system

Abstract

The system according to this embodiment aims to analyze animal sensor information and provide information necessary for earthquake prediction. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data from sensors attached to animals. The analysis unit analyzes the data collected by the data collection unit and translates the information necessary for earthquake prediction into language. The notification unit transmits an anomaly notification based on the information obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] In the conventional technology, earthquake prediction based on animal behavior has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze sensor information of animals and provide information necessary for earthquake prediction.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects data from sensors attached to animals. The analysis unit analyzes the data collected by the collection unit and verbalizes the information necessary for earthquake prediction. The notification unit transmits an abnormality notification based on the information obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze animal sensor information and provide information necessary for earthquake prediction. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The earthquake prediction system according to an embodiment of the present invention is a system that predicts earthquakes by utilizing the "body sensors" of living organisms. This system digitizes sensor information from animals and, in conjunction with a generating AI, accurately verbalizes the information that animals use to predict earthquakes, thereby performing earthquake prediction. The earthquake prediction system aims to eliminate earthquake casualties worldwide while generating profits through sensor sales, personal apps, and regional contracts. For example, the earthquake prediction system attaches sensors to animals to detect their activity levels and abnormal behavior. For example, the earthquake prediction system attaches an activity level sensor to the neck of a cow and collects data. This data is analyzed by the generating AI, and the information necessary for earthquake prediction is verbalized. The generating AI analyzes the animal's abnormal behavior and changes in activity levels to detect earthquake precursors. Next, the earthquake prediction system sends an anomaly notification to a smartphone based on the information analyzed by the generating AI. This allows users to grasp earthquake precursors in real time. Furthermore, the earthquake prediction system can strengthen disaster prevention measures throughout a region by providing earthquake precursor reports to local governments and companies. Furthermore, the earthquake prediction system also performs health management using animal sensors. For example, the system measures the activity level and sleep of animals and notifies owners or veterinarians if abnormalities are detected. This enables both animal health management and earthquake prediction. The earthquake prediction system is collaborating with earthquake prediction research institutions and is already partially commercialized. In the future, the aim is to provide services to even more regions and individuals, minimizing damage caused by earthquakes. Through this, the earthquake prediction system becomes capable of earthquake prediction by collecting, analyzing, and notifying animal sensor information.

[0029] The earthquake prediction system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data from sensors attached to animals. The data collection unit uses, for example, sensors that detect the activity level and abnormal behavior of animals. The data collection unit can collect data using, for example, GPS sensors, acceleration sensors, heart rate sensors, etc. The data collection unit collects data such as the animal's location information, activity level, and heart rate. The data collection unit can, for example, measure the animal's activity level and set thresholds for detecting abnormal behavior. The data collection unit can, for example, analyze the animal's behavior patterns to detect abnormal behavior. The analysis unit analyzes the data collected by the data collection unit and translates the information necessary for earthquake prediction into language. The analysis unit can, for example, analyze the data using data preprocessing methods and analysis algorithms. The analysis unit can, for example, analyze the animal's abnormal behavior and changes in activity level to detect earthquake precursors. The analysis unit can, for example, analyze patterns of abnormal behavior and rapid changes in activity level. The analysis unit can, for example, use a generative AI to analyze abnormal animal behavior and changes in activity levels to detect earthquake precursors. The analysis unit, for example, uses a generative AI to analyze abnormal animal behavior and changes in activity levels and executes an algorithm to detect earthquake precursors. The notification unit sends an anomaly notification based on the information obtained by the analysis unit. The notification unit can, for example, send an anomaly notification to a smartphone. The notification unit can, for example, use text, audio, images, etc., as the format of the anomaly notification. The notification unit can, for example, send the anomaly notification by means of email, SMS, push notification, etc. The notification unit can, for example, include information about earthquake precursors as the content of the anomaly notification. The notification unit can, for example, use devices such as smartphones, tablets, and PCs as means of sending anomaly notifications. Thus, the earthquake prediction system according to the embodiment can predict earthquakes by collecting, analyzing, and notifying animal sensor information.

[0030] The data collection unit collects data from sensors attached to animals. Specifically, it uses GPS sensors, accelerometers, and heart rate sensors to detect the animal's activity level and abnormal behavior. The GPS sensor acquires the animal's location information in real time, allowing the system to understand the animal's movement patterns and range of activity. The accelerometer detects the intensity and direction of the animal's movement, capturing abnormal movements and sudden changes in movement. The heart rate sensor monitors the animal's heart rate, detecting stress and excitement levels. The data obtained from these sensors is used to record the animal's behavior patterns and physiological state in detail. The data collection unit centrally manages this data and transmits it to a central database in real time. Furthermore, the data collection unit can measure the animal's activity level and set thresholds for detecting abnormal behavior. For example, if an abnormally high or low activity level is detected compared to normal activity levels, it is recognized as abnormal behavior. The data collection unit can analyze the animal's behavior patterns and establish criteria for detecting abnormal behavior. This allows the data collection unit to efficiently collect animal behavior data and contribute to the early detection of abnormal behavior. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if abnormal animal behavior, which is considered a precursor to earthquakes, occurs frequently, increasing the data collection frequency allows for the acquisition of more detailed data, which can then be provided to the analysis unit. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit and translates the information necessary for earthquake prediction into language. Specifically, it analyzes the data using data preprocessing methods and analysis algorithms. Data preprocessing includes noise reduction, data normalization, and missing value imputation. This creates a dataset suitable for analysis. As analysis algorithms, machine learning models and statistical methods are used to detect abnormal animal behavior and changes in activity levels. For example, it analyzes rapid changes in animal activity levels and patterns of abnormal behavior to detect earthquake precursors. The analysis unit can use generative AI to analyze abnormal animal behavior and changes in activity levels and detect earthquake precursors. Generative AI learns from large amounts of data and analyzes patterns of abnormal behavior and changes in activity levels with high accuracy. Specifically, the generative AI receives animal behavior data as input and executes algorithms to detect abnormal behavior and predict earthquake precursors. By analyzing animal behavior data and detecting patterns of abnormal behavior and rapid changes in activity levels, the generative AI can predict earthquake precursors with high accuracy. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past animal behavior data from earthquakes, the system can predict risk fluctuations in specific regions and time periods, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The notification unit sends anomaly notifications based on information obtained by the analysis unit. Specifically, it can send anomaly notifications to smartphones. The notification unit can use text, audio, images, and other formats for anomaly notifications. For example, it can send information about earthquake precursors as a text message to quickly notify the user. By using audio notifications, information can be conveyed to users who have difficulty reading text messages, such as the visually impaired and the elderly. Furthermore, by using image notifications, visual information about earthquake precursors can be provided, deepening the user's understanding. The notification unit can send anomaly notifications by means of email, SMS, push notifications, etc. This allows users to receive anomaly notifications through multiple means, reducing the risk of missing important information. The notification unit can include information about earthquake precursors in the content of anomaly notifications. For example, it can provide details of unusual animal behavior or information about the region and time of day when earthquakes are predicted to occur. This allows users to obtain specific information about earthquake precursors and take appropriate action. The notification unit can use devices such as smartphones, tablets, and PCs as means of sending anomaly notifications. This allows users to receive anomaly notifications on their devices and respond quickly. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, it can review notification content and transmission methods based on feedback from users who have received anomaly notifications. The notification unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the notification unit can provide users with anomaly notifications quickly and reliably, improving the reliability and effectiveness of the earthquake prediction system.

