system

Abstract

The system according to the embodiment aims to analyze animal behavior data and provide earthquake prediction information. [Solution] 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 attached to an animal. The analysis unit analyzes the data collected by the collection unit. The generation unit generates predictive information based on the analysis results obtained by the analysis unit. The provision unit provides the predictive information generated by the generation unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not fully utilized animal behavior data for earthquake prediction, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze animal behavior data and provide earthquake prediction information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from sensors attached to the animal. The analysis unit analyzes the data collected by the collection unit. The generation unit generates predictive information based on the analysis results obtained by the analysis unit. The provision unit provides the predictive information generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze animal behavior data and provide earthquake prediction information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic 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).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including 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.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An earthquake prediction system according to an embodiment of the present invention predicts earthquakes by utilizing the "body sensors" of living organisms. This system digitizes animal sensor information and works in conjunction with a generation AI to accurately verbalize the information the animal uses to predict earthquakes. For example, in an earthquake prediction system, sensors attached to the animal monitor the animal's physical reactions in real time. For example, the sensors detect the behavior and physiological changes that animals, such as dogs and cats, exhibit when sensing the signs of an earthquake. This sensor information includes the animal's heart rate, body temperature, and movement patterns. The collected sensor information is then digitized and input into a generation AI. The generation AI analyzes this data and extracts characteristic patterns that animals use to predict earthquakes. For example, it identifies specific increases in heart rate and abnormal movement patterns as signs of an earthquake. The generation AI then verbalizes earthquake prediction information based on the extracted patterns. For example, it generates specific prediction information, such as, "The dog's heart rate has risen sharply and it is exhibiting abnormal movements, which are considered to be signs of an earthquake." This prediction information is provided to local disaster prevention systems, providing advance warning of an upcoming earthquake. This allows local residents to evacuate early, minimizing damage from earthquakes. Furthermore, this system can generate profits through the sale of sensors and local contracts. For example, revenue can be generated by selling sensors to animal lovers and areas with a high awareness of disaster prevention and providing a service that links with the generative AI. In this way, a system that predicts earthquakes using the "body sensors" of living creatures can accurately predict earthquakes and reduce damage from earthquakes by digitizing animal sensor information and linking it with the generative AI. This allows the earthquake prediction system to accurately predict earthquakes by digitizing animal sensor information and linking it with the generative AI.

[0029] The earthquake prediction system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from sensors attached to the animal. Examples of sensors attached to the animal include, but are not limited to, a heart rate sensor, a body temperature sensor, and an acceleration sensor. For example, the collection unit monitors the animal's heart rate in real time using the heart rate sensor. The collection unit can also measure the animal's body temperature using the body temperature sensor. The collection unit can also detect the animal's movement pattern using the acceleration sensor. For example, the collection unit can detect fluctuations in the animal's heart rate using the heart rate sensor and collect data when an abnormal fluctuation is detected. The collection unit can also detect changes in the animal's body temperature using the body temperature sensor and collect data when an abnormal change is detected. The collection unit can also detect the animal's movement pattern using the acceleration sensor and collect data when an abnormal movement is detected. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, K-means and random forest. The analysis unit clusters data using, for example, K-means, and determines that an abnormal cluster is detected as a precursor to an earthquake. The analysis unit can also classify data using a random forest, and determine that an abnormal classification result is obtained as a precursor to an earthquake. The analysis unit can also analyze data using a neural network, and determine that an abnormal pattern is detected as a precursor to an earthquake. The generation unit generates predictive information based on the analysis results obtained by the analysis unit. The generation unit generates predictive information using, for example, a generation AI. Examples of generation AI include, but are not limited to, neural networks and generative models. The generation unit generates predictive information based on the analysis results using, for example, a neural network. The generation unit can also generate predictive information based on the analysis results using a generative model. The generation unit can also generate predictive information based on the analysis results using a generation AI. The provision unit provides the predictive information generated by the generation unit.The providing unit provides the generated forecast information to a local disaster prevention system, for example. Local disaster prevention systems include, but are not limited to, warning systems and evacuation guidance systems. The providing unit notifies local residents of the forecast information using, for example, a warning system. The providing unit can also issue evacuation instructions based on the forecast information using an evacuation guidance system. Furthermore, the providing unit can provide local residents with the generated forecast information through a web application or a mobile application. As a result, the earthquake prediction system according to the embodiment can accurately predict earthquakes by digitizing animal sensor information and linking it with the generation AI.

