System

Abstract

An object of the present embodiment is to analyze animal behavior data and provide earthquake prediction information. A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from a sensor worn on an animal. The analysis unit analyzes the data collected by the collection unit. The generation unit generates prediction information on the basis of the analysis result obtained by the analysis unit. The provision unit provides the prediction information generated by the generation unit.

Description

TECHNICAL FIELD

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

[0002] Patent Literature 1 discloses a personalized chatbot control method executed by at least one processor, the method including: a step of receiving a user utterance; a step of adding the user utterance to a prompt containing an instruction of a description related to a chatbot character; a step of encoding the prompt; and a step of inputting the encoded prompt to a language model to generate a chatbot utterance in response to the user utterance.

[0003] Patent Literature 1: Japanese Patent Application Publication No. 2022-180282 SUMMARY

[0004] In the related art, the behavior data of animals has not been sufficiently utilized for earthquake prediction, and there is room for improvement.

[0005] The system of the present technology aims to analyze the behavior data of animals and provide earthquake prediction information.

[0006] The system of the present technology includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from a sensor attached to an animal. The analysis unit analyzes the data collected by the collection unit. The generation unit generates prediction information based on the analysis result obtained by the analysis unit. The provision unit provides the prediction information generated by the generation unit.

[0007] The system of the present technology can analyze the behavior data of animals and provide earthquake prediction information. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment.

[0009] Figure 2 is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment.

[0010] Figure 3 is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment.

[0011] Figure 4 is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment.

[0012] Figure 5 is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment.

[0013] Figure 6This is a conceptual diagram illustrating an example of the main functions of the data processing device and head-mounted terminal according to the third embodiment.

[0014] Figure 7 This is a conceptual diagram illustrating an example of the data processing system configuration in the fourth embodiment.

[0015] Figure 8 This is a conceptual diagram illustrating an example of the functions of the main parts of the data processing device and robot according to the fourth embodiment.

[0016] Figure 9 It represents an emotion graph that maps multiple emotions.

[0017] Figure 10 It represents an emotion graph that maps multiple emotions.

[0018] Explanation of reference numerals in the attached figures

[0019] Data processing systems 10, 210, 310, and 410

[0020] 12 Data processing device

[0021] 14 Smart devices

[0022] 214 Smart Glasses

[0023] 314 Head-mounted terminal

[0024] 414 Robot Detailed Implementation

[0025] Hereinafter, an example of an implementation of the system involved in this disclosure will be described with reference to the accompanying drawings.

[0026] First, let's explain the terms used in the following description.

[0027] In the following embodiments, the processor (hereinafter referred to as "processor"), as indicated by the reference numerals, can be a single computing device or a combination of multiple computing devices. Furthermore, a processor can be a single computing device or a combination of multiple computing devices. Examples of computing devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit), etc.

[0028] In the following embodiments, RAM (Random Access Memory), as indicated in the figures, is a memory for temporary storage of information, which is used by the processor as working memory.

[0029] In the following embodiments, the memory, as indicated by the reference numerals, is one or more non-volatile storage devices used to store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), disk (e.g., hard disk), or magnetic tape, etc.

[0030] In the following embodiments, the communication I / F (Interface) with reference numerals is an interface including a communication processor and an antenna, etc. The communication I / F is responsible for communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0031] In the following implementation, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, only B, or a combination of A and B. Furthermore, in this specification, when "and / or" connects more than three items, the same approach as "A and / or B" applies.

[0032] First Implementation Method

[0033] Figure 1 An example of the configuration of the data processing system 10 according to the first embodiment is shown.

[0034] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0035] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the receiver 38, output device 40, and camera 42 are also connected to the bus 52.

[0037] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A sends data representing user input received via the touchscreen 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, a specific processing unit 290 (see reference...) Figure 2 Get the data that represents the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, which present data to the user by outputting data in a user-perceptible format (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0040] Figure 2 An example of the main functions of the data processing device 12 and the smart device 14 is shown.

[0041] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0042] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0043] In the smart device 14, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The specific processing program 60 is used in conjunction with the data processing system 10. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart device 14 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0044] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. Furthermore, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0045] Implementation Method 1

[0046] The earthquake prediction system described in this invention utilizes biological "body sensors" for earthquake prediction. This system digitizes animal sensor information and integrates it with generative AI to accurately express the information the animal uses to predict earthquakes, thereby enabling earthquake prediction. For example, the system monitors the animal's bodily responses in real time using sensors worn on the animal. For instance, the sensors detect behavioral or physiological changes in animals such as dogs and cats when they sense earthquake precursors. This sensor information includes the animal's heart rate, body temperature, and movement patterns. The collected sensor information is then digitized and input into the generative AI. The AI ​​analyzes this data and extracts characteristic patterns that indicate the animal is predicting earthquakes. For example, a specific increase in heart rate or abnormal movement pattern is identified as an earthquake precursor. Based on the extracted patterns, the AI ​​expresses the earthquake prediction information verbally. For example, it generates specific prediction information such as, "Because the dog's heart rate has increased sharply and it is exhibiting abnormal movement, it is determined to be an earthquake precursor." This prediction information is provided to the regional disaster prevention system, providing early warnings of earthquakes. This allows local residents to evacuate early and minimize earthquake damage. Furthermore, the system can also generate revenue through sensor sales or regional contracts. For example, it can sell sensors to animal lovers or areas with high disaster prevention awareness, and provide services that integrate with generative AI to generate income. Thus, a system that uses biological "body sensors" for earthquake prediction, by digitizing animal sensor data and integrating it with generative AI, can accurately predict earthquakes and reduce earthquake damage. Therefore, the earthquake prediction system can accurately predict earthquakes by digitizing animal sensor data and integrating it with generative AI.

[0047] The earthquake prediction system according to this embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from sensors worn on an animal. These sensors include, for example, heart rate sensors, body temperature sensors, and accelerometers, but are not limited to these. The collection unit, for example, uses a heart rate sensor to monitor the animal's heart rate in real time. Furthermore, the collection unit can also use a body temperature sensor to measure the animal's body temperature. Further, the collection unit can also use an accelerometer to detect the animal's movement patterns. For example, the collection unit uses a heart rate sensor to detect changes in the animal's heart rate and collects data when abnormal changes occur. The collection unit can also use a body temperature sensor to detect changes in the animal's body temperature and collect data when abnormal changes occur. Further, the collection unit can also use an accelerometer to detect animal movement patterns and collect data when abnormal movement occurs. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms, for example. Machine learning algorithms include, for example, K-means, random forest, etc., but are not limited to these. The analysis unit uses K-means to cluster the data, and when abnormal clustering is detected, it is determined to be a precursor to an earthquake. The analysis unit can also use random forests to classify the data, identifying anomalies as earthquake precursors. Furthermore, the analysis unit can also use neural networks to analyze the data, identifying abnormal patterns as earthquake precursors. The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The generation unit may, for example, use generative AI to generate prediction information. Generative AI includes, but is not limited to, neural networks and generative models. The generation unit may, for example, use neural networks to generate prediction information based on the analysis results. The generation unit may also use generative models to generate prediction information based on the analysis results. Furthermore, the generation unit may also use generative AI to generate prediction information based on the analysis results. The provision unit provides the prediction information generated by the generation unit. The provision unit may, for example, provide the generated prediction information to a regional disaster prevention system. A regional disaster prevention system includes, for example, alarm systems and evacuation guidance systems, but is not limited to, these. The provision unit may, for example, use alarm systems to notify regional residents of the prediction information. The provision unit may also use evacuation guidance systems to issue evacuation instructions based on the prediction information. Furthermore, the provision unit may also provide the generated prediction information to regional residents through web applications or mobile applications. Therefore, the earthquake prediction system involved in this embodiment can accurately predict earthquakes by digitizing the sensor information of animals and linking it with generative AI.