[0033] The data collection unit can use sensors to detect animal activity levels and abnormal behavior. For example, the data collection unit may use an accelerometer to measure animal activity levels. For example, the data collection unit may use a heart rate sensor to detect abnormal animal behavior. For example, the data collection unit may use a GPS sensor to acquire animal location information. This improves the accuracy of earthquake prediction by detecting animal activity levels and abnormal behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal activity data into a generating AI and have the generating AI perform abnormal behavior detection.

[0034] The analysis unit can analyze abnormal animal behavior and changes in activity levels to detect earthquake precursors. For example, the analysis unit can analyze animal behavior patterns to detect abnormal animal behavior. For example, the analysis unit can analyze activity level data to detect changes in animal activity levels. For example, the analysis unit can analyze patterns of abnormal animal behavior and execute algorithms to detect earthquake precursors. This allows for accurate detection of earthquake precursors by analyzing abnormal animal behavior and changes in activity levels. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data into a generative AI and have the generative AI perform earthquake precursor detection.

[0035] The notification unit can send anomaly notifications to smartphones. The notification unit can use text, audio, images, etc., as the format of the anomaly notification. The notification unit can send anomaly notifications by means of email, SMS, push notifications, etc. The notification unit can include information about earthquake precursors as the content of the anomaly notification. This allows users to understand earthquake precursors in real time by sending anomaly notifications to their smartphones. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send earthquake precursor information analyzed by a generating AI to a smartphone.

[0036] The analysis unit can generate earthquake precursor reports and provide them to local governments and companies. The analysis unit can use formats such as PDF and HTML for the earthquake precursor reports. The analysis unit can provide the earthquake precursor reports by means such as email and websites. By generating and providing earthquake precursor reports to local governments and companies, disaster prevention measures for the entire region can be strengthened. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can generate earthquake precursor reports based on earthquake precursor information analyzed by a generation AI and provide them to local governments and companies.

[0037] The data collection unit measures the animal's activity level and sleep, and can notify the owner or veterinarian if it detects an abnormality. For example, the data collection unit can use an accelerometer to measure the animal's activity level. For example, the data collection unit can use a heart rate sensor to measure the animal's sleep. For example, the data collection unit can analyze the animal's activity level and sleep data and execute an algorithm to detect abnormalities. This enables animal health management by measuring the animal's activity level and sleep, and notifying the owner or veterinarian if an abnormality is detected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal activity level data into a generating AI and have the generating AI perform abnormality detection.

[0038] The data collection unit can optimize the timing of data collection based on the animal's biological rhythms. For example, the data collection unit can concentrate data collection during the animal's active periods. For example, the data collection unit can refrain from collecting data during the animal's resting periods. For example, the data collection unit can adjust the timing of data collection according to the season or weather. This enables efficient data collection by optimizing the timing of data collection based on the animal's biological rhythms. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal biological rhythm data into a generating AI and have the generating AI perform the optimization of the data collection timing.

[0039] The data collection unit can collect data by combining different sensors depending on the animal species and individual differences. For example, the data collection unit can attach an activity sensor and a heart rate sensor to a cow. For example, the data collection unit can attach a GPS sensor and a body temperature sensor to a dog. For example, the data collection unit can attach an activity sensor and a sleep sensor to a cat. This allows for more accurate data collection by combining different sensors according to the animal species and individual differences. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sensor data according to the animal species and individual differences into a generating AI and have the generating AI perform optimization of data collection.

[0040] The data collection unit can collect data for comparing abnormal behavior in different regions, taking into account the geographical location information of the animals. For example, the data collection unit can collect different abnormal behavior patterns of animals in different regions. For example, the data collection unit can compare the frequency of abnormal behavior based on geographical location information. For example, the data collection unit can collect data while considering environmental factors in each region. This allows for the comparison of abnormal behavior in different regions by collecting data while considering the geographical location information of the animals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data of animals into a generating AI and have the generating AI perform a comparison of abnormal behavior in different regions.