[0030] The collection unit may include a heart rate sensor, a body temperature sensor, and an acceleration sensor. The heart rate sensor is used to monitor the heart rate of the animal in real time. For example, the heart rate sensor may be attached to the animal's collar and detect heart rate fluctuations. Alternatively, the heart rate sensor may be attached to the animal's chest and detect heart rate fluctuations. Furthermore, the heart rate sensor may be attached to the animal's ear and detect heart rate fluctuations. The body temperature sensor is used to measure the body temperature of the animal. For example, the body temperature sensor may be attached to the animal's collar and detect body temperature fluctuations. Alternatively, the body temperature sensor may be attached to the animal's chest and detect body temperature fluctuations. Furthermore, the body temperature sensor may be attached to the animal's ear and detect body temperature fluctuations. The acceleration sensor is used to detect the movement pattern of the animal. For example, the acceleration sensor may be attached to the animal's collar and detect movement fluctuations. Alternatively, the acceleration sensor may be attached to the animal's chest and detect movement fluctuations. Furthermore, the acceleration sensor may be attached to the animal's paw and detect movement fluctuations. Thus, by using various sensors, the animal's physical reactions can be monitored in detail. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the animal's heart rate data into the generation AI and analyze heart rate fluctuations. The collection unit may also input the animal's body temperature data into the generation AI and analyze body temperature fluctuations. Furthermore, the collection unit may input the animal's movement pattern data into the generation AI and analyze movement fluctuations.

[0031] The analysis unit can analyze data using machine learning algorithms. Machine learning algorithms are used to analyze collected data. For example, K-means is used to cluster data. K-means divides data into multiple clusters and calculates the center of each cluster. Then, each data point is assigned to the closest cluster. This allows for identifying patterns in the data. Random forests are also used to classify data. Random forests build multiple decision trees and aggregate the prediction results of each decision tree. This allows for improved data classification accuracy. Neural networks are also used to analyze data. Neural networks are networks consisting of multiple layers, each of which processes input data and generates output. This allows for identifying complex patterns in the data. For example, the analysis unit can cluster collected data using K-means and determine that if an abnormal cluster is detected, it is a precursor to an earthquake. The analysis unit can also classify collected data using random forests and determine that if an abnormal classification result is obtained, it is a precursor to an earthquake. The analysis unit can also analyze collected data using neural networks and determine that if an abnormal pattern is detected, it is a precursor to an earthquake. This improves the accuracy of data analysis by using a machine learning algorithm. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input collected data into a generation AI and have the generation AI analyze the data.

[0032] The generation unit can generate predictive information using a generation AI. The generation 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 is a network consisting of multiple layers, each of which processes input data and generates output. This allows complex patterns in the analysis results to be identified and predictive information to be generated. A generative model is also used to generate predictive information based on the analysis results. A generative model is a model for generating new data based on input data and can generate predictive information based on the analysis results. For example, the generation unit can generate predictive information based on the analysis results using a neural network. The generation unit can also generate predictive information based on the analysis results using a generative model. Furthermore, the generation unit can generate predictive information based on the analysis results using a generation AI. This improves the accuracy of the predictive information. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the analysis results to a generation AI and cause the generation AI to generate predictive information.

[0033] The providing unit can provide the generated forecast information to a local disaster prevention system. Examples of local disaster prevention systems include warning systems and evacuation guidance systems. Warning systems are used to notify local residents of earthquakes. For example, warning systems notify local residents of earthquakes through audio alarms or text messages. Warning systems can also notify local residents of earthquakes through local television and radio. Furthermore, warning systems can also notify local residents of earthquakes through local websites and mobile applications. Evacuation guidance systems are used to guide local residents to safe locations when an earthquake occurs. For example, evacuation guidance systems guide local residents along evacuation routes through audio guides or text messages. Evacuation guidance systems can also guide evacuation routes through local television and radio. Furthermore, evacuation guidance systems can also guide evacuation routes through local websites and mobile applications. By providing forecast information to a local disaster prevention system, advance warning of an earthquake can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the generated forecast information into a generating AI and cause the generating AI to execute a method for providing the forecast information to a local disaster prevention system.

[0034] The collection unit can analyze the animal's past behavioral history and select optimal sensor placement. The collection unit analyzes the animal's past behavioral history and selects optimal sensor placement. For example, if the animal has previously exhibited abnormal behavior in a specific location, sensors can be concentrated in that location. The placement of heart rate sensors and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if the 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 the animal's behavior log and place sensors in locations where abnormal behavior frequently occurs. The collection unit can also analyze the animal's location information data and adjust the sensitivity of the sensors during times when abnormal behavior frequently occurs. Furthermore, the collection unit can analyze the animal's behavioral patterns and place sensors in locations and times when abnormal behavior frequently occurs. This improves the accuracy of data collection by selecting optimal sensor placement based on the animal's past behavioral history. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input animal behavior history data into the generation AI and have the generation AI select the optimal sensor placement.

[0035] The collection unit can filter sensor data based on the animal's current health condition when collecting sensor data. The collection unit can filter sensor data based on the animal's current health condition when collecting sensor data. For example, if the animal is healthy, normal data collection can be performed. If the animal is sick, abnormal data can be filtered to collect accurate data. Furthermore, if the animal is tired, the frequency of data collection can be adjusted to reduce the burden on the animal. For example, the collection unit can analyze the animal's health check data and collect normal data when the animal is in good health. The collection unit can also analyze the animal's vital sign data and filter abnormal data. Furthermore, the collection unit can monitor the animal's health condition in real time and adjust the frequency of data collection depending on the fatigue level. This allows accurate data to be collected by filtering data according to the animal's health condition. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the animal's health condition data to the generation AI and have the generation AI perform data filtering.