[0048] The collection unit may include a heart rate sensor, a body temperature sensor, and an accelerometer. The heart rate sensor is used to monitor the animal's heart rate in real time. For example, the heart rate sensor can be mounted on the animal's collar to detect changes in heart rate. The heart rate sensor can also be mounted on the animal's chest to detect changes in heart rate. Furthermore, the heart rate sensor can also be mounted on the animal's ear to detect changes in heart rate. The body temperature sensor is used to measure the animal's body temperature. For example, the body temperature sensor can be mounted on the animal's collar to detect changes in body temperature. The body temperature sensor can also be mounted on the animal's chest to detect changes in body temperature. Furthermore, the body temperature sensor can also be mounted on the animal's ear to detect changes in body temperature. The accelerometer is used to detect the animal's movement patterns. For example, the accelerometer can be mounted on the animal's collar to detect changes in movement. The accelerometer can also be mounted on the animal's chest to detect changes in movement. Furthermore, the accelerometer can also be mounted on the animal's legs to detect changes in movement. By using multiple sensors, detailed monitoring of the animal's bodily responses is possible. Some or all of the above-described processing in the collection unit may be performed using AI, or it may not. For example, the data collection unit can input animal heart rate data into the generative AI to analyze changes in heart rate. It can also input animal body temperature data into the generative AI to analyze changes in body temperature. Furthermore, the data collection unit can input animal movement pattern data into the generative AI to analyze changes in movement.

[0049] The analysis department can utilize machine learning algorithms to analyze the data. Machine learning algorithms are used to analyze collected data. For example, K-means is used to cluster the data. K-means divides the data into multiple clusters and calculates the center of each cluster. Then, each data point is assigned to the nearest cluster. This allows for the identification of patterns in the data. Furthermore, random forests are used to classify the data. Random forests construct multiple decision trees and aggregate the predictions from each decision tree. This improves the classification accuracy of the data. Further, neural networks are used to analyze the data. Neural networks consist of multiple layers, each processing the input data and generating an output. This allows for the identification of complex patterns in the data. For example, the analysis department uses K-means to cluster the collected data and identifies abnormal clusters as earthquake precursors. The analysis department can also use random forests to classify the collected data and identify abnormal classification results as earthquake precursors. Furthermore, the analysis department can also use neural networks to analyze the collected data and identify abnormal patterns as earthquake precursors. By utilizing machine learning algorithms, the accuracy of data analysis can be improved. Some or all of the above-described processing in the parsing unit may be performed using AI, or it may not. For example, the parsing unit can input the collected data into a generative AI, which will then perform data parsing.

[0050] The generation unit can utilize generative AI to generate predictive information. Generative AI is used to generate predictive information based on the analysis results. For example, a neural network is used to generate predictive information based on the analysis results. A neural network consists of multiple layers, each processing the input data and generating an output. Thus, complex patterns in the analysis results can be identified and predictive information can be generated. Furthermore, a generative model is used to generate predictive information based on the analysis results. A generative model is a model that generates new data based on input data and can generate predictive information based on the analysis results. For example, the generation unit uses a neural network to generate predictive information based on the analysis results. The generation unit can also utilize a generative model to generate predictive information based on the analysis results. Further, the generation unit can also utilize generative AI to generate predictive information based on the analysis results. By utilizing generative AI, the accuracy of the predictive information can be improved. Some or all of the above processing in the generation unit can be performed using AI, or not. For example, the generation unit can input the analysis results into the generative AI, which then performs the generation of predictive information.

[0051] The provision department can provide the generated prediction information to the regional disaster prevention system. The regional disaster prevention system includes, for example, an alarm system and an evacuation guidance system. The alarm system is used to notify local residents of the occurrence of an earthquake. For example, the alarm system notifies local residents of the occurrence of an earthquake via voice alerts or text messages. The alarm system can also notify local residents of the occurrence of an earthquake via local television or radio. Furthermore, the alarm system can also notify local residents of the occurrence of an earthquake via a local website or mobile application. The evacuation guidance system is used to guide local residents to safe places when an earthquake occurs. For example, the evacuation guidance system instructs local residents on evacuation routes via voice guidance or text messages. The evacuation guidance system can also instruct local residents on evacuation routes via local television or radio. Furthermore, the evacuation guidance system can also instruct local residents on evacuation routes via a local website or mobile application. By providing the prediction information to the regional disaster prevention system, early warning of earthquakes can be given. Some or all of the above processing in the provision department can be performed using AI, or it can be performed without AI. For example, the provision department can input the generated prediction information into a generative AI, which then executes the method of providing the information to the regional disaster prevention system.

[0052] The data collection unit can analyze past behavioral records of animals to select the optimal sensor placement. For example, if an animal has exhibited abnormal behavior in a specific location in the past, sensors can be concentrated at that location. The placement of heart rate and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if an animal exhibits abnormal behavior during a specific time period, the sensitivity of the sensors can be increased during that time period. For example, the collection unit can analyze animal behavior logs and place sensors at locations where abnormal behavior frequently occurs. The collection unit can also analyze animal location information data and adjust sensor sensitivity during time periods when abnormal behavior is frequent. Furthermore, the collection unit can analyze animal behavior patterns and place sensors at locations and during time periods where abnormal behavior is frequent. By selecting the optimal sensor placement based on the animal's past behavioral records, the accuracy of data collection can be improved. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input animal behavior record data into a generative AI, which can then select the optimal sensor placement.

[0053] The data collection unit can filter sensor data based on the animal's current health status. For example, when the animal is healthy, normal data collection is performed. When the animal is sick, abnormal data can be filtered out to collect accurate data. Furthermore, when the animal is fatigued, the data collection frequency can be adjusted to reduce its workload. For example, the collection unit analyzes the animal's health check data and performs normal data collection when the animal is in good health. The collection unit can also analyze the animal's vital sign data and filter out abnormal data. Furthermore, the collection unit can monitor the animal's health status in real time and adjust the data collection frequency based on fatigue levels. By filtering data based on the animal's health status, accurate data can be collected. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input the animal's health status data into a generative AI, which will then perform data filtering.

[0054] The data collection unit can consider the animal's geographic location information during sensor data collection, prioritizing the collection of highly relevant data. For example, when the animal is in a specific area, data from that area is collected first. When the animal is moving, data along its movement path is also prioritized. Furthermore, when the animal exhibits abnormal behavior at a specific location, data from that location is also prioritized. For instance, the collection unit can analyze the animal's GPS data and prioritize collecting data from that area when the animal is in a specific region. The collection unit can also analyze the animal's movement path data and prioritize collecting data along that path when the animal is moving. Furthermore, the collection unit can analyze the animal's behavioral pattern data and prioritize collecting data from that location when the animal exhibits abnormal behavior at a specific location. By considering the animal's geographic location information, highly relevant data can be prioritized. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input the animal's geographic location information data into a generative AI, which then performs the priority collection of highly relevant data.

[0055] The data collection unit can analyze the pet owner's social media activities during sensor data collection to gather relevant data. For example, when the owner reports unusual behavior in their animal on social media, data related to that behavior is collected. Data related to the owner's participation in specific activities can also be collected. Furthermore, data can be collected based on the owner's sharing of the animal's health status on social media. For example, the collection unit can analyze the owner's social media posts to collect data related to unusual animal behavior. The collection unit can also analyze the owner's social media activities to collect data related to specific activities. Furthermore, the collection unit can analyze the owner's social media posts to collect data related to the animal's health status. Relevant data can be collected by analyzing the owner's social media activities. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input the owner's social media data into a generative AI, which will then perform the collection of relevant data.

[0056] The analysis unit can improve analysis accuracy by referencing the animal's past data during analysis. For example, it can refer to the animal's past heart rate data when analyzing current data. It can also refer to the animal's past body temperature data. Furthermore, it can refer to the animal's past movement pattern data when analyzing current data. For example, the analysis unit analyzes the animal's past heart rate data and compares it with the current heart rate data. The analysis unit can also analyze the animal's past body temperature data and compare it with the current body temperature data. Furthermore, the analysis unit can analyze the animal's past movement pattern data and compare it with the current movement pattern data. By referring to the animal's past data, analysis accuracy can be improved. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis unit can input the animal's past data into a generative AI, which can then perform the analysis accuracy improvement.

[0057] The analysis unit can employ different analysis methods for different animal species during analysis. For example, when analyzing dog data, it uses an analysis method that considers dog-specific behavioral patterns. The same method can be used when analyzing cat data, and similarly, when analyzing bird data. For instance, when analyzing dog behavior patterns, the analysis unit uses a method that considers dog-specific behavioral patterns. It can also analyze cat behavior patterns using a method that considers cat-specific behavioral patterns. Furthermore, it can analyze bird behavior patterns using a method that considers bird-specific behavioral patterns. By employing different analysis methods for different animal species, the analysis accuracy can be improved. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis unit can input data from different animal species into a generative AI, which then applies different analysis methods.