[0041] The data collection unit can monitor the animal's health status in real time and increase the collection frequency if an abnormality is detected. For example, the data collection unit can increase the data collection frequency if the animal's body temperature rises abnormally. For example, the data collection unit can increase the data collection frequency if the animal's heart rate fluctuates abnormally. For example, the data collection unit can increase the data collection frequency if the animal's activity level decreases sharply. This allows for a rapid response by monitoring the animal's health status in real time and increasing the collection frequency when an abnormality is detected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal health status data into a generating AI and have the generating AI perform abnormality detection and adjustment of the collection frequency.

[0042] The analysis unit can improve the accuracy of earthquake prediction by analyzing patterns of abnormal animal behavior in detail. For example, the analysis unit can analyze the correlation between the frequency of abnormal behavior and the occurrence of earthquakes. For example, the analysis unit can evaluate the accuracy of earthquake prediction for each type of abnormal behavior. For example, the analysis unit can analyze the time of day of abnormal behavior and the timing of earthquake occurrence. In this way, the accuracy of earthquake prediction is improved by analyzing patterns of abnormal animal behavior in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data into a generative AI and have the generative AI perform an analysis of abnormal behavior patterns.

[0043] The analysis unit can analyze animal activity data in combination with other environmental data (temperature, humidity, etc.). For example, the analysis unit can analyze the relationship between temperature changes and animal activity levels. For example, the analysis unit can analyze the relationship between humidity changes and abnormal animal behavior. For example, the analysis unit can analyze the relationship between environmental data and the health status of animals. By combining animal activity data with other environmental data, more accurate earthquake prediction becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input animal activity data and environmental data into a generative AI and have the generative AI perform a combined analysis of the data.

[0044] The analysis unit can compare abnormal behavior data of animals with data of other animal species to identify common earthquake precursor patterns. For example, the analysis unit can compare abnormal behavior data of cows and dogs to identify common earthquake precursor patterns. For example, the analysis unit can compare abnormal behavior data of cats and birds to identify common earthquake precursor patterns. For example, the analysis unit can integrate abnormal behavior data of multiple animal species and analyze earthquake precursor patterns. This allows for the identification of common earthquake precursor patterns by comparing abnormal behavior data of animals with data of other animal species. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal behavior data of different animal species into a generative AI and have the generative AI identify common earthquake precursor patterns.

[0045] The analysis unit can analyze animal health status data and use it for both earthquake prediction and health management. For example, the analysis unit can improve the accuracy of earthquake prediction based on animal health status data. For example, the analysis unit can analyze animal health status data and identify the causes of abnormal behavior. For example, the analysis unit can utilize animal health status data to help with both earthquake prediction and health management. Thus, by analyzing animal health status data, it is possible to use it for both earthquake prediction and health management. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input animal health status data into a generative AI and have the generative AI perform analysis to help with both earthquake prediction and health management.

[0046] The notification unit can select the optimal notification method by considering the user's current location information when sending an abnormality notification. For example, if the user is outdoors, the notification unit can send a push notification to the smartphone. For example, if the user is at home, the notification unit can send a notification via a smart speaker. For example, if the user is in a car, the notification unit can send a notification via the car navigation system. By selecting the optimal notification method by considering the user's current location information, the user can receive notifications in the most optimal way. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's location information data into a generating AI and have the generating AI select the optimal notification method.

[0047] The notification unit can determine the priority of notifications by referring to past notification history when sending an anomaly notification. For example, the notification unit can prioritize sending important notifications to users who have frequently received anomaly notifications in the past. For example, the notification unit can send detailed notifications to users who have never received anomaly notifications in the past. For example, the notification unit can adjust the priority of notifications according to the user's level of interest based on past notification history. This allows users to receive important notifications preferentially by determining the priority of notifications by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the determination of notification priorities.

[0048] The notification unit can select the optimal notification method when sending an abnormality notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. For example, if the user is using a tablet, the notification unit can send a notification optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit can send a concise and highly visible notification. By selecting the optimal notification method considering the user's device information, the user can receive notifications in the most optimal way. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0049] The notification unit can send notifications to the user's social media accounts when an anomaly notification is sent. This allows the user to receive notifications on the platform they use. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media account information into a generating AI and have the generating AI perform the notification linking.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can automatically adjust the sensor sensitivity in response to changes in the animal's physical condition and environment when collecting animal sensor information. For example, if an animal becomes ill, the sensor sensitivity can be increased to collect more detailed data. Similarly, if the temperature or humidity of the environment changes rapidly, the sensor sensitivity can be adjusted to maintain data accuracy. Furthermore, if an animal is experiencing stress, the sensor sensitivity can be appropriately adjusted to minimize the effects of stress. As a result, the data collection unit can collect more accurate data by automatically adjusting the sensor sensitivity in response to changes in the animal's physical condition and environment.

[0052] The analysis unit can identify patterns in abnormal animal behavior by comparing it with past data. For example, by referring to data on abnormal behavior exhibited by the same animal in the past and comparing it with current abnormal behavior, earthquake precursors can be detected more accurately. Furthermore, by comparing it with abnormal behavior data from other animals, common abnormal behavior patterns can be identified. In addition, by analyzing the frequency and duration of abnormal behavior, the probability of an earthquake occurring can be predicted. In this way, the analysis unit can improve the accuracy of earthquake prediction by identifying patterns in abnormal behavior by comparing it with past data.

[0053] The notification unit can adjust the timing of abnormal notifications, taking the user's schedule into consideration. For example, it can delay notifications if the user is in a meeting or sleeping. It can also prioritize notifications if the user has an important appointment. Furthermore, it can adjust the content and format of notifications based on the user's schedule. As a result, the notification unit can adjust the timing of notifications to suit the user's schedule, ensuring that users receive notifications at the most opportune time.

[0054] The data collection unit can adjust the data collection range considering the animal's range of movement when collecting animal sensor information. For example, if the animal moves over a wide area, the data collection range can be expanded to collect more detailed data. Conversely, if the animal stays in a specific area, the data collection range can be narrowed to collect data more efficiently. Furthermore, the placement and settings of the sensors can be optimized according to the animal's range of movement. As a result, the data collection unit can efficiently and accurately collect data by adjusting the data collection range considering the animal's range of movement.