[0036] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the animal. When collecting sensor data, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the animal. For example, if the animal is in a specific area, data from that area can be collected preferentially. Also, if the animal is moving, data along its movement route can be collected preferentially. Furthermore, if the animal exhibits abnormal behavior in a specific location, data from that location can be collected preferentially. For example, the collection unit analyzes the animal's GPS data and, if the animal is in a specific area, prioritizes collecting data from that area. Also, the collection unit can analyze the animal's movement route data and, if the animal is moving, prioritizes collecting data along its movement route. Furthermore, the collection unit can analyze the animal's behavior pattern data and, if the animal exhibits abnormal behavior in a specific location, prioritizes collecting data from that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the animal. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input geographic location data of animals into the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0037] The collection unit can analyze the social media activities of the animal owner when collecting sensor data and collect related data. The collection unit can analyze the social media activities of the animal owner when collecting sensor data and collect related data. For example, if the owner reports abnormal animal behavior on social media, the collection unit can collect data related to the behavior. If the owner is participating in a specific event, the collection unit can also collect data related to the event. Furthermore, if the owner shares the animal's health condition on social media, the collection unit can also collect data based on that information. For example, the collection unit can analyze the owner's social media posts and collect data related to the animal's abnormal behavior. The collection unit can also analyze the owner's social media activities and collect data related to a specific event. Furthermore, the collection unit can analyze the owner's social media posts and collect data related to the animal's health condition. In this way, related data can be collected by analyzing the owner's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the owner's social media data into the generation AI and cause the generation AI to collect related data.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the animal's past data during analysis. The analysis unit can improve the accuracy of the analysis by referring to the animal's past data during analysis. For example, the analysis unit can analyze current data by referring to the animal's past heart rate data. The analysis unit can also analyze current data by referring to the animal's past body temperature data. The analysis unit can also analyze current data by referring to the animal's past movement pattern data. For example, the analysis unit can analyze the animal's past heart rate data and compare 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. The analysis unit can also analyze the animal's past movement pattern data and compare it with the current movement pattern data. In this way, the accuracy of the analysis is improved by referring to the animal's past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the animal's past data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] The analysis unit can apply different analysis methods to each type of animal during analysis. The analysis unit applies different analysis methods to each type of animal during analysis. For example, when analyzing dog data, an analysis method that takes into account dog-specific behavioral patterns can be applied. Furthermore, when analyzing cat data, an analysis method that takes into account cat-specific behavioral patterns can be applied. Furthermore, when analyzing bird data, an analysis method that takes into account bird-specific behavioral patterns can be applied. For example, the analysis unit analyzes dog behavior patterns and applies an analysis method that takes into account dog-specific behavioral patterns. Furthermore, the analysis unit can analyze cat behavior patterns and apply an analysis method that takes into account cat-specific behavioral patterns. Furthermore, the analysis unit can analyze bird behavior patterns and apply an analysis method that takes into account bird-specific behavioral patterns. In this way, by applying different analysis methods to each type of animal, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data for each type of animal into the generation AI and cause the generation AI to apply different analysis methods.

[0040] The analysis unit can take the geographical distribution of animals into consideration when performing analysis. The analysis unit can take the geographical distribution of animals into consideration when performing analysis. For example, if an animal is in a specific area, it can prioritize analyzing data from that area. Also, if an animal is moving, it can prioritize analyzing data along its movement route. Furthermore, if an animal exhibits abnormal behavior in a specific location, it can prioritize analyzing data from that location. For example, the analysis unit analyzes the animal's GPS data and, if the animal is in a specific area, prioritizes analyzing data from that area. Also, the analysis unit can analyze the animal's movement route data and, if the animal is moving, prioritize analyzing data along its movement route. Furthermore, the analysis unit can analyze the animal's behavior pattern data and, if the animal exhibits abnormal behavior in a specific location, prioritize analyzing data from that location. This enables more relevant analysis by taking the geographical distribution of animals into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the geographical distribution data of animals to the generation AI and cause the generation AI to perform preferential analysis of highly relevant data.