[0058] The analysis unit can consider the geographical distribution of animals during analysis. For example, when an animal is in a specific area, data from that area is analyzed first. When an animal is moving, data along its movement path is also analyzed first. Furthermore, when an animal exhibits abnormal behavior at a specific location, data from that location is analyzed first. For example, the analysis unit analyzes the animal's GPS data, prioritizing data from that area when the animal is in a specific region. The analysis unit can also analyze the animal's movement path data, prioritizing data along the movement path when the animal is moving. Furthermore, the analysis unit can analyze the animal's behavioral pattern data, prioritizing data from that location when the animal exhibits abnormal behavior. By considering the geographical distribution of animals, more relevant analysis can be performed. Some or all of the above processing in the analysis unit can be performed using AI, or not. For example, the analysis unit can input the animal's geographical distribution data into a generative AI, which will then perform priority analysis of highly relevant data.

[0059] The analysis unit can improve its accuracy by referencing relevant animal literature during analysis. For example, it can refer to the latest research papers on animal behavior, literature on animal physiological changes, and even past research data on animal earthquake prediction. By referencing relevant animal literature, the analysis accuracy can be improved. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis unit can input relevant animal literature data into a generative AI, which will then improve the analysis accuracy.

[0060] The generation unit can improve the accuracy of prediction information generation by referencing the animal's past behavioral patterns. For example, it can refer to the animal's past heart rate data to generate current prediction information. It can also refer to the animal's past body temperature data. Furthermore, it can refer to the animal's past movement pattern data to generate current prediction information. For example, the generation unit analyzes the animal's past heart rate data and compares it with the current heart rate data to generate prediction information. The generation unit can also analyze the animal's past body temperature data and compare it with the current body temperature data to generate prediction information. Furthermore, the generation unit can analyze the animal's past movement pattern data and compare it with the current movement pattern data to generate prediction information. By referring to the animal's past behavioral patterns, the accuracy of the prediction information can be improved. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generation unit can input the animal's past behavioral pattern data into a generative AI, which can then improve the accuracy of the prediction information generation.

[0061] The generation unit can employ different generation algorithms for different animal species when generating predictive information. For example, when generating predictive information based on dog data, a generation algorithm that considers dog-specific behavioral patterns can be used. Similarly, when generating predictive information based on cat data, a generation algorithm that considers cat-specific behavioral patterns can be used. Furthermore, when generating predictive information based on bird data, a generation algorithm that considers bird-specific behavioral patterns can also be used. For instance, the generation unit analyzes dog behavior patterns and uses a generation algorithm that considers dog-specific behavioral patterns. The generation unit can also analyze cat behavior patterns and use a generation algorithm that considers cat-specific behavioral patterns. Furthermore, the generation unit can also analyze bird behavior patterns and use a generation algorithm that considers bird-specific behavioral patterns. By employing different generation algorithms for different animal species, the accuracy of the predictive information can be improved. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generation unit can input data from different animal species into a generative AI, which can then execute the application of different generation algorithms.

[0062] The generation unit can consider the geographical distribution of animals when generating predictive information. For example, when an animal is in a specific area, predictive information is generated based on data from that area. When an animal is moving, predictive information can also be generated based on data along its movement path. Furthermore, when an animal exhibits abnormal behavior at a specific location, predictive information can also be generated based on data from that location. For example, the generation unit can analyze the animal's GPS data and generate predictive information based on data from that area when the animal is in a specific area. The generation unit can also analyze the animal's movement path data and generate predictive information based on data along the movement path when the animal is moving. Furthermore, the generation unit can also analyze the animal's behavioral pattern data and generate predictive information based on data from that location when the animal exhibits abnormal behavior at a specific location. By considering the geographical distribution of animals, more relevant predictive information can be generated. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generation unit can input the animal's geographical distribution data into a generative AI, which will then prioritize the generation of highly relevant data.

[0063] The generation department can improve the accuracy of prediction information by referencing relevant animal literature. For example, it can refer to recent research papers on animal behavior, literature on animal physiological changes, and even past research data on animal earthquake prediction. By referencing relevant animal literature, the accuracy of the prediction information can be improved. Some or all of the above processing in the generation department can be performed using AI, or it can be performed without AI. For example, the generation department can input relevant animal literature data into a generative AI, which will then improve the accuracy of the prediction information generation.

[0064] The prediction unit can improve accuracy by referencing past earthquake data of the region when providing prediction information. For example, it can provide current prediction information based on past earthquake data of the region. It can also improve the reliability of the prediction information by referencing past earthquake data of the region. Furthermore, it can adjust the way prediction information is provided based on past earthquake data of the region. For example, the prediction unit can analyze past earthquake records of the region and reflect them in the current prediction information. The prediction unit can also analyze past seismic intensity data of the region to improve the reliability of the prediction information. Furthermore, the prediction unit can adjust the way prediction information is provided based on past earthquake data of the region. By referencing past earthquake data of the region, the reliability of the prediction information can be improved. Some or all of the above processing in the prediction unit can be performed using AI, or it can be performed without AI. For example, the prediction unit can input past earthquake data of the region into a generative AI, which can then improve the accuracy of the prediction information provided.

[0065] The provision department can employ different delivery methods for different regional disaster prevention systems when providing forecast information. For example, when a regional disaster prevention system uses SMS notifications, forecast information is provided via SMS. When a regional disaster prevention system uses application notifications, forecast information can also be provided via application notifications. Furthermore, when a regional disaster prevention system uses email notifications, forecast information can also be provided via email. For instance, the provision department can analyze the type of regional disaster prevention system and provide forecast information via SMS when using SMS notifications. The provision department can also provide forecast information via application notifications when a regional disaster prevention system uses application notifications. Furthermore, the provision department can also provide forecast information via email when a regional disaster prevention system uses email notifications. By employing different delivery methods based on the regional disaster prevention system, information can be provided more effectively. Some or all of the above processing in the provision department can be performed using AI, or it can be performed without AI. For example, the provision department can input regional disaster prevention system data into a generative AI, which can then execute the application of different delivery methods.

[0066] The information provision department can consider the geographical characteristics of a region when providing forecast information. For example, if the region is mountainous, the forecast information can consider the impact of earthquakes. If the region is urban, the forecast information can also consider building density. Furthermore, if the region is coastal, the forecast information can also consider tsunami risk. For example, the information provision department can analyze the region's topographic data and provide forecast information considering the impact of earthquakes when the region is mountainous. The information provision department can also analyze the region's population distribution data and provide forecast information considering building density when the region is urban. Furthermore, the information provision department can also analyze the region's coastal data and provide forecast information considering tsunami risk. By considering the region's geographical characteristics, more relevant information can be provided. Some or all of the above processing in the information provision department can be performed using AI, or it can be performed without AI. For example, the information provision department can input the region's geographical characteristic data into a generative AI, which will then provide highly relevant information.

[0067] The information provision department can improve the accuracy of prediction information by referencing relevant literature in the region. For example, it can refer to the latest research papers related to earthquakes in the region. It can also refer to literature related to disaster prevention in the region. Furthermore, it can refer to past earthquake data in the region. By referencing relevant literature in the region, the accuracy of the prediction information can be improved. Some or all of the above processing in the information provision department can be performed using AI, or it can be performed without AI. For example, the information provision department can input relevant literature data from the region into a generative AI, which will then improve the accuracy of the prediction information.

[0068] The system described in this embodiment is not limited to the examples above. For example, various modifications can be made as follows.

[0069] The data collection unit can analyze the animal's past behavioral records to select the optimal sensor placement. For example, if an animal has exhibited abnormal behavior in a specific location in the past, sensors can be concentrated at that location. The placement of heart rate and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if an animal exhibits abnormal behavior during a specific time period, the sensitivity of the sensors can be increased during that time period. By selecting the optimal sensor placement based on the animal's past behavioral records, the accuracy of data collection can be improved.

[0070] The data collection unit can filter sensor data based on the animal's current health status. For example, normal data collection is performed when the animal is healthy. When the animal is sick, abnormal data can be filtered out to collect accurate data. Furthermore, when the animal is fatigued, the data collection frequency can be adjusted to reduce its workload. By filtering data according to the animal's health status, accurate data can be collected.

[0071] The data collection unit can consider the animal's geographical location information when collecting sensor data, prioritizing the collection of highly relevant data. For example, when the animal is in a specific area, data from that area can be collected first. When the animal is moving, data along its movement path can also be collected first. Furthermore, when the animal exhibits abnormal behavior at a specific location, data from that location can be collected first. By considering the animal's geographical location information, highly relevant data can be prioritized for collection.