[0055] The analysis unit can identify the location of abnormal animal behavior when analyzing abnormal animal behavior data. For example, it can display the locations where animals exhibited abnormal behavior on a map to pinpoint the location of the abnormal behavior. Furthermore, by comparing the locations of abnormal behavior with the locations of earthquakes, it is possible to detect earthquake precursors more accurately. In addition, it is possible to predict the probability of an earthquake occurring based on the locations of abnormal behavior. In this way, the analysis unit can improve the accuracy of earthquake prediction by identifying the locations of abnormal behavior.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data from sensors attached to the animal. The data collection unit uses sensors that detect the animal's activity level and abnormal behavior, for example. The data collection unit can collect data using GPS sensors, accelerometers, heart rate sensors, etc. The data collection unit collects data such as the animal's location, activity level, and heart rate. The data collection unit can measure the animal's activity level and set thresholds for detecting abnormal behavior. The data collection unit can analyze the animal's behavior patterns in order to detect abnormal behavior. Step 2: The analysis unit analyzes the data collected by the collection unit and translates the information necessary for earthquake prediction into language. The analysis unit can analyze the data using data preprocessing methods and analysis algorithms. The analysis unit analyzes abnormal animal behavior and changes in activity levels to detect earthquake precursors. The analysis unit can analyze patterns of abnormal behavior and rapid changes in activity levels. The analysis unit can use generative AI to analyze abnormal animal behavior and changes in activity levels to detect earthquake precursors. The analysis unit uses generative AI to analyze abnormal animal behavior and changes in activity levels and executes algorithms to detect earthquake precursors. Step 3: The notification unit sends an anomaly notification based on the information obtained by the analysis unit. The notification unit can send an anomaly notification to a smartphone. The notification unit can use text, audio, images, etc., as the format of the anomaly notification. The notification unit can send anomaly notifications by means of email, SMS, push notifications, etc. The notification unit can include information about earthquake precursors as the content of the anomaly notification. The notification unit can use devices such as smartphones, tablets, and PCs as means of sending anomaly notifications.

[0058] (Example of form 2) The earthquake prediction system according to an embodiment of the present invention is a system that predicts earthquakes by utilizing the "body sensors" of living organisms. This system digitizes sensor information from animals and, in conjunction with a generating AI, accurately verbalizes the information that animals use to predict earthquakes, thereby performing earthquake prediction. The earthquake prediction system aims to eliminate earthquake casualties worldwide while generating profits through sensor sales, personal apps, and regional contracts. For example, the earthquake prediction system attaches sensors to animals to detect their activity levels and abnormal behavior. For example, the earthquake prediction system attaches an activity level sensor to the neck of a cow and collects data. This data is analyzed by the generating AI, and the information necessary for earthquake prediction is verbalized. The generating AI analyzes the animal's abnormal behavior and changes in activity levels to detect earthquake precursors. Next, the earthquake prediction system sends an anomaly notification to a smartphone based on the information analyzed by the generating AI. This allows users to grasp earthquake precursors in real time. Furthermore, the earthquake prediction system can strengthen disaster prevention measures throughout a region by providing earthquake precursor reports to local governments and companies. Furthermore, the earthquake prediction system also performs health management using animal sensors. For example, the system measures the activity level and sleep of animals and notifies owners or veterinarians if abnormalities are detected. This enables both animal health management and earthquake prediction. The earthquake prediction system is collaborating with earthquake prediction research institutions and is already partially commercialized. In the future, the aim is to provide services to even more regions and individuals, minimizing damage caused by earthquakes. Through this, the earthquake prediction system becomes capable of earthquake prediction by collecting, analyzing, and notifying animal sensor information.

[0059] The earthquake prediction system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data from sensors attached to animals. The data collection unit uses, for example, sensors that detect the activity level and abnormal behavior of animals. The data collection unit can collect data using, for example, GPS sensors, acceleration sensors, heart rate sensors, etc. The data collection unit collects data such as the animal's location information, activity level, and heart rate. The data collection unit can, for example, measure the animal's activity level and set thresholds for detecting abnormal behavior. The data collection unit can, for example, analyze the animal's behavior patterns to detect abnormal behavior. The analysis unit analyzes the data collected by the data collection unit and translates the information necessary for earthquake prediction into language. The analysis unit can, for example, analyze the data using data preprocessing methods and analysis algorithms. The analysis unit can, for example, analyze the animal's abnormal behavior and changes in activity level to detect earthquake precursors. The analysis unit can, for example, analyze patterns of abnormal behavior and rapid changes in activity level. The analysis unit can, for example, use a generative AI to analyze abnormal animal behavior and changes in activity levels to detect earthquake precursors. The analysis unit, for example, uses a generative AI to analyze abnormal animal behavior and changes in activity levels and executes an algorithm to detect earthquake precursors. The notification unit sends an anomaly notification based on the information obtained by the analysis unit. The notification unit can, for example, send an anomaly notification to a smartphone. The notification unit can, for example, use text, audio, images, etc., as the format of the anomaly notification. The notification unit can, for example, send the anomaly notification by means of email, SMS, push notification, etc. The notification unit can, for example, include information about earthquake precursors as the content of the anomaly notification. The notification unit can, for example, use devices such as smartphones, tablets, and PCs as means of sending anomaly notifications. Thus, the earthquake prediction system according to the embodiment can predict earthquakes by collecting, analyzing, and notifying animal sensor information.