[0041] The analysis unit can improve the accuracy of the analysis by referring to literature related to animals during the analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to animals during the analysis. For example, the analysis can be performed by referring to the latest research papers on animal behavior. The analysis can also be performed by referring to literature on physiological changes in animals. Furthermore, the analysis can be performed by referring to past research data on earthquake prediction by animals. For example, the analysis unit can refer to the latest research papers on animal behavior and reflect the results in the analysis. The analysis unit can also refer to literature on physiological changes in animals and reflect the results in the analysis. Furthermore, the analysis unit can refer to past research data on earthquake prediction by animals and reflect the results in the analysis. In this way, the accuracy of the analysis is improved by referring to literature related to animals. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature data related to animals into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The generation unit can improve the accuracy of generation by referring to the animal's past behavioral patterns when generating the prediction information. The generation unit can improve the accuracy of generation by referring to the animal's past behavioral patterns when generating the prediction information. For example, the generation unit generates current prediction information by referring to the animal's past heart rate data. The current prediction information can also be generated by referring to the animal's past body temperature data. The current prediction information can also be generated by referring to the animal's past movement pattern data. 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. The generation unit can also analyze the animal's past movement pattern data and compare it with the current movement pattern data to generate prediction information. In this way, the accuracy of the prediction information is improved by referring to the animal's past behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the animal's past behavioral pattern data to the generation AI and cause the generation AI to improve the accuracy of generating the prediction information.

[0043] The generation unit can apply different generation algorithms to each type of animal when generating prediction information. The generation unit applies different generation algorithms to each type of animal when generating prediction information. For example, when generating prediction information based on dog data, a generation algorithm that takes into account dog-specific behavioral patterns can be applied. Furthermore, when generating prediction information based on cat data, a generation algorithm that takes into account cat-specific behavioral patterns can be applied. Furthermore, when generating prediction information based on bird data, a generation algorithm that takes into account bird-specific behavioral patterns can be applied. For example, the generation unit analyzes dog behavior patterns and applies a generation algorithm that takes into account dog-specific behavioral patterns. Furthermore, the generation unit can analyze cat behavior patterns and apply a generation algorithm that takes into account cat-specific behavioral patterns. Furthermore, the generation unit can analyze bird behavior patterns and apply a generation algorithm that takes into account bird-specific behavioral patterns. In this way, applying different generation algorithms to each type of animal improves the accuracy of the prediction information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data for each type of animal into the generation AI and cause the generation AI to apply different generation algorithms.

[0044] The generation unit can take the geographical distribution of animals into consideration when generating the prediction information. The generation unit can take the geographical distribution of animals into consideration when generating the prediction information. For example, if an animal is in a specific area, the generation unit can generate the prediction information based on data for that area. Also, if an animal is moving, the generation unit can generate the prediction information based on data along its movement route. Furthermore, if an animal exhibits abnormal behavior in a specific location, the generation unit can generate the prediction information based on data for that location. For example, the generation unit can analyze the animal's GPS data and, if the animal is in a specific area, generate the prediction information based on data for that area. Also, the generation unit can analyze the animal's movement route data and, if the animal is moving, generate the prediction information based on data along its movement route. Furthermore, the generation unit can analyze the animal's behavior pattern data and, if the animal exhibits abnormal behavior in a specific location, generate the prediction information based on data for that location. In this way, by taking the geographical distribution of animals into consideration, more relevant prediction information can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the geographical distribution data of animals into the generation AI and cause the generation AI to prioritize the generation of highly relevant data.

[0045] The generation unit can improve the accuracy of the generation of prediction information by referring to literature related to animals when generating the prediction information. The generation unit can improve the accuracy of the generation by referring to literature related to animals when generating the prediction information. For example, the generation unit can generate prediction information by referring to the latest research papers on animal behavior. The generation unit can also generate prediction information by referring to literature on physiological changes in animals. Furthermore, the generation unit can generate prediction information by referring to past research data on earthquake prediction by animals. For example, the generation unit can refer to the latest research papers on animal behavior and reflect the results in the prediction information. The generation unit can also refer to literature on physiological changes in animals and reflect the results in the prediction information. Furthermore, the generation unit can refer to past research data on earthquake prediction by animals and reflect the results in the prediction information. In this way, the accuracy of the prediction information is improved by referring to literature related to animals. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input animal-related literature data into the generation AI and cause the generation AI to improve the accuracy of generating prediction information.

[0046] The providing unit can improve the accuracy of the provision of forecast information by referring to past earthquake data for the region when providing the forecast information. The providing unit can improve the accuracy of the provision by referring to past earthquake data for the region when providing the forecast information. For example, the providing unit provides current forecast information based on past earthquake data for the region. The reliability of the forecast information can also be improved by referring to past earthquake data for the region. Furthermore, the method of providing the forecast information can be adjusted based on past earthquake data for the region. For example, the providing unit analyzes past earthquake records for the region and reflects them in the current forecast information. The providing unit can also analyze past seismic intensity data for the region and improve the reliability of the forecast information. Furthermore, the providing unit can adjust the method of providing the forecast information based on past earthquake data for the region. In this way, the reliability of the forecast information is improved by referring to past earthquake data for the region. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past earthquake data for the region into the generation AI and cause the generation AI to improve the accuracy of providing the forecast information.