[0072] The analysis unit can refer to the animal's past data during analysis to improve accuracy. For example, it can refer to the animal's past heart rate data when analyzing the current data. It can also refer to the animal's past body temperature data when analyzing the current data. Furthermore, it can refer to the animal's past movement pattern data when analyzing the current data. By referring to the animal's past data, the analysis accuracy can be improved.

[0073] The analysis department can employ different analysis methods for different animal species during the analysis process. For example, when analyzing dog data, an analysis method that considers dog-specific behavioral patterns can be used. The same method can be used when analyzing cat data, and similarly, when analyzing bird data, a method that considers bird-specific behavioral patterns can be employed. By using different analysis methods for different animal species, the accuracy of the analysis can be improved.

[0074] The analysis unit can consider the geographical distribution of animals during analysis. For example, if an animal is in a specific area, data from that area can be analyzed first. If an animal is moving, data along its movement path can also be analyzed first. Furthermore, if an animal exhibits unusual behavior at a specific location, data from that location can be analyzed first. By considering the geographical distribution of animals, more relevant analysis can be performed.

[0075] The following is a brief description of the processing flow of Implementation Method 1.

[0076] Step 1: The data collection unit gathers data from sensors attached to the animal. These sensors include heart rate sensors, body temperature sensors, and accelerometers. The data collection unit uses the heart rate sensor to monitor the animal's heart rate in real time, the body temperature sensor to measure the animal's body temperature, and the accelerometer to detect the animal's movement patterns. Data is collected when abnormal changes occur.

[0077] Step 2: The analysis department analyzes the data collected by the collection department. The analysis department uses machine learning algorithms (such as K-means, random forest, neural networks) to analyze the data, and judges abnormal clustering, classification results, or patterns as earthquake precursors.

[0078] Step 3: The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The generation unit uses generative AI (e.g., neural networks, generative models) to generate prediction information based on the analysis results.

[0079] Step 4: The Provision Department provides the forecast information generated by the Generation Department. The Provision Department provides the generated forecast information to the regional disaster prevention system (e.g., alarm system, evacuation guidance system) and notifies regional residents. It can also be provided to regional residents via web applications or mobile applications.

[0080] Implementation Method 2

[0081] The earthquake prediction system described in this invention utilizes biological "body sensors" for earthquake prediction. This system digitizes animal sensor information and integrates it with generative AI to accurately express the information the animal uses to predict earthquakes, thereby enabling earthquake prediction. For example, the system monitors the animal's bodily responses in real time using sensors worn on the animal. For instance, the sensors detect behavioral or physiological changes in animals such as dogs and cats when they sense earthquake precursors. This sensor information includes the animal's heart rate, body temperature, and movement patterns. The collected sensor information is then digitized and input into the generative AI. The AI ​​analyzes this data and extracts characteristic patterns that indicate the animal is predicting earthquakes. For example, a specific increase in heart rate or abnormal movement pattern is identified as an earthquake precursor. Based on the extracted patterns, the AI ​​expresses the earthquake prediction information verbally. For example, it generates specific prediction information such as, "Because the dog's heart rate has increased sharply and it is exhibiting abnormal movement, it is determined to be an earthquake precursor." This prediction information is provided to the regional disaster prevention system, providing early warnings of earthquakes. This allows local residents to evacuate early and minimize earthquake damage. Furthermore, the system can also generate revenue through sensor sales or regional contracts. For example, it can sell sensors to animal lovers or areas with high disaster prevention awareness, and provide services that integrate with generative AI to generate income. Thus, a system that uses biological "body sensors" for earthquake prediction, by digitizing animal sensor data and integrating it with generative AI, can accurately predict earthquakes and reduce earthquake damage. Therefore, the earthquake prediction system can accurately predict earthquakes by digitizing animal sensor data and integrating it with generative AI.

[0082] The earthquake prediction system according to this embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from sensors worn on an animal. These sensors include, for example, heart rate sensors, body temperature sensors, and accelerometers, but are not limited to these. The collection unit, for example, uses a heart rate sensor to monitor the animal's heart rate in real time. Furthermore, the collection unit can also use a body temperature sensor to measure the animal's body temperature. Further, the collection unit can also use an accelerometer to detect the animal's movement patterns. For example, the collection unit uses a heart rate sensor to detect changes in the animal's heart rate and collects data when abnormal changes occur. The collection unit can also use a body temperature sensor to detect changes in the animal's body temperature and collect data when abnormal changes occur. Further, the collection unit can also use an accelerometer to detect animal movement patterns and collect data when abnormal movement occurs. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms, for example. Machine learning algorithms include, for example, K-means, random forest, etc., but are not limited to these. The analysis unit uses K-means to cluster the data, and when abnormal clustering is detected, it is determined to be a precursor to an earthquake. The analysis unit can also use random forests to classify the data, identifying anomalies as earthquake precursors. Furthermore, the analysis unit can also use neural networks to analyze the data, identifying abnormal patterns as earthquake precursors. The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The generation unit may, for example, use generative AI to generate prediction information. Generative AI includes, but is not limited to, neural networks and generative models. The generation unit may, for example, use neural networks to generate prediction information based on the analysis results. The generation unit may also use generative models to generate prediction information based on the analysis results. Furthermore, the generation unit may also use generative AI to generate prediction information based on the analysis results. The provision unit provides the prediction information generated by the generation unit. The provision unit may, for example, provide the generated prediction information to a regional disaster prevention system. A regional disaster prevention system includes, for example, alarm systems and evacuation guidance systems, but is not limited to, these. The provision unit may, for example, use alarm systems to notify regional residents of the prediction information. The provision unit may also use evacuation guidance systems to issue evacuation instructions based on the prediction information. Furthermore, the provision unit may also provide the generated prediction information to regional residents through web applications or mobile applications. Therefore, the earthquake prediction system involved in this embodiment can accurately predict earthquakes by digitizing the sensor information of animals and linking it with generative AI.

[0083] The collection unit may include a heart rate sensor, a body temperature sensor, and an accelerometer. The heart rate sensor is used to monitor the animal's heart rate in real time. For example, the heart rate sensor can be mounted on the animal's collar to detect changes in heart rate. The heart rate sensor can also be mounted on the animal's chest to detect changes in heart rate. Furthermore, the heart rate sensor can also be mounted on the animal's ear to detect changes in heart rate. The body temperature sensor is used to measure the animal's body temperature. For example, the body temperature sensor can be mounted on the animal's collar to detect changes in body temperature. The body temperature sensor can also be mounted on the animal's chest to detect changes in body temperature. Furthermore, the body temperature sensor can also be mounted on the animal's ear to detect changes in body temperature. The accelerometer is used to detect the animal's movement patterns. For example, the accelerometer can be mounted on the animal's collar to detect changes in movement. The accelerometer can also be mounted on the animal's chest to detect changes in movement. Furthermore, the accelerometer can also be mounted on the animal's legs to detect changes in movement. By using multiple sensors, detailed monitoring of the animal's bodily responses is possible. Some or all of the above-described processing in the collection unit may be performed using AI, or it may not. For example, the data collection unit can input animal heart rate data into the generative AI to analyze changes in heart rate. It can also input animal body temperature data into the generative AI to analyze changes in body temperature. Furthermore, the data collection unit can input animal movement pattern data into the generative AI to analyze changes in movement.

[0084] The analysis department can utilize machine learning algorithms to analyze the data. Machine learning algorithms are used to analyze collected data. For example, K-means is used to cluster the data. K-means divides the data into multiple clusters and calculates the center of each cluster. Then, each data point is assigned to the nearest cluster. This allows for the identification of patterns in the data. Furthermore, random forests are used to classify the data. Random forests construct multiple decision trees and aggregate the predictions from each decision tree. This improves the classification accuracy of the data. Further, neural networks are used to analyze the data. Neural networks consist of multiple layers, each processing the input data and generating an output. This allows for the identification of complex patterns in the data. For example, the analysis department uses K-means to cluster the collected data and identifies abnormal clusters as earthquake precursors. The analysis department can also use random forests to classify the collected data and identify abnormal classification results as earthquake precursors. Furthermore, the analysis department can also use neural networks to analyze the collected data and identify abnormal patterns as earthquake precursors. By utilizing machine learning algorithms, the accuracy of data analysis can be improved. Some or all of the above-described processing in the parsing unit may be performed using AI, or it may not. For example, the parsing unit can input the collected data into a generative AI, which will then perform data parsing.