[0060] The data collection unit collects data from sensors attached to animals. Specifically, it uses GPS sensors, accelerometers, and heart rate sensors to detect the animal's activity level and abnormal behavior. The GPS sensor acquires the animal's location information in real time, allowing the system to understand the animal's movement patterns and range of activity. The accelerometer detects the intensity and direction of the animal's movement, capturing abnormal movements and sudden changes in movement. The heart rate sensor monitors the animal's heart rate, detecting stress and excitement levels. The data obtained from these sensors is used to record the animal's behavior patterns and physiological state in detail. The data collection unit centrally manages this data and transmits it to a central database in real time. Furthermore, the data collection unit can measure the animal's activity level and set thresholds for detecting abnormal behavior. For example, if an abnormally high or low activity level is detected compared to normal activity levels, it is recognized as abnormal behavior. The data collection unit can analyze the animal's behavior patterns and establish criteria for detecting abnormal behavior. This allows the data collection unit to efficiently collect animal behavior data and contribute to the early detection of abnormal behavior. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if abnormal animal behavior, which is considered a precursor to earthquakes, occurs frequently, increasing the data collection frequency allows for the acquisition of more detailed data, which can then be provided to the analysis unit. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0061] The analysis unit analyzes the data collected by the collection unit and translates the information necessary for earthquake prediction into language. Specifically, it analyzes the data using data preprocessing methods and analysis algorithms. Data preprocessing includes noise reduction, data normalization, and missing value imputation. This creates a dataset suitable for analysis. As analysis algorithms, machine learning models and statistical methods are used to detect abnormal animal behavior and changes in activity levels. For example, it analyzes rapid changes in animal activity levels and patterns of abnormal behavior to detect earthquake precursors. The analysis unit can use generative AI to analyze abnormal animal behavior and changes in activity levels and detect earthquake precursors. Generative AI learns from large amounts of data and analyzes patterns of abnormal behavior and changes in activity levels with high accuracy. Specifically, the generative AI receives animal behavior data as input and executes algorithms to detect abnormal behavior and predict earthquake precursors. By analyzing animal behavior data and detecting patterns of abnormal behavior and rapid changes in activity levels, the generative AI can predict earthquake precursors with high accuracy. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past animal behavior data from earthquakes, the system can predict risk fluctuations in specific regions and time periods, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0062] The notification unit sends anomaly notifications based on information obtained by the analysis unit. Specifically, it can send anomaly notifications to smartphones. The notification unit can use text, audio, images, and other formats for anomaly notifications. For example, it can send information about earthquake precursors as a text message to quickly notify the user. By using audio notifications, information can be conveyed to users who have difficulty reading text messages, such as the visually impaired and the elderly. Furthermore, by using image notifications, visual information about earthquake precursors can be provided, deepening the user's understanding. The notification unit can send anomaly notifications by means of email, SMS, push notifications, etc. This allows users to receive anomaly notifications through multiple means, reducing the risk of missing important information. The notification unit can include information about earthquake precursors in the content of anomaly notifications. For example, it can provide details of unusual animal behavior or information about the region and time of day when earthquakes are predicted to occur. This allows users to obtain specific information about earthquake precursors and take appropriate action. The notification unit can use devices such as smartphones, tablets, and PCs as means of sending anomaly notifications. This allows users to receive anomaly notifications on their devices and respond quickly. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, it can review notification content and transmission methods based on feedback from users who have received anomaly notifications. The notification unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the notification unit can provide users with anomaly notifications quickly and reliably, improving the reliability and effectiveness of the earthquake prediction system.

[0063] The data collection unit can use sensors to detect animal activity levels and abnormal behavior. For example, the data collection unit may use an accelerometer to measure animal activity levels. For example, the data collection unit may use a heart rate sensor to detect abnormal animal behavior. For example, the data collection unit may use a GPS sensor to acquire animal location information. This improves the accuracy of earthquake prediction by detecting animal activity levels and abnormal behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal activity data into a generating AI and have the generating AI perform abnormal behavior detection.

[0064] The analysis unit can analyze abnormal animal behavior and changes in activity levels to detect earthquake precursors. For example, the analysis unit can analyze animal behavior patterns to detect abnormal animal behavior. For example, the analysis unit can analyze activity level data to detect changes in animal activity levels. For example, the analysis unit can analyze patterns of abnormal animal behavior and execute algorithms to detect earthquake precursors. This allows for accurate detection of earthquake precursors by analyzing abnormal animal behavior and changes in activity levels. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data into a generative AI and have the generative AI perform earthquake precursor detection.

[0065] The notification unit can send anomaly notifications to smartphones. The notification unit can use text, audio, images, etc., as the format of the anomaly notification. The notification unit can send anomaly notifications by means of email, SMS, push notifications, etc. The notification unit can include information about earthquake precursors as the content of the anomaly notification. This allows users to understand earthquake precursors in real time by sending anomaly notifications to their smartphones. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send earthquake precursor information analyzed by a generating AI to a smartphone.

[0066] The analysis unit can generate earthquake precursor reports and provide them to local governments and companies. The analysis unit can use formats such as PDF and HTML for the earthquake precursor reports. The analysis unit can provide the earthquake precursor reports by means such as email and websites. By generating and providing earthquake precursor reports to local governments and companies, disaster prevention measures for the entire region can be strengthened. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can generate earthquake precursor reports based on earthquake precursor information analyzed by a generation AI and provide them to local governments and companies.

[0067] The data collection unit measures the animal's activity level and sleep, and can notify the owner or veterinarian if it detects an abnormality. For example, the data collection unit can use an accelerometer to measure the animal's activity level. For example, the data collection unit can use a heart rate sensor to measure the animal's sleep. For example, the data collection unit can analyze the animal's activity level and sleep data and execute an algorithm to detect abnormalities. This enables animal health management by measuring the animal's activity level and sleep, and notifying the owner or veterinarian if an abnormality is detected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal activity level data into a generating AI and have the generating AI perform abnormality detection.

[0068] The data collection unit can estimate the user's emotions and adjust the frequency of sensor data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the data collection frequency to provide more detailed information. For example, if the user is relaxed, the data collection unit can decrease the data collection frequency to conserve battery power. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This allows for efficient data collection by adjusting the data collection frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the data collection frequency.