[0047] The providing unit can apply different delivery methods to each local disaster prevention system when providing forecast information. The providing unit applies different delivery methods to each local disaster prevention system when providing forecast information. For example, if the local disaster prevention system uses SMS notification, the forecast information can be provided by SMS. Also, if the local disaster prevention system uses app notification, the forecast information can be provided by app notification. Furthermore, if the local disaster prevention system uses email notification, the forecast information can be provided by email. For example, the providing unit analyzes the type of local disaster prevention system and provides the forecast information by SMS when SMS notification is used. Also, the providing unit can provide the forecast information by app notification when the local disaster prevention system uses app notification. Furthermore, the providing unit can provide the forecast information by email when the local disaster prevention system uses email notification. This enables more effective information provision by applying a delivery method according to the local disaster prevention system. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input local disaster prevention system data to the generation AI and cause the generation AI to apply different delivery methods.

[0048] The providing unit may provide forecast information taking into account the geographical characteristics of the region. The providing unit may provide forecast information taking into account the geographical characteristics of the region. For example, if the region is a mountainous area, the providing unit may provide forecast information taking into account the impact of earthquakes. Furthermore, if the region is an urban area, the providing unit may provide forecast information taking into account the density of buildings. Furthermore, if the region is a coastal area, the providing unit may provide forecast information taking into account the risk of tsunamis. For example, the providing unit may analyze topographical data of the region and provide forecast information taking into account the impact of earthquakes if the region is a mountainous area. Furthermore, the providing unit may analyze population distribution data of the region and provide forecast information taking into account the density of buildings if the region is an urban area. Furthermore, the providing unit may analyze coastal data of the region and provide forecast information taking into account the risk of tsunamis. This allows for more relevant information to be provided by taking into account the geographical characteristics of the region. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input geographical characteristic data of the region into the generating AI and cause the generating AI to provide highly relevant information.

[0049] The providing unit can improve the accuracy of the provided forecast information by referring to literature related to the region when providing the forecast information. The providing unit can improve the accuracy of the provided forecast information by referring to literature related to the region when providing the forecast information. For example, the providing unit can provide forecast information by referring to the latest research papers on earthquakes in the region. The providing unit can also provide forecast information by referring to literature on disaster prevention in the region. Furthermore, the providing unit can provide forecast information by referring to past earthquake data in the region. For example, the providing unit can refer to the latest research papers on earthquakes in the region and reflect the results in the forecast information. The providing unit can also refer to literature on disaster prevention in the region and reflect the results in the forecast information. Furthermore, the providing unit can refer to past earthquake data in the region and reflect the results in the forecast information. In this way, the accuracy of the forecast information is improved by referring to literature related to the region. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input literature data related to the region into the generating AI and cause the generating AI to improve the accuracy of the provided forecast information.

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

[0051] The collection unit can analyze the animal's past behavioral history and select the optimal sensor placement. For example, if the animal has previously exhibited abnormal behavior in a specific location, sensors can be concentrated in that location. The placement of heart rate sensors and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if the animal exhibits abnormal behavior during a specific time period, the sensitivity of the sensors can be increased during that time period. This improves the accuracy of data collection by selecting the optimal sensor placement based on the animal's past behavioral history.

[0052] The collection unit can filter sensor data collection based on the animal's current health condition. For example, if the animal is healthy, normal data collection is performed. If the animal is sick, abnormal data can be filtered to collect accurate data. Furthermore, if the animal is tired, the frequency of data collection can be adjusted to reduce the burden on the animal. This allows accurate data to be collected by filtering data according to the animal's health condition.

[0053] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the animal. For example, if the animal is in a specific area, data from that area can be collected preferentially. Also, if the animal is moving, data along its path can be collected preferentially. Furthermore, if the animal exhibits abnormal behavior in a specific location, data from that location can be collected preferentially. In this way, by taking into account the geographical location information of the animal, highly relevant data can be collected preferentially.

[0054] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the animal's past data. For example, the analysis unit can analyze the current data by referring to the animal's past heart rate data. The analysis unit can also analyze the current data by referring to the animal's past body temperature data. Furthermore, the analysis unit can analyze the current data by referring to the animal's past movement pattern data. In this way, by referring to the animal's past data, the accuracy of the analysis can be improved.

[0055] During analysis, the analysis unit can apply different analysis methods to each type of animal. For example, when analyzing dog data, an analysis method that takes into account dog-specific behavioral patterns can be applied. When analyzing cat data, an analysis method that takes into account cat-specific behavioral patterns can also be applied. Furthermore, when analyzing bird data, an analysis method that takes into account bird-specific behavioral patterns can also be applied. In this way, by applying different analysis methods to each type of animal, the accuracy of the analysis is improved.