[0085] The generation unit can utilize generative AI to generate predictive information. Generative AI is used to generate predictive information based on the analysis results. For example, a neural network is used to generate predictive information based on the analysis results. A neural network consists of multiple layers, each processing the input data and generating an output. Thus, complex patterns in the analysis results can be identified and predictive information can be generated. Furthermore, a generative model is used to generate predictive information based on the analysis results. A generative model is a model that generates new data based on input data and can generate predictive information based on the analysis results. For example, the generation unit uses a neural network to generate predictive information based on the analysis results. The generation unit can also utilize a generative model to generate predictive information based on the analysis results. Further, the generation unit can also utilize generative AI to generate predictive information based on the analysis results. By utilizing generative AI, the accuracy of the predictive information can be improved. Some or all of the above processing in the generation unit can be performed using AI, or not. For example, the generation unit can input the analysis results into the generative AI, which then performs the generation of predictive information.

[0086] The provision department can provide the generated prediction information to the regional disaster prevention system. The regional disaster prevention system includes, for example, an alarm system and an evacuation guidance system. The alarm system is used to notify local residents of the occurrence of an earthquake. For example, the alarm system notifies local residents of the occurrence of an earthquake via voice alerts or text messages. The alarm system can also notify local residents of the occurrence of an earthquake via local television or radio. Furthermore, the alarm system can also notify local residents of the occurrence of an earthquake via a local website or mobile application. The evacuation guidance system is used to guide local residents to safe places when an earthquake occurs. For example, the evacuation guidance system instructs local residents on evacuation routes via voice guidance or text messages. The evacuation guidance system can also instruct local residents on evacuation routes via local television or radio. Furthermore, the evacuation guidance system can also instruct local residents on evacuation routes via a local website or mobile application. By providing the prediction information to the regional disaster prevention system, early warning of earthquakes can be given. Some or all of the above processing in the provision department can be performed using AI, or it can be performed without AI. For example, the provision department can input the generated prediction information into a generative AI, which then executes the method of providing the information to the regional disaster prevention system.

[0087] The data collection unit can infer the animal's emotions and adjust the sensor sensitivity based on these inferred emotions. For example, when the animal is excited, the sensitivity of the heart rate sensor is increased to collect detailed data. When the animal is relaxed, the sensitivity of the body temperature sensor can be adjusted to detect minute changes. Furthermore, when the animal is anxious, the sensitivity of the accelerometer sensor can be increased to record movement patterns in detail. For instance, the data collection unit analyzes the animal's heart rate data and increases the sensor sensitivity when there are significant changes in heart rate. The data collection unit can also analyze the animal's body temperature data and adjust the sensor sensitivity when there are significant changes in body temperature. Furthermore, the data collection unit can analyze the animal's movement pattern data and increase the sensor sensitivity when there are significant changes in movement. By adjusting the sensor sensitivity based on the animal's emotions, more accurate data can be collected. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing in the collection department may be performed using AI, or it may not. For example, the collection department can input animal emotional data into a generative AI, which will then perform emotion-based sensor sensitivity adjustments.

[0088] The data collection unit can analyze past behavioral records of animals to select the optimal sensor placement. For example, if an animal has exhibited abnormal behavior in a specific location in the past, sensors can be concentrated at that location. The placement of heart rate and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if an animal exhibits abnormal behavior during a specific time period, the sensitivity of the sensors can be increased during that time period. For example, the collection unit can analyze animal behavior logs and place sensors at locations where abnormal behavior frequently occurs. The collection unit can also analyze animal location information data and adjust sensor sensitivity during time periods when abnormal behavior is frequent. Furthermore, the collection unit can analyze animal behavior patterns and place sensors at locations and during time periods where abnormal behavior is frequent. By selecting the optimal sensor placement based on the animal's past behavioral records, the accuracy of data collection can be improved. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input animal behavior record data into a generative AI, which can then select the optimal sensor placement.

[0089] The data collection unit can filter sensor data based on the animal's current health status. For example, when the animal is healthy, normal data collection is performed. When the animal is sick, abnormal data can be filtered out to collect accurate data. Furthermore, when the animal is fatigued, the data collection frequency can be adjusted to reduce its workload. For example, the collection unit analyzes the animal's health check data and performs normal data collection when the animal is in good health. The collection unit can also analyze the animal's vital sign data and filter out abnormal data. Furthermore, the collection unit can monitor the animal's health status in real time and adjust the data collection frequency based on fatigue levels. By filtering data based on the animal's health status, accurate data can be collected. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input the animal's health status data into a generative AI, which will then perform data filtering.

[0090] The data collection unit can infer the animal's emotions and determine the priority of data collection based on the inferred emotions. For example, when the animal is excited, heart rate data is collected first. When the animal is relaxed, body temperature data can also be collected first. Furthermore, when the animal is anxious, movement pattern data can be collected first. For example, the data collection unit analyzes the animal's heart rate data and prioritizes data collection when heart rate changes are significant. The data collection unit can also analyze the animal's body temperature data and prioritize data collection when body temperature changes are significant. Furthermore, the data collection unit can also analyze the animal's movement pattern data and prioritize data collection when movement patterns change significantly. By prioritizing data based on the animal's emotions, important data can be collected first. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the data collection unit can be performed using AI, or it can be performed without AI. For example, the data collection department can input animal emotional data into a generative AI, which will then prioritize the data.

[0091] The data collection unit can consider the animal's geographic location information during sensor data collection, prioritizing the collection of highly relevant data. For example, when the animal is in a specific area, data from that area is collected first. When the animal is moving, data along its movement path is also prioritized. Furthermore, when the animal exhibits abnormal behavior at a specific location, data from that location is also prioritized. For instance, the collection unit can analyze the animal's GPS data and prioritize collecting data from that area when the animal is in a specific region. The collection unit can also analyze the animal's movement path data and prioritize collecting data along that path when the animal is moving. Furthermore, the collection unit can analyze the animal's behavioral pattern data and prioritize collecting data from that location when the animal exhibits abnormal behavior at a specific location. By considering the animal's geographic location information, highly relevant data can be prioritized. Some or all of the above processing in the collection unit can be performed using AI, or it can be performed without AI. For example, the collection unit can input the animal's geographic location information data into a generative AI, which then performs the priority collection of highly relevant data.

[0092] The data collection unit can analyze the pet owner's social media activities during sensor data collection to gather relevant data. For example, when the owner reports unusual behavior in their animal on social media, data related to that behavior is collected. Data related to the owner's participation in specific activities can also be collected. Furthermore, data can be collected based on the owner's sharing of the animal's health status on social media. For example, the data collection unit can analyze the owner's social media posts to collect data related to unusual animal behavior. The data collection unit can also analyze the owner's social media activities to collect data related to specific activities. Furthermore, the data collection unit can analyze the owner's social media posts to collect data related to the animal's health status. Relevant data can be collected by analyzing the owner's social media activities. Some or all of the above processing in the data collection unit can be performed using AI, or it can be performed without AI. For example, the data collection unit can input the owner's social media data into a generative AI, which will then perform the data collection.

[0093] The analysis unit can infer the animal's emotions and adjust the analysis algorithm based on the inferred emotions. For example, when the animal is excited, the analysis algorithm for heart rate data is adjusted. When the animal is relaxed, the analysis algorithm for body temperature data can also be adjusted. Furthermore, when the animal is anxious, the analysis algorithm for movement pattern data can also be adjusted. For example, the analysis unit analyzes the animal's heart rate data and adjusts the analysis algorithm when the heart rate changes significantly. The analysis unit can also analyze the animal's body temperature data and adjust the analysis algorithm when the body temperature changes significantly. Furthermore, the analysis unit can also analyze the animal's movement pattern data and adjust the analysis algorithm when the movement pattern changes significantly. By adjusting the analysis algorithm based on the animal's emotions, the analysis accuracy can be improved. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis department can input animal emotional data into a generative AI, which will then execute an analysis algorithm to make adjustments.

[0094] The analysis unit can improve analysis accuracy by referencing the animal's past data during analysis. For example, it can refer to the animal's past heart rate data when analyzing current data. It can also refer to the animal's past body temperature data. Furthermore, it can refer to the animal's past movement pattern data when analyzing current data. For example, the analysis unit analyzes the animal's past heart rate data and compares it with the current heart rate data. The analysis unit can also analyze the animal's past body temperature data and compare it with the current body temperature data. Furthermore, the analysis unit can analyze the animal's past movement pattern data and compare it with the current movement pattern data. By referring to the animal's past data, analysis accuracy can be improved. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis unit can input the animal's past data into a generative AI, which can then perform the analysis accuracy improvement.

