition to detect abnormal behavior. Specifically, the algorithm compares each dataset with known abnormal patterns. The output is the analysis result indicating whether or not abnormal behavior occurred.

[0774] Step 3:

[0775] The server compares the results of the abnormal behavior analysis with past earthquake data. The inputs are the analysis results and past earthquake data stored in the database. The server performs a statistical similarity analysis to assess the probability of an earthquake occurring. The algorithm then refines the prediction model based on similar past cases. The output is earthquake prediction information.

[0776] Step 4:

[0777] The server acquires the user's emotional state via an emotion engine. The input consists of user stress level and anxiety data collected from smartphones and wearable devices. The server analyzes this input data and performs specific actions to understand the user's emotional state. The output is the analysis result regarding the user's emotional state.

[0778] Step 5:

[0779] The server adjusts the notification content based on the acquired emotional state. The input consists of the user's emotional analysis results and earthquake prediction information. The server utilizes a generative AI model to generate prompt messages and optimize the message the user receives. Specific actions include, for example, including gentler messages or additional safety information. The output is the adjusted notification message.

[0780] Step 6:

[0781] The user receives notifications from the server via their device. The input is information provided as a coordinated notification message. The user reviews the message content and takes action to make specific decisions regarding earthquake preparations and evacuation. The output is the appropriate safety measures taken by the user.

[0782] Step 7:

[0783] Users provide feedback after receiving a notification. The input consists of the user's own reaction and evaluation of the notification's effectiveness. This user feedback is sent to the server, and specific actions are taken to improve future notifications. The output is feedback data that helps improve the accuracy of system analysis.

[0784] (Application Example 2)

[0785] 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".

[0786] In recent years, the importance of improving the accuracy of natural disaster predictions has increased. However, conventional prediction systems do not take into account the emotional state of users, and the information they provide may increase anxiety and stress. Furthermore, optimizing the customer experience in physical stores requires a flexible approach that combines disaster information with the emotional state of customers. Solving these challenges is essential.

[0787] 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.

[0788] In this invention, the server includes means for analyzing biometric information acquired from a detection device to detect abnormal behavior, a decision device for generating predictive information and providing information based on the user's emotional state, and means for adjusting the customer experience. This makes it possible to adjust predictive information about natural disasters according to the user's emotional state and optimize the customer experience in physical stores.

[0789] A "detection device" is a device attached to an animal to acquire biological information.

[0790] "Biometric information" refers to data such as an animal's heart rate, body surface temperature, and movement patterns.

[0791] "Abnormal behavior" refers to behaviors in animals that are different from the norm and may be a precursor to an earthquake.

[0792] "Predictive information" refers to information about predicting the occurrence of natural disasters, generated based on the results of an analysis of abnormal behavior.

[0793] A "decision-making device" refers to an analytical tool that analyzes the user's emotional state and provides appropriate information.

[0794] "Emotional state" refers to the user's psychological state, such as their stress level or anxiety level.

[0795] "Customer experience" refers to the customer's purchasing and service usage experience at physical stores.

[0796] "Information provision" refers to the act of conveying predictive information, reassuring messages, or advice to users.

[0797] The system for implementing the present invention consists of a detection device attached to an animal, a server, a user terminal, and a display device. The server acquires biological information such as the animal's heart rate, body surface temperature, and movement patterns, and analyzes it using a machine learning algorithm. Based on the analyzed data, the server detects abnormal behavior and generates predictive information by comparing it with past natural disaster data.

[0798] A key element of this system is the emotion engine, which analyzes the user's emotional state in real time. The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes this data to understand their current emotional state. The emotional state quantifies psychological factors such as stress and anxiety, and based on this, the server optimizes the information it conveys to the user. For example, it adjusts the tone and content of notifications regarding predicted natural disasters to match the user's emotional state. This allows users to receive information with greater peace of mind.

[0799] Furthermore, in physical stores, the system uses earthquake probability information and customer sentiment data to display personalized messages to customers. This provides a sense of security while encouraging safe behavior. For example, if a store is crowded and the system detects that a customer looks anxious, it will display a message such as, "This is the area where you can enjoy shopping with peace of mind."

[0800] An example of a prompt message to use when using a generative AI model is: "When the system detects that the user is feeling anxious, generate relaxing recommendations and reassuring messages." This allows the system to provide a more personalized user experience.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The terminal acquires biometric information such as heart rate, body surface temperature, and movement patterns from detection devices attached to animals. The input is biometric data from sensors, and by collecting this data in real time, it outputs it as biometric information.

[0804] Step 2:

[0805] The server applies machine learning algorithms based on biometric information received from the terminal to detect abnormal animal behavior. The input is biometric information, and by performing data analysis on this information, it outputs patterns of abnormal animal behavior.

[0806] Step 3:

[0807] The server generates earthquake prediction information by comparing detected abnormal behavior patterns with past natural disaster data. The input consists of abnormal behavior patterns and historical data, and the prediction information is obtained by matching and analyzing them.