[0069] The data collection unit can optimize the timing of data collection based on the animal's biological rhythms. For example, the data collection unit can concentrate data collection during the animal's active periods. For example, the data collection unit can refrain from collecting data during the animal's resting periods. For example, the data collection unit can adjust the timing of data collection according to the season or weather. This enables efficient data collection by optimizing the timing of data collection based on the animal's biological rhythms. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal biological rhythm data into a generating AI and have the generating AI perform the optimization of the data collection timing.

[0070] The data collection unit can collect data by combining different sensors depending on the animal species and individual differences. For example, the data collection unit can attach an activity sensor and a heart rate sensor to a cow. For example, the data collection unit can attach a GPS sensor and a body temperature sensor to a dog. For example, the data collection unit can attach an activity sensor and a sleep sensor to a cat. This allows for more accurate data collection by combining different sensors according to the animal species and individual differences. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sensor data according to the animal species and individual differences into a generating AI and have the generating AI perform optimization of data collection.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit may prioritize collecting abnormal behavior data. For example, if the user is feeling at ease, the data collection unit may prioritize collecting normal activity data. For example, if the user is excited, the data collection unit may prioritize real-time data collection. This allows for the priority collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0072] The data collection unit can collect data for comparing abnormal behavior in different regions, taking into account the geographical location information of the animals. For example, the data collection unit can collect different abnormal behavior patterns of animals in different regions. For example, the data collection unit can compare the frequency of abnormal behavior based on geographical location information. For example, the data collection unit can collect data while considering environmental factors in each region. This allows for the comparison of abnormal behavior in different regions by collecting data while considering the geographical location information of the animals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data of animals into a generating AI and have the generating AI perform a comparison of abnormal behavior in different regions.

[0073] The data collection unit can monitor the animal's health status in real time and increase the collection frequency if an abnormality is detected. For example, the data collection unit can increase the data collection frequency if the animal's body temperature rises abnormally. For example, the data collection unit can increase the data collection frequency if the animal's heart rate fluctuates abnormally. For example, the data collection unit can increase the data collection frequency if the animal's activity level decreases sharply. This allows for a rapid response by monitoring the animal's health status in real time and increasing the collection frequency when an abnormality is detected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal health status data into a generating AI and have the generating AI perform abnormality detection and adjustment of the collection frequency.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide detailed analysis results. For example, if the user is feeling at ease, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis results based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis results.

[0075] The analysis unit can improve the accuracy of earthquake prediction by analyzing patterns of abnormal animal behavior in detail. For example, the analysis unit can analyze the correlation between the frequency of abnormal behavior and the occurrence of earthquakes. For example, the analysis unit can evaluate the accuracy of earthquake prediction for each type of abnormal behavior. For example, the analysis unit can analyze the time of day of abnormal behavior and the timing of earthquake occurrence. In this way, the accuracy of earthquake prediction is improved by analyzing patterns of abnormal animal behavior in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data into a generative AI and have the generative AI perform an analysis of abnormal behavior patterns.

[0076] The analysis unit can analyze animal activity data in combination with other environmental data (temperature, humidity, etc.). For example, the analysis unit can analyze the relationship between temperature changes and animal activity levels. For example, the analysis unit can analyze the relationship between humidity changes and abnormal animal behavior. For example, the analysis unit can analyze the relationship between environmental data and the health status of animals. By combining animal activity data with other environmental data, more accurate earthquake prediction becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input animal activity data and environmental data into a generative AI and have the generative AI perform a combined analysis of the data.

[0077] The analysis unit can estimate the user's emotions and adjust the timing of notification of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can quickly notify the user of the analysis results. For example, if the user is feeling at ease, the analysis unit can periodically notify the user of the analysis results. For example, if the user is excited, the analysis unit can notify the user of the analysis results in real time. This allows for notifications to be made at the optimal time for the user by adjusting the timing of notification of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the notification timing.

[0078] The analysis unit can compare abnormal behavior data of animals with data of other animal species to identify common earthquake precursor patterns. For example, the analysis unit can compare abnormal behavior data of cows and dogs to identify common earthquake precursor patterns. For example, the analysis unit can compare abnormal behavior data of cats and birds to identify common earthquake precursor patterns. For example, the analysis unit can integrate abnormal behavior data of multiple animal species and analyze earthquake precursor patterns. This allows for the identification of common earthquake precursor patterns by comparing abnormal behavior data of animals with data of other animal species. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal behavior data of different animal species into a generative AI and have the generative AI identify common earthquake precursor patterns.

[0079] The analysis unit can analyze animal health status data and use it for both earthquake prediction and health management. For example, the analysis unit can improve the accuracy of earthquake prediction based on animal health status data. For example, the analysis unit can analyze animal health status data and identify the causes of abnormal behavior. For example, the analysis unit can utilize animal health status data to help with both earthquake prediction and health management. Thus, by analyzing animal health status data, it is possible to use it for both earthquake prediction and health management. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input animal health status data into a generative AI and have the generative AI perform analysis to help with both earthquake prediction and health management.

[0080] The notification unit can estimate the user's emotions and adjust the content and presentation of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can provide detailed notification content. For example, if the user is feeling at ease, the notification unit can provide concise notification content. For example, if the user is excited, the notification unit can provide visually easy-to-understand notification content. In this way, by adjusting the content and presentation of notifications based on the user's emotions, notifications that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI adjust the content and presentation of notifications.

[0081] The notification unit can select the optimal notification method by considering the user's current location information when sending an abnormality notification. For example, if the user is outdoors, the notification unit can send a push notification to the smartphone. For example, if the user is at home, the notification unit can send a notification via a smart speaker. For example, if the user is in a car, the notification unit can send a notification via the car navigation system. By selecting the optimal notification method by considering the user's current location information, the user can receive notifications in the most optimal way. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's location information data into a generating AI and have the generating AI select the optimal notification method.