[0095] The analysis unit can employ different analysis methods for different animal species during analysis. For example, when analyzing dog data, it uses an analysis method that considers dog-specific behavioral patterns. The same method can be used when analyzing cat data, and similarly, when analyzing bird data. For instance, when analyzing dog behavior patterns, the analysis unit uses a method that considers dog-specific behavioral patterns. It can also analyze cat behavior patterns using a method that considers cat-specific behavioral patterns. Furthermore, it can analyze bird behavior patterns using a method that considers bird-specific behavioral patterns. By employing different analysis methods for different animal species, the analysis accuracy can be improved. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis unit can input data from different animal species into a generative AI, which then applies different analysis methods.

[0096] The analysis unit can infer the animal's emotions and adjust the display of the analysis results accordingly. For example, when the animal is excited, heart rate data is highlighted. When the animal is relaxed, body temperature data can be displayed in detail. Furthermore, when the animal is anxious, movement pattern data can be highlighted. For instance, the analysis unit analyzes the animal's heart rate data and highlights the heart rate data when there are significant heart rate fluctuations. The analysis unit can also analyze the animal's body temperature data and display the body temperature data in detail when there are significant body temperature fluctuations. Furthermore, the analysis unit can also analyze the animal's movement pattern data and highlight the movement pattern data when there are significant movement changes. By adjusting the display of the analysis results according to the animal's emotions, a more easily understandable display can be achieved. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis department can input animal emotional data into the generative AI, which will then adjust the way the analysis results are displayed.

[0097] The analysis unit can consider the geographical distribution of animals during analysis. For example, when an animal is in a specific area, data from that area is analyzed first. When an animal is moving, data along its movement path is also analyzed first. Furthermore, when an animal exhibits abnormal behavior at a specific location, data from that location is analyzed first. For example, the analysis unit analyzes the animal's GPS data, prioritizing data from that area when the animal is in a specific region. The analysis unit can also analyze the animal's movement path data, prioritizing data along the movement path when the animal is moving. Furthermore, the analysis unit can analyze the animal's behavioral pattern data, prioritizing data from that location when the animal exhibits abnormal behavior. By considering the geographical distribution of animals, more relevant analysis can be performed. Some or all of the above processing in the analysis unit can be performed using AI, or not. For example, the analysis unit can input the animal's geographical distribution data into a generative AI, which will then perform priority analysis of highly relevant data.

[0098] The analysis unit can improve its accuracy by referencing relevant animal literature during analysis. For example, it can refer to the latest research papers on animal behavior, literature on animal physiological changes, and even past research data on animal earthquake prediction. By referencing relevant animal literature, the analysis accuracy can be improved. Some or all of the above processing in the analysis unit can be performed using AI, or it can be performed without AI. For example, the analysis unit can input relevant animal literature data into a generative AI, which will then improve the analysis accuracy.

[0099] The generation unit can infer the animal's emotions and adjust the generation method of prediction information based on the inferred emotions. For example, when the animal is excited, prediction information is generated based on heart rate data. When the animal is relaxed, prediction information can also be generated based on body temperature data. Furthermore, when the animal is anxious, prediction information can also be generated based on movement pattern data. For example, the generation unit analyzes the animal's heart rate data and generates prediction information when the heart rate changes significantly. The generation unit can also analyze the animal's body temperature data and generate prediction information when the body temperature changes significantly. Furthermore, the generation unit can also analyze the animal's movement pattern data and generate prediction information when the movement pattern changes significantly. By adjusting the generation method of prediction information based on the animal's emotions, more accurate prediction information can be generated. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generative department can input animal emotional data into the generative AI, which will then adjust the way predictive information is generated.

[0100] The generation unit can improve the accuracy of prediction information generation by referencing the animal's past behavioral patterns. For example, it can refer to the animal's past heart rate data to generate current prediction information. It can also refer to the animal's past body temperature data. Furthermore, it can refer to the animal's past movement pattern data to generate current prediction information. For example, the generation unit analyzes the animal's past heart rate data and compares it with the current heart rate data to generate prediction information. The generation unit can also analyze the animal's past body temperature data and compare it with the current body temperature data to generate prediction information. Furthermore, the generation unit can analyze the animal's past movement pattern data and compare it with the current movement pattern data to generate prediction information. By referring to the animal's past behavioral patterns, the accuracy of the prediction information can be improved. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generation unit can input the animal's past behavioral pattern data into a generative AI, which can then improve the accuracy of the prediction information generation.

[0101] The generation unit can employ different generation algorithms for different animal species when generating predictive information. For example, when generating predictive information based on dog data, a generation algorithm that considers dog-specific behavioral patterns can be used. Similarly, when generating predictive information based on cat data, a generation algorithm that considers cat-specific behavioral patterns can be used. Furthermore, when generating predictive information based on bird data, a generation algorithm that considers bird-specific behavioral patterns can also be used. For instance, the generation unit analyzes dog behavior patterns and uses a generation algorithm that considers dog-specific behavioral patterns. The generation unit can also analyze cat behavior patterns and use a generation algorithm that considers cat-specific behavioral patterns. Furthermore, the generation unit can also analyze bird behavior patterns and use a generation algorithm that considers bird-specific behavioral patterns. By employing different generation algorithms for different animal species, the accuracy of the predictive information can be improved. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generation unit can input data from different animal species into a generative AI, which can then execute the application of different generation algorithms.

[0102] The generation unit can infer the animal's emotions and adjust the display of predicted information accordingly. For example, when the animal is excited, heart rate data is highlighted. When the animal is relaxed, body temperature data can be displayed in detail. Furthermore, when the animal is anxious, movement pattern data can be highlighted. For instance, the generation unit analyzes the animal's heart rate data and highlights heart rate data when there are significant changes. The generation unit can also analyze the animal's body temperature data and display body temperature data in detail when there are significant changes. Furthermore, the generation unit can analyze the animal's movement pattern data and highlight movement pattern data when there are significant changes. By adjusting the display of predicted information based on the animal's emotions, a more easily understood display can be achieved. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generative department can input animal emotional data into the generative AI, which will then adjust the way the predicted information is displayed.

[0103] The generation unit can consider the geographical distribution of animals when generating predictive information. For example, when an animal is in a specific area, predictive information is generated based on data from that area. When an animal is moving, predictive information can also be generated based on data along its movement path. Furthermore, when an animal exhibits abnormal behavior at a specific location, predictive information can also be generated based on data from that location. For example, the generation unit can analyze the animal's GPS data and generate predictive information based on data from that area when the animal is in a specific area. The generation unit can also analyze the animal's movement path data and generate predictive information based on data along the movement path when the animal is moving. Furthermore, the generation unit can also analyze the animal's behavioral pattern data and generate predictive information based on data from that location when the animal exhibits abnormal behavior at a specific location. By considering the geographical distribution of animals, more relevant predictive information can be generated. Some or all of the above processing in the generation unit can be performed using AI, or it can be performed without AI. For example, the generation unit can input the animal's geographical distribution data into a generative AI, which will then prioritize the generation of highly relevant data.

[0104] The generation department can improve the accuracy of prediction information by referencing relevant animal literature. For example, it can refer to recent research papers on animal behavior, literature on animal physiological changes, and even past research data on animal earthquake prediction. By referencing relevant animal literature, the accuracy of the prediction information can be improved. Some or all of the above processing in the generation department can be performed using AI, or it can be performed without AI. For example, the generation department can input relevant animal literature data into a generative AI, which will then improve the accuracy of the prediction information generation.

[0105] The information delivery unit can infer the animal's emotions and adjust the way it provides predicted information based on these inferences. For example, when the animal is excited, heart rate data is highlighted. When the animal is relaxed, detailed body temperature data can be provided. Furthermore, when the animal is anxious, movement pattern data can be highlighted. For instance, the information delivery unit analyzes the animal's heart rate data and highlights heart rate data when there are significant heart rate fluctuations. The information delivery unit can also analyze the animal's body temperature data and provide detailed body temperature data when there are significant temperature fluctuations. Furthermore, the information delivery unit can analyze the animal's movement pattern data and highlight movement pattern data when there are significant movement changes. By adjusting the way predicted information is provided based on the animal's emotions, more appropriate information delivery can be achieved. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the information delivery unit can be performed using AI, or it can be performed without AI. For example, the provider can input animal emotional data into a generative AI, which will then adjust the way predictive information is provided.