[0808] Step 4:

[0809] The user terminal uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state. The input is video and audio data, which is used for emotion analysis, and the emotional state is output.

[0810] Step 5:

[0811] The server considers the generated predictive information and the user's emotional state to provide optimized information to the user. The input consists of predictive information and emotional state; based on these, the server generates notification content and outputs it to the user.

[0812] Step 6:

[0813] In physical stores, display devices show personalized messages to customers based on notifications from the server. The input is the notified information, and accordingly, the system outputs a message designed to reassure the customer.

[0814] Step 7:

[0815] Users contribute to improving the accuracy of the emotion engine by providing feedback to the system after receiving a notification. The input is feedback information, and reflecting this leads to improvements in the system.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0821] 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.

[0822] 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.

[0823] 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 based, for example, 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.

[0824] 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."

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] The following is further disclosed regarding the embodiments described above.

[0838] (Claim 1)

[0839] A means of acquiring biological information from sensors attached to animals,

[0840] A means for detecting abnormal behavior in animals by analyzing acquired biological information,

[0841] A means for generating earthquake prediction information based on the results of abnormal behavior analysis,

[0842] A means of notifying the user of the generated prediction information,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein a machine learning algorithm is used to analyze the abnormal behavior of the animal.

[0846] (Claim 3)

[0847] The system according to claim 1, which includes means for generating the prediction information to evaluate the probability of an earthquake occurring based on past earthquake data and similar patterns.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A means of acquiring biological information from a measuring device attached to an animal,

[0851] A means for collecting acquired biometric information at regular intervals and transmitting it to a central processing unit using communication technology,

[0852] In the central processing unit, there is a means for accumulating the acquired data set and detecting the unique behavior of animals using data analysis technology,

[0853] A means for generating prediction information about the occurrence of natural phenomena based on the results of analyzing unusual behavior,

[0854] A means of transmitting the generated prediction information to the user's terminal and notifying the user,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein a machine learning method is used as a data analysis technique for analyzing the specific behavior of the aforementioned animals.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the generation of the predictive information includes means for evaluating the likelihood of a natural phenomenon occurring based on data of natural phenomena recorded in the past and similar behavioral patterns.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] A device for acquiring biological information from animals,

[0863] A device that analyzes acquired biological information to identify abnormal behavior in animals,

[0864] A device that generates earthquake prediction information based on the results of analyzing abnormal behavior,

[0865] A device that notifies the user of the generated prediction information,

[0866] A device having the function of implementing measures to ensure animal and human safety in accordance with the aforementioned notification,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which uses artificial intelligence in analyzing the abnormal behavior of the aforementioned animals.

[0870] (Claim 3)

[0871] The system according to claim 1, which includes a device for evaluating the probability of an earthquake occurring based on past earthquake data and similar behavioral patterns in generating the aforementioned prediction information.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] A means of acquiring biological information from sensors attached to animals,

[0875] A means for detecting abnormal behavior in animals by analyzing acquired biological information,

[0876] A means for generating earthquake prediction information based on the results of abnormal behavior analysis,

[0877] A means for acquiring and analyzing the user's emotional state and adjusting the notification content,

[0878] Means for providing users with generated predictive information and tailored notifications,

[0879] A means of collecting user feedback and improving the accuracy of the system's analysis,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein a machine learning algorithm is used to analyze the abnormal behavior of the animal.

[0883] (Claim 3)

[0884] The system according to claim 1, which includes means for generating the prediction information to evaluate the probability of an earthquake occurring based on past earthquake data and similar patterns.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means of acquiring biological information from a detection device attached to an animal,

[0888] A means for detecting abnormal behavior in animals by analyzing acquired biological information,

[0889] A means for generating earthquake prediction information based on the results of abnormal behavior analysis,

[0890] A decision-making device that analyzes the user's emotional state and provides information tailored to the user,

[0891] A means of notifying the user of the generated prediction information,

[0892] A means of adjusting the customer experience in the store environment based on the user's emotional state,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, which uses a machine learning algorithm to analyze the abnormal behavior of the aforementioned animals, and further uses it to analyze the emotional state of the user.

[0896] (Claim 3)

[0897] The system according to claim 1, which includes means for generating the aforementioned predictive information, to evaluate the likelihood of occurrence based on past natural disaster data and similar patterns, and to select notification content according to the user's emotional state. [Explanation of Symbols]

[0898] 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 means of acquiring biological information from sensors attached to animals, A means for detecting abnormal behavior in animals by analyzing acquired biological information, A means for generating earthquake prediction information based on the results of abnormal behavior analysis, A means of notifying the user of the generated prediction information, A system that includes this.

2. The system according to claim 1, wherein a machine learning algorithm is used to analyze the abnormal behavior of the animal.

3. The system according to claim 1, which includes means for generating the prediction information to evaluate the probability of an earthquake occurring based on past earthquake data and similar patterns.

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