[0082] The notification unit can determine the priority of notifications by referring to past notification history when sending an anomaly notification. For example, the notification unit can prioritize sending important notifications to users who have frequently received anomaly notifications in the past. For example, the notification unit can send detailed notifications to users who have never received anomaly notifications in the past. For example, the notification unit can adjust the priority of notifications according to the user's level of interest based on past notification history. This allows users to receive important notifications preferentially by determining the priority of notifications by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the determination of notification priorities.

[0083] The notification unit can estimate the user's emotions and adjust the notification frequency based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can increase the notification frequency to provide reassurance. For example, if the user is feeling at ease, the notification unit can decrease the notification frequency to reduce stress. For example, if the user is excited, the notification unit can prioritize real-time notifications. This allows the user to receive notifications at the optimal frequency by adjusting the notification frequency based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification frequency.

[0084] The notification unit can select the optimal notification method when sending an abnormality notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. For example, if the user is using a tablet, the notification unit can send a notification optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit can send a concise and highly visible notification. By selecting the optimal notification method considering the user's device information, the user can receive notifications in the most optimal way. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0085] The notification unit can send notifications to the user's social media accounts when an anomaly notification is sent. This allows the user to receive notifications on the platform they use. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media account information into a generating AI and have the generating AI perform the notification linking.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can automatically adjust the sensor sensitivity in response to changes in the animal's physical condition and environment when collecting animal sensor information. For example, if an animal becomes ill, the sensor sensitivity can be increased to collect more detailed data. Similarly, if the temperature or humidity of the environment changes rapidly, the sensor sensitivity can be adjusted to maintain data accuracy. Furthermore, if an animal is experiencing stress, the sensor sensitivity can be appropriately adjusted to minimize the effects of stress. As a result, the data collection unit can collect more accurate data by automatically adjusting the sensor sensitivity in response to changes in the animal's physical condition and environment.

[0088] The analysis unit can identify patterns in abnormal animal behavior by comparing it with past data. For example, by referring to data on abnormal behavior exhibited by the same animal in the past and comparing it with current abnormal behavior, earthquake precursors can be detected more accurately. Furthermore, by comparing it with abnormal behavior data from other animals, common abnormal behavior patterns can be identified. In addition, by analyzing the frequency and duration of abnormal behavior, the probability of an earthquake occurring can be predicted. In this way, the analysis unit can improve the accuracy of earthquake prediction by identifying patterns in abnormal behavior by comparing it with past data.

[0089] The notification unit can adjust the timing of abnormal notifications, taking the user's schedule into consideration. For example, it can delay notifications if the user is in a meeting or sleeping. It can also prioritize notifications if the user has an important appointment. Furthermore, it can adjust the content and format of notifications based on the user's schedule. As a result, the notification unit can adjust the timing of notifications to suit the user's schedule, ensuring that users receive notifications at the most opportune time.

[0090] The data collection unit can adjust the data collection range considering the animal's range of movement when collecting animal sensor information. For example, if the animal moves over a wide area, the data collection range can be expanded to collect more detailed data. Conversely, if the animal stays in a specific area, the data collection range can be narrowed to collect data more efficiently. Furthermore, the placement and settings of the sensors can be optimized according to the animal's range of movement. As a result, the data collection unit can efficiently and accurately collect data by adjusting the data collection range considering the animal's range of movement.

[0091] The analysis unit can identify the location of abnormal animal behavior when analyzing abnormal animal behavior data. For example, it can display the locations where animals exhibited abnormal behavior on a map to pinpoint the location of the abnormal behavior. Furthermore, by comparing the locations of abnormal behavior with the locations of earthquakes, it is possible to detect earthquake precursors more accurately. In addition, it is possible to predict the probability of an earthquake occurring based on the locations of abnormal behavior. In this way, the analysis unit can improve the accuracy of earthquake prediction by identifying the locations of abnormal behavior.

[0092] The data collection unit can estimate the animal's emotions when collecting sensor information from the animal and adjust the data collection method based on the estimated emotions. For example, if the animal is stressed, the frequency of data collection can be increased to collect more detailed data. Conversely, if the animal is relaxed, the frequency of data collection can be reduced to conserve battery power. Furthermore, if the animal is excited, real-time data collection can be prioritized. In this way, the data collection unit can efficiently and accurately collect data by adjusting the data collection method based on the animal's emotions.

[0093] The analysis unit can estimate the animal's emotions when analyzing abnormal animal behavior data and adjust the analysis method based on the estimated emotions. For example, if the animal is stressed, a detailed analysis can be performed to identify the cause of the abnormal behavior. If the animal is relaxed, a concise analysis can be performed to identify patterns of abnormal behavior. Furthermore, if the animal is excited, real-time analysis can be prioritized. In this way, the analysis unit can perform efficient and accurate analysis by adjusting the analysis method based on the animal's emotions.

[0094] The notification unit can estimate the user's emotions when sending an abnormality notification and adjust the content and format of the notification based on the estimated emotions. For example, if the user is feeling anxious, it can provide detailed notification content to reassure them. If the user is feeling at ease, it can provide concise notification content to reduce stress. Furthermore, if the user is excited, it can provide visually easy-to-understand notification content. In this way, the notification unit can provide notifications that are easy for the user to understand by adjusting the content and format of the notification based on the user's emotions.

[0095] The data collection unit can estimate the animal's emotions when collecting sensor information from the animal and determine the priority of data collection based on the estimated emotions. For example, if the animal is feeling anxious, abnormal behavior data can be prioritized for collection. If the animal is at ease, normal activity data can be prioritized for collection. Furthermore, if the animal is excited, real-time data collection can be prioritized. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data collection based on the animal's emotions.