[0106] The prediction unit can improve accuracy by referencing past earthquake data of the region when providing prediction information. For example, it can provide current prediction information based on past earthquake data of the region. It can also improve the reliability of the prediction information by referencing past earthquake data of the region. Furthermore, it can adjust the way prediction information is provided based on past earthquake data of the region. For example, the prediction unit can analyze past earthquake records of the region and reflect them in the current prediction information. The prediction unit can also analyze past seismic intensity data of the region to improve the reliability of the prediction information. Furthermore, the prediction unit can adjust the way prediction information is provided based on past earthquake data of the region. By referencing past earthquake data of the region, the reliability of the prediction information can be improved. Some or all of the above processing in the prediction unit can be performed using AI, or it can be performed without AI. For example, the prediction unit can input past earthquake data of the region into a generative AI, which can then improve the accuracy of the prediction information provided.

[0107] The provision department can employ different delivery methods for different regional disaster prevention systems when providing forecast information. For example, when a regional disaster prevention system uses SMS notifications, forecast information is provided via SMS. When a regional disaster prevention system uses application notifications, forecast information can also be provided via application notifications. Furthermore, when a regional disaster prevention system uses email notifications, forecast information can also be provided via email. For instance, the provision department can analyze the type of regional disaster prevention system and provide forecast information via SMS when using SMS notifications. The provision department can also provide forecast information via application notifications when a regional disaster prevention system uses application notifications. Furthermore, the provision department can also provide forecast information via email when a regional disaster prevention system uses email notifications. By employing different delivery methods based on the regional disaster prevention system, information can be provided more effectively. Some or all of the above processing in the provision department can be performed using AI, or it can be performed without AI. For example, the provision department can input regional disaster prevention system data into a generative AI, which can then execute the application of different delivery methods.

[0108] The information delivery unit can infer the animal's emotions and adjust the order of information delivery based on these inferred emotions. For example, when the animal is excited, heart rate data is provided first. When the animal is relaxed, body temperature data can also be provided first. Furthermore, when the animal is anxious, movement pattern data can be provided first. For instance, the information delivery unit analyzes the animal's heart rate data and provides heart rate data first when there are significant heart rate fluctuations. The information delivery unit can also analyze the animal's body temperature data and provide body temperature data first when there are significant body temperature fluctuations. Furthermore, the information delivery unit can also analyze the animal's movement pattern data and provide movement pattern data first when there are significant movement changes. By adjusting the order of information delivery based on the animal's emotions, more appropriate information delivery can be achieved. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the information delivery unit can be performed using AI, or it can be performed without AI. For example, the provider can input animal emotional data into a generative AI, which will then perform predictive information to adjust the order of events.

[0109] The information provision department can consider the geographical characteristics of a region when providing forecast information. For example, if the region is mountainous, the forecast information can consider the impact of earthquakes. If the region is urban, the forecast information can also consider building density. Furthermore, if the region is coastal, the forecast information can also consider tsunami risk. For example, the information provision department can analyze the region's topographic data and provide forecast information considering the impact of earthquakes when the region is mountainous. The information provision department can also analyze the region's population distribution data and provide forecast information considering building density when the region is urban. Furthermore, the information provision department can also analyze the region's coastal data and provide forecast information considering tsunami risk. By considering the region's geographical characteristics, more relevant information can be provided. Some or all of the above processing in the information provision department can be performed using AI, or it can be performed without AI. For example, the information provision department can input the region's geographical characteristic data into a generative AI, which will then provide highly relevant information.

[0110] The information provision department can improve the accuracy of prediction information by referencing relevant literature in the region. For example, it can refer to the latest research papers related to earthquakes in the region. It can also refer to literature related to disaster prevention in the region. Furthermore, it can refer to past earthquake data in the region. By referencing relevant literature in the region, the accuracy of the prediction information can be improved. Some or all of the above processing in the information provision department can be performed using AI, or it can be performed without AI. For example, the information provision department can input relevant literature data from the region into a generative AI, which will then improve the accuracy of the prediction information.

[0111] ===Hardware Guarantee 1-1===

[0112] Each of the aforementioned elements—collection unit, analysis unit, generation unit, and provision unit—is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses sensors in the smart device 14 to monitor the animal's heart rate, body temperature, and movement patterns in real time. The analysis unit, for example, is implemented by a specific processing unit 290 of the data processing device 12, which analyzes the collected data. The generation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, which generates predictive information based on the analysis results. The provision unit, for example, is implemented by the control unit 46A of the smart device 14, which provides the generated predictive information to the regional disaster prevention system.

[0113] ===Hardware Guarantee 1-2===

[0114] Each of the aforementioned elements—collection unit, analysis unit, generation unit, and provision unit—is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses sensors in the smart glasses 214 to monitor the animal's heart rate, body temperature, and movement patterns in real time. The analysis unit, for example, is implemented by a specific processing unit 290 of the data processing device 12, which analyzes the collected data. The generation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, which generates predictive information based on the analysis results. The provision unit, for example, is implemented by the control unit 46A of the smart glasses 214, which provides the generated predictive information to the regional disaster prevention system.

[0115] ===Hardware Guarantee 1-3===

[0116] Each of the aforementioned elements—collection unit, analysis unit, generation unit, and provision unit—is implemented, for example, by at least one of the head-mounted terminal 314 and the data processing device 12. For example, the collection unit uses sensors in the head-mounted terminal 314 to monitor the animal's heart rate, body temperature, and movement patterns in real time. The analysis unit, for example, is implemented by a specific processing unit 290 in the data processing device 12, which analyzes the collected data. The generation unit, for example, is implemented by the specific processing unit 290 in the data processing device 12, which generates predictive information based on the analysis results. The provision unit, for example, is implemented by a control unit 46A in the head-mounted terminal 314, which provides the generated predictive information to the regional disaster prevention system.

[0117] ===Hardware Guarantee 1-4===

[0118] Each of the aforementioned elements—collection unit, analysis unit, generation unit, and provision unit—is implemented, for example, by at least one of robot 414 and data processing device 12. For example, the collection unit uses sensors in robot 414 to monitor the animal's heart rate, body temperature, and movement patterns in real time. The analysis unit, for example, is implemented by a specific processing unit 290 of data processing device 12, which analyzes the collected data. The generation unit, for example, is implemented by the specific processing unit 290 of data processing device 12, which generates predictive information based on the analysis results. The provision unit, for example, is implemented by a control unit 46A of robot 414, which provides the generated predictive information to the regional disaster prevention system.

[0119] The system described in this embodiment is not limited to the examples above. For example, various modifications can be made as follows.

[0120] The data collection unit can infer the animal's emotions and adjust the sensitivity of the sensors accordingly. For example, when the animal is excited, the sensitivity of the heart rate sensor is increased to collect detailed data. When the animal is relaxed, the sensitivity of the body temperature sensor can be adjusted to detect minute changes. Furthermore, when the animal is anxious, the sensitivity of the accelerometer can be increased to record movement patterns in detail. By adjusting the sensor sensitivity based on the animal's emotions, more accurate data can be collected.

[0121] The data collection unit can analyze the animal's past behavioral records to select the optimal sensor placement. For example, if an animal has exhibited abnormal behavior in a specific location in the past, sensors can be concentrated at that location. The placement of heart rate and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if an animal exhibits abnormal behavior during a specific time period, the sensitivity of the sensors can be increased during that time period. By selecting the optimal sensor placement based on the animal's past behavioral records, the accuracy of data collection can be improved.

[0122] The data collection unit can filter sensor data based on the animal's current health status. For example, normal data collection is performed when the animal is healthy. When the animal is sick, abnormal data can be filtered out to collect accurate data. Furthermore, when the animal is fatigued, the data collection frequency can be adjusted to reduce its workload. By filtering data according to the animal's health status, accurate data can be collected.

[0123] The data collection department can infer the animal's emotional state and determine the priority of data collection based on this inferred emotional state. For example, when the animal is excited, heart rate data can be collected first. When the animal is relaxed, body temperature data can be collected first. Furthermore, when the animal is anxious, movement pattern data can be collected first. By prioritizing data based on the animal's emotional state, important data can be collected first.