[0056] The analysis unit can take into account the geographical distribution of animals during analysis. For example, if an animal is in a specific area, it can prioritize analyzing data from that area. Also, if an animal is moving, it can prioritize analyzing data along its route. Furthermore, if an animal exhibits abnormal behavior in a specific location, it can prioritize analyzing data from that location. This allows for more relevant analysis by taking the geographical distribution of animals into account.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects data from sensors attached to the animal. The sensors attached to the animal include a heart rate sensor, a body temperature sensor, and an acceleration sensor. The collection unit monitors the animal's heart rate in real time using the heart rate sensor, measures the animal's body temperature using the body temperature sensor, and detects the animal's movement patterns using the acceleration sensor. Data is collected when there are abnormal fluctuations or changes. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms (e.g., K-means, random forest, neural network) and determines that any abnormal clusters, classification results, or patterns detected are precursors to earthquakes. Step 3: The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The generation unit generates prediction information based on the analysis results using a generation AI (e.g., a neural network or a generative model). Step 4: The providing unit provides the forecast information generated by the generating unit. The providing unit provides the generated forecast information to a local disaster prevention system (e.g., an alert system, an evacuation guidance system) and notifies local residents. The information can also be provided to local residents through a web application or a mobile application.

[0059] (Example 2) An earthquake prediction system according to an embodiment of the present invention predicts earthquakes by utilizing the "body sensors" of living organisms. This system digitizes animal sensor information and works in conjunction with a generation AI to accurately verbalize the information the animal uses to predict earthquakes. For example, in an earthquake prediction system, sensors attached to the animal monitor the animal's physical reactions in real time. For example, the sensors detect the behavior and physiological changes that animals, such as dogs and cats, exhibit when sensing the signs of an earthquake. This sensor information includes the animal's heart rate, body temperature, and movement patterns. The collected sensor information is then digitized and input into a generation AI. The generation AI analyzes this data and extracts characteristic patterns that animals use to predict earthquakes. For example, it identifies specific increases in heart rate and abnormal movement patterns as signs of an earthquake. The generation AI then verbalizes earthquake prediction information based on the extracted patterns. For example, it generates specific prediction information, such as, "The dog's heart rate has risen sharply and it is exhibiting abnormal movements, which are considered to be signs of an earthquake." This prediction information is provided to local disaster prevention systems, providing advance warning of an upcoming earthquake. This allows local residents to evacuate early, minimizing damage from earthquakes. Furthermore, this system can generate profits through the sale of sensors and local contracts. For example, revenue can be generated by selling sensors to animal lovers and areas with a high awareness of disaster prevention and providing a service that links with the generative AI. In this way, a system that predicts earthquakes using the "body sensors" of living creatures can accurately predict earthquakes and reduce damage from earthquakes by digitizing animal sensor information and linking it with the generative AI. This allows the earthquake prediction system to accurately predict earthquakes by digitizing animal sensor information and linking it with the generative AI.

[0060] The earthquake prediction system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from sensors attached to the animal. Examples of sensors attached to the animal include, but are not limited to, a heart rate sensor, a body temperature sensor, and an acceleration sensor. For example, the collection unit monitors the animal's heart rate in real time using the heart rate sensor. The collection unit can also measure the animal's body temperature using the body temperature sensor. The collection unit can also detect the animal's movement pattern using the acceleration sensor. For example, the collection unit can detect fluctuations in the animal's heart rate using the heart rate sensor and collect data when an abnormal fluctuation is detected. The collection unit can also detect changes in the animal's body temperature using the body temperature sensor and collect data when an abnormal change is detected. The collection unit can also detect the animal's movement pattern using the acceleration sensor and collect data when an abnormal movement is detected. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, K-means and random forest. The analysis unit clusters data using, for example, K-means, and determines that an abnormal cluster is detected as a precursor to an earthquake. The analysis unit can also classify data using a random forest, and determine that an abnormal classification result is obtained as a precursor to an earthquake. The analysis unit can also analyze data using a neural network, and determine that an abnormal pattern is detected as a precursor to an earthquake. The generation unit generates predictive information based on the analysis results obtained by the analysis unit. The generation unit generates predictive information using, for example, a generation AI. Examples of generation AI include, but are not limited to, neural networks and generative models. The generation unit generates predictive information based on the analysis results using, for example, a neural network. The generation unit can also generate predictive information based on the analysis results using a generative model. The generation unit can also generate predictive information based on the analysis results using a generation AI. The provision unit provides the predictive information generated by the generation unit.The providing unit provides the generated forecast information to a local disaster prevention system, for example. Local disaster prevention systems include, but are not limited to, warning systems and evacuation guidance systems. The providing unit notifies local residents of the forecast information using, for example, a warning system. The providing unit can also issue evacuation instructions based on the forecast information using an evacuation guidance system. Furthermore, the providing unit can provide local residents with the generated forecast information through a web application or a mobile application. As a result, the earthquake prediction system according to the embodiment can accurately predict earthquakes by digitizing animal sensor information and linking it with the generation AI.