[0096] The analysis unit can estimate the animal's emotions when analyzing abnormal animal behavior data, and adjust the timing of notification of analysis results based on the estimated emotions. For example, if the animal is feeling anxious, the analysis results can be notified quickly. If the animal is feeling at ease, the analysis results can be notified periodically. Furthermore, if the animal is excited, the analysis results can be notified in real time. In this way, the analysis unit can adjust the timing of notification of analysis results based on the animal's emotions, enabling notifications to be delivered at the optimal time for the user.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects data from sensors attached to the animal. The data collection unit uses sensors that detect the animal's activity level and abnormal behavior, for example. The data collection unit can collect data using GPS sensors, accelerometers, heart rate sensors, etc. The data collection unit collects data such as the animal's location, activity level, and heart rate. The data collection unit can measure the animal's activity level and set thresholds for detecting abnormal behavior. The data collection unit can analyze the animal's behavior patterns in order to detect abnormal behavior. Step 2: The analysis unit analyzes the data collected by the collection unit and translates the information necessary for earthquake prediction into language. The analysis unit can analyze the data using data preprocessing methods and analysis algorithms. The analysis unit analyzes abnormal animal behavior and changes in activity levels to detect earthquake precursors. The analysis unit can analyze patterns of abnormal behavior and rapid changes in activity levels. The analysis unit can use generative AI to analyze abnormal animal behavior and changes in activity levels to detect earthquake precursors. The analysis unit uses generative AI to analyze abnormal animal behavior and changes in activity levels and executes algorithms to detect earthquake precursors. Step 3: The notification unit sends an anomaly notification based on the information obtained by the analysis unit. The notification unit can send an anomaly notification to a smartphone. The notification unit can use text, audio, images, etc., as the format of the anomaly notification. The notification unit can send anomaly notifications by means of email, SMS, push notifications, etc. The notification unit can include information about earthquake precursors as the content of the anomaly notification. The notification unit can use devices such as smartphones, tablets, and PCs as means of sending anomaly notifications.

[0099] 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.

[0100] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the sensors of the smart device 14 to detect animal activity levels and abnormal behavior and collect data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and translates the information necessary for earthquake prediction into language. The notification unit is implemented in the control unit 46A of the smart device 14, which sends an anomaly notification to a smartphone based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0106] 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.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0108] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the sensors of the smart glasses 214 to detect the activity level and abnormal behavior of animals and collect data. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and translates the information necessary for earthquake prediction into language. The notification unit is implemented, for example, in the control unit 46A of the smart glasses 214, which sends an abnormality notification to a smartphone based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0124] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] 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.

[0126] 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.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the sensors of the headset terminal 314 to detect animal activity levels and abnormal behavior and collect data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected data and translate the information necessary for earthquake prediction into language. The notification unit is implemented in the control unit 46A of the headset terminal 314, for example, to send an abnormality notification to a smartphone based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] 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.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the sensors of the robot 414 to detect the activity level and abnormal behavior of animals and collect data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and translates the information necessary for earthquake prediction into language. The notification unit is implemented, for example, by the control unit 46A of the robot 414, which sends an abnormality notification to a smartphone based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] Figure 9 shows the 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.

[0154] 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.

[0155] 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.

[0156] 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, and motorcycles, 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.

[0157] 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."

[0158] 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.

[0159] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] 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 other things 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.

[0169] 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.

[0170] (Note 1) A data collection unit that collects data from sensors attached to animals, The data collected by the aforementioned collection unit is analyzed by an analysis unit, and the information necessary for earthquake prediction is expressed in language. The system includes a notification unit that transmits an abnormality notification based on the information obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is This system uses sensors to detect animal activity levels and abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyzing abnormal animal behavior and changes in activity levels to detect earthquake precursors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Send an abnormality notification to your smartphone The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We generate earthquake prediction reports and provide them to local governments and companies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system measures the animal's activity level and sleep patterns, and notifies the owner or veterinarian if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of sensor data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Optimize the timing of data collection based on the animal's biological rhythms. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Data is collected by combining different sensors depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We collect data to compare abnormal behaviors across different regions, taking into account the geographical location of animals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system monitors the animals' health in real time and increases the data collection frequency if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Detailed analysis of abnormal animal behavior patterns improves the accuracy of earthquake prediction. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Analyze animal activity data in combination with other environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of notification of analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, By comparing abnormal animal behavior data with that of other animal species, we can identify common earthquake prediction patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Analyzing animal health data can be used for both earthquake prediction and health management. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system estimates the user's emotions and adjusts the content and presentation of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending an anomaly notification, the system selects the most appropriate notification method, taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending an anomaly notification, the system prioritizes notifications by referring to past notification history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and adjusts the frequency of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending an anomaly notification, the system selects the optimal notification method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending an anomaly notification, the system will also send notifications to the user's social media accounts. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from sensors attached to animals, The data collected by the aforementioned collection unit is analyzed by an analysis unit, and the information necessary for earthquake prediction is expressed in language. The system includes a notification unit that transmits an abnormality notification based on the information obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is This system uses sensors to detect animal activity levels and abnormal behavior. The system according to feature 1.

3. The aforementioned analysis unit, Analyzing abnormal animal behavior and changes in activity levels to detect earthquake precursors. The system according to feature 1.

4. The aforementioned notification unit, Send an abnormality notification to your smartphone The system according to feature 1.

5. The aforementioned analysis unit, We generate earthquake prediction reports and provide them to local governments and companies. The system according to feature 1.

6. The aforementioned collection unit is The system measures the animal's activity level and sleep patterns, and notifies the owner or veterinarian if any abnormalities are detected. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of sensor data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Optimize the timing of data collection based on the animal's biological rhythms. The system according to feature 1.

9. The aforementioned collection unit is Data is collected by combining different sensors depending on the animal species and individual differences. The system according to feature 1.

Images