[0124] The data collection unit can consider the animal's geographical location information when collecting sensor data, prioritizing the collection of highly relevant data. For example, when the animal is in a specific area, data from that area can be collected first. When the animal is moving, data along its movement path can also be collected first. Furthermore, when the animal exhibits abnormal behavior at a specific location, data from that location can be collected first. By considering the animal's geographical location information, highly relevant data can be prioritized for collection.

[0125] The analysis unit can infer an animal's emotions and adjust the analysis algorithm accordingly. For example, when an animal is excited, the algorithm for analyzing heart rate data is adjusted. When an animal is relaxed, the algorithm for analyzing body temperature data can be adjusted. Furthermore, when an animal is anxious, the algorithm for analyzing movement pattern data can be adjusted. By adjusting the analysis algorithm based on the animal's emotions, the accuracy of the analysis can be improved.

[0126] The analysis unit can refer to the animal's past data during analysis to improve accuracy. For example, it can refer to the animal's past heart rate data when analyzing the current data. It can also refer to the animal's past body temperature data when analyzing the current data. Furthermore, it can refer to the animal's past movement pattern data when analyzing the current data. By referring to the animal's past data, the analysis accuracy can be improved.

[0127] The analysis department can employ different analysis methods for different animal species during the analysis process. For example, when analyzing dog data, an analysis method that considers dog-specific behavioral patterns can be used. The same method can be used when analyzing cat data, and similarly, when analyzing bird data, a method that considers bird-specific behavioral patterns can be employed. By using different analysis methods for different animal species, the accuracy of the analysis can be improved.

[0128] The analysis unit can infer an animal's emotions and adjust the display of the analysis results accordingly. For example, when an animal is excited, heart rate data is highlighted. When an animal is relaxed, body temperature data can be displayed in detail. Furthermore, when an animal is anxious, movement pattern data can be highlighted. By adjusting the display of analysis results based on the animal's emotions, a more easily understandable display can be achieved.

[0129] The analysis unit can consider the geographical distribution of animals during analysis. For example, if an animal is in a specific area, data from that area can be analyzed first. If an animal is moving, data along its movement path can also be analyzed first. Furthermore, if an animal exhibits unusual behavior at a specific location, data from that location can be analyzed first. By considering the geographical distribution of animals, more relevant analysis can be performed.

[0130] The following is a brief description of the processing flow of Implementation Method 2.

[0131] Step 1: The data collection unit gathers data from sensors attached to the animal. These sensors include heart rate sensors, body temperature sensors, and accelerometers. The data collection unit uses the heart rate sensor to monitor the animal's heart rate in real time, the body temperature sensor to measure the animal's body temperature, and the accelerometer to detect the animal's movement patterns. Data is collected when abnormal changes occur.

[0132] Step 2: The analysis department analyzes the data collected by the collection department. The analysis department uses machine learning algorithms (such as K-means, random forest, neural networks) to analyze the data, and judges abnormal clustering, classification results, or patterns as earthquake precursors.

[0133] Step 3: The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The generation unit uses generative AI (e.g., neural networks, generative models) to generate prediction information based on the analysis results.

[0134] Step 4: The Provision Department provides the forecast information generated by the Generation Department. The Provision Department provides the generated forecast information to the regional disaster prevention system (e.g., alarm system, evacuation guidance system) and notifies regional residents. It can also be provided to regional residents via web applications or mobile applications.

[0135] The specific processing unit 290 sends 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 voice representing the user's input to the result of the specific processing. The control unit 46A sends the voice data representing the user's 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 voice data.

[0136] Data generation model 58 is what is known as generative AI (Artificial Intelligence). An example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL:https: / / openai.com / blog / chatgpt> Generative AI, such as data generation model 58, is obtained by performing deep learning on a neural network. The data generation model 58 is input with a prompt containing instructions, and with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data of still images or data of moving images). The data generation model 58 infers the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the above-described specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0137] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0138] The correspondence between the various parts and the devices or control units is not limited to the examples mentioned above, and various changes can be made.

[0139] Second Implementation Method

[0140] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0141] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0142] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0143] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0144] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0145] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0146] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0147] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0149] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0150] In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0151] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 sends 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 voice input representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 includes generative AIs such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers from the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts that do not contain instructions; in this case, the data generation model 58 can output inference results from prompts that do not contain instructions. The data processing device 12, etc., includes various data generation models 58, including AIs other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0154] The data processing system 210 of the second embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0155] The correspondence between the various parts and the devices or control units is not limited to the examples mentioned above, and various changes can be made.

[0156] Third Implementation Method

[0157] Figure 5An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0158] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0159] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0160] The head-mounted terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0162] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0163] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0164] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0166] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0167] In the head-mounted terminal 314, specific processing is performed by the processor 46. A specific program 60 is stored in the memory 50. The processor 46 reads the specific program 60 from the memory 50 and executes the read specific program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific program 60 executed on the RAM 48. Furthermore, the head-mounted terminal 314 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0168] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 sends the result of the specific processing to the head-mounted terminal 314. In the head-mounted 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 voice representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 includes generative AIs such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers from the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts that do not contain instructions; in this case, the data generation model 58 can output inference results from prompts that do not contain instructions. The data processing device 12, etc., includes various data generation models 58, including AIs other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0171] The data processing system 310 of the third embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0172] The correspondence between the various parts and the devices or control units is not limited to the examples mentioned above, and various changes can be made.

[0173] Fourth Implementation Method

[0174] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0175] like Figure 7 As shown, 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.

[0176] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0177] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and control object 443 are also connected to the bus 52.

[0178] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0179] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0180] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0181] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0182] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0184] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in memory 50. Processor 46 reads the specific program 60 from memory 50 and executes the read specific program 60 on RAM 48. Specific processing is achieved by processor 46 acting as control unit 46A based on the specific program 60 executed on RAM 48. Furthermore, robot 414 may also have the same data generation model and emotion-specific model as data generation model 58 and emotion-specific model 59, and use these models to perform the same processing as specific processing unit 290.

[0186] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 sends 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 voice representing the user's input regarding the result of the specific processing. The control unit 46A sends the voice data representing the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0188] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. 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. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, 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 are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0189] The data processing system 410 of the fourth embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0190] The correspondence between the various parts and the devices or control units is not limited to the examples mentioned above, and various changes can be made.

[0191] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The robot's emotions can be determined by the emotion-specific model 59. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0192] Figure 9 This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0194] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0195] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Speech Emotion Recognition and Brain Physiological Signal Analysis Systems for Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "Reaction" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "Situation" domain, where situational cognition is dominant, are arranged.

[0196] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0197] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, sentiment values ​​in nearby configurations are similar to each other. Figure 10 Examples show that multiple emotions such as "peace of mind", "stability", and "reassurance" have similar emotional values.

[0198] In the above embodiments, a specific processing is described by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing by multiple computers, including computer 22, is also possible.

[0199] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed into the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0200] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0201] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0202] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., programs. Additionally, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit). Every processor has built-in or connected memory, and every processor executes specific processing by using memory.

[0203] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0204] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources for performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors as hardware resources.

[0205] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0206] Furthermore, although the above examples have been described in terms of first to fourth embodiments, some or all of these embodiments can be combined. Additionally, the smart device 14, smart glasses 214, head-mounted terminal 314, and robot 414 are just examples and can be combined separately, or other devices may be used. Furthermore, although the above examples have been described in terms of morphological example 1 and morphological example 2, these can also be combined.

[0207] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0208] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described as incorporated by reference.

Claims

1. A system, characterized by The system comprises: a collection unit configured to collect data from sensors worn on an animal; an analysis unit configured to analyze the data collected by the collection unit; a generation unit configured to generate prediction information based on the analysis results obtained by the analysis unit; and a provision unit configured to provide the prediction information generated by the generation unit.

2. The system according to claim 1, wherein the collection unit comprises a heart rate sensor, a body temperature sensor, and an acceleration sensor.

3. The system according to claim 1, wherein the analysis unit analyzes the data using a machine learning algorithm.

4. The system according to claim 1, wherein the generation unit generates the prediction information using a generative AI.

5. The system according to claim 1, wherein the provision unit provides the generated prediction information to a regional disaster prevention system.

6. The system according to claim 1, wherein the collection unit provides a method for inferring the animal's emotions and adjusting the sensitivity of the sensors based on the inferred emotions of the animal.

7. The system according to claim 1, wherein the collection unit analyzes the animal's past behavior records and selects appropriate sensor arrangements.

8. The system according to claim 1, wherein the collection unit filters the sensor data based on the current health status of the animal during data collection.

Images