[0061] The collection unit may include a heart rate sensor, a body temperature sensor, and an acceleration sensor. The heart rate sensor is used to monitor the heart rate of the animal in real time. For example, the heart rate sensor may be attached to the animal's collar and detect heart rate fluctuations. Alternatively, the heart rate sensor may be attached to the animal's chest and detect heart rate fluctuations. Furthermore, the heart rate sensor may be attached to the animal's ear and detect heart rate fluctuations. The body temperature sensor is used to measure the body temperature of the animal. For example, the body temperature sensor may be attached to the animal's collar and detect body temperature fluctuations. Alternatively, the body temperature sensor may be attached to the animal's chest and detect body temperature fluctuations. Furthermore, the body temperature sensor may be attached to the animal's ear and detect body temperature fluctuations. The acceleration sensor is used to detect the movement pattern of the animal. For example, the acceleration sensor may be attached to the animal's collar and detect movement fluctuations. Alternatively, the acceleration sensor may be attached to the animal's chest and detect movement fluctuations. Furthermore, the acceleration sensor may be attached to the animal's paw and detect movement fluctuations. Thus, by using various sensors, the animal's physical reactions can be monitored in detail. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the animal's heart rate data into the generation AI and analyze heart rate fluctuations. The collection unit may also input the animal's body temperature data into the generation AI and analyze body temperature fluctuations. Furthermore, the collection unit may input the animal's movement pattern data into the generation AI and analyze movement fluctuations.

[0062] The analysis unit can analyze data using machine learning algorithms. Machine learning algorithms are used to analyze collected data. For example, K-means is used to cluster data. K-means divides data into multiple clusters and calculates the center of each cluster. Then, each data point is assigned to the closest cluster. This allows for identifying patterns in the data. Random forests are also used to classify data. Random forests build multiple decision trees and aggregate the prediction results of each decision tree. This allows for improved data classification accuracy. Neural networks are also used to analyze data. Neural networks are networks consisting of multiple layers, each of which processes input data and generates output. This allows for identifying complex patterns in the data. For example, the analysis unit can cluster collected data using K-means and determine that if an abnormal cluster is detected, it is a precursor to an earthquake. The analysis unit can also classify collected data using random forests and determine that if an abnormal classification result is obtained, it is a precursor to an earthquake. The analysis unit can also analyze collected data using neural networks and determine that if an abnormal pattern is detected, it is a precursor to an earthquake. This improves the accuracy of data analysis by using a machine learning algorithm. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input collected data into a generation AI and have the generation AI analyze the data.

[0063] The generation unit can generate predictive information using a generation AI. The generation 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 is a network consisting of multiple layers, each of which processes input data and generates output. This allows complex patterns in the analysis results to be identified and predictive information to be generated. A generative model is also used to generate predictive information based on the analysis results. A generative model is a model for generating new data based on input data and can generate predictive information based on the analysis results. For example, the generation unit can generate predictive information based on the analysis results using a neural network. The generation unit can also generate predictive information based on the analysis results using a generative model. Furthermore, the generation unit can generate predictive information based on the analysis results using a generation AI. This improves the accuracy of the predictive information. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the analysis results to a generation AI and cause the generation AI to generate predictive information.

[0064] The providing unit can provide the generated forecast information to a local disaster prevention system. Examples of local disaster prevention systems include warning systems and evacuation guidance systems. Warning systems are used to notify local residents of earthquakes. For example, warning systems notify local residents of earthquakes through audio alarms or text messages. Warning systems can also notify local residents of earthquakes through local television and radio. Furthermore, warning systems can also notify local residents of earthquakes through local websites and mobile applications. Evacuation guidance systems are used to guide local residents to safe locations when an earthquake occurs. For example, evacuation guidance systems guide local residents along evacuation routes through audio guides or text messages. Evacuation guidance systems can also guide evacuation routes through local television and radio. Furthermore, evacuation guidance systems can also guide evacuation routes through local websites and mobile applications. By providing forecast information to a local disaster prevention system, advance warning of an earthquake can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the generated forecast information into a generating AI and cause the generating AI to execute a method for providing the forecast information to a local disaster prevention system.

[0065] The collection unit can estimate the animal's emotion and adjust the sensor sensitivity based on the estimated emotion. The collection unit can estimate the animal's emotion and adjust the sensor sensitivity based on the estimated emotion. For example, if the animal is excited, the sensitivity of the heart rate sensor can be increased to collect more detailed data. Also, if the animal is relaxed, the sensitivity of the body temperature sensor can be adjusted to detect subtle changes. Furthermore, if the animal is anxious, the sensitivity of the acceleration sensor can be increased to record the animal's movement patterns in detail. For example, the collection unit can analyze the animal's heart rate data and increase the sensor sensitivity when the heart rate fluctuations are large. The collection unit can also analyze the animal's body temperature data and adjust the sensor sensitivity when the body temperature fluctuations are large. Furthermore, the collection unit can analyze the animal's movement pattern data and increase the sensor sensitivity when the movement fluctuations are large. In this way, more accurate data can be collected by adjusting the sensor sensitivity according to the animal's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data of an animal to the generation AI and cause the generation AI to adjust the sensitivity of the sensor based on the emotion.

[0066] The collection unit can analyze the animal's past behavioral history and select optimal sensor placement. The collection unit analyzes the animal's past behavioral history and selects optimal sensor placement. For example, if the animal has previously exhibited abnormal behavior in a specific location, sensors can be concentrated in that location. The placement of heart rate sensors and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if the 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 the animal's behavior log and place sensors in locations where abnormal behavior frequently occurs. The collection unit can also analyze the animal's location information data and adjust the sensitivity of the sensors during times when abnormal behavior frequently occurs. Furthermore, the collection unit can analyze the animal's behavioral patterns and place sensors in locations and times when abnormal behavior frequently occurs. This improves the accuracy of data collection by selecting optimal sensor placement based on the animal's past behavioral history. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input animal behavior history data into the generation AI and have the generation AI select the optimal sensor placement.

[0067] The collection unit can filter sensor data based on the animal's current health condition when collecting sensor data. The collection unit can filter sensor data based on the animal's current health condition when collecting sensor data. For example, if the animal is healthy, normal data collection can be performed. If the animal is sick, abnormal data can be filtered to collect accurate data. Furthermore, if the animal is tired, the frequency of data collection can be adjusted to reduce the burden on the animal. For example, the collection unit can analyze the animal's health check data and collect normal data when the animal is in good health. The collection unit can also analyze the animal's vital sign data and filter abnormal data. Furthermore, the collection unit can monitor the animal's health condition in real time and adjust the frequency of data collection depending on the fatigue level. This allows accurate data to be collected by filtering data according to the animal's health condition. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the animal's health condition data to the generation AI and have the generation AI perform data filtering.

[0068] The collection unit can estimate the animal's emotion and determine the priority of data to be collected based on the estimated emotion. The collection unit can estimate the animal's emotion and determine the priority of data to be collected based on the estimated emotion. For example, if the animal is excited, it can prioritize collecting heart rate data. Also, if the animal is relaxed, it can prioritize collecting body temperature data. Furthermore, if the animal is anxious, it can prioritize collecting movement pattern data. For example, the collection unit can analyze the animal's heart rate data and prioritize collection if the heart rate fluctuations are large. Also, the collection unit can analyze the animal's body temperature data and prioritize collection if the body temperature fluctuations are large. Furthermore, the collection unit can analyze the animal's movement pattern data and prioritize collection if the movement fluctuations are large. In this way, by prioritizing data according to the animal's emotion, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input animal emotion data to the generation AI and have the generation AI determine the priority of the data.

[0069] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the animal. When collecting sensor data, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the animal. For example, if the animal is in a specific area, data from that area can be collected preferentially. Also, if the animal is moving, data along its movement route can be collected preferentially. Furthermore, if the animal exhibits abnormal behavior in a specific location, data from that location can be collected preferentially. For example, the collection unit analyzes the animal's GPS data and, if the animal is in a specific area, prioritizes collecting data from that area. Also, the collection unit can analyze the animal's movement route data and, if the animal is moving, prioritizes collecting data along its movement route. Furthermore, the collection unit can analyze the animal's behavior pattern data and, if the animal exhibits abnormal behavior in a specific location, prioritizes collecting data from that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the animal. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input geographic location data of animals into the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0070] The collection unit can analyze the social media activities of the animal owner when collecting sensor data and collect related data. The collection unit can analyze the social media activities of the animal owner when collecting sensor data and collect related data. For example, if the owner reports abnormal animal behavior on social media, the collection unit can collect data related to the behavior. If the owner is participating in a specific event, the collection unit can also collect data related to the event. Furthermore, if the owner shares the animal's health condition on social media, the collection unit can also collect data based on that information. For example, the collection unit can analyze the owner's social media posts and collect data related to the animal's abnormal behavior. The collection unit can also analyze the owner's social media activities and collect data related to a specific event. Furthermore, the collection unit can analyze the owner's social media posts and collect data related to the animal's health condition. In this way, related data can be collected by analyzing the owner's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the owner's social media data into the generation AI and cause the generation AI to collect related data.

[0071] The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. For example, if the animal is excited, the analysis algorithm of the heart rate data can be adjusted. Also, if the animal is relaxed, the analysis algorithm of the body temperature data can be adjusted. Furthermore, if the animal is anxious, the analysis algorithm of the movement pattern data can be adjusted. For example, the analysis unit can analyze the heart rate data of the animal and adjust the analysis algorithm if the heart rate fluctuations are large. Also, the analysis unit can analyze the body temperature data of the animal and adjust the analysis algorithm if the body temperature fluctuations are large. Furthermore, the analysis unit can analyze the movement pattern data of the animal and adjust the analysis algorithm if the movement fluctuations are large. In this way, by adjusting the analysis algorithm according to the emotion of the animal, the accuracy of the analysis is improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input emotion data of the animal to the generation AI and have the generation AI adjust the analysis algorithm.

[0072] The analysis unit can improve the accuracy of the analysis by referring to the animal's past data during analysis. The analysis unit can improve the accuracy of the analysis by referring to the animal's past data during analysis. For example, the analysis unit can analyze current data by referring to the animal's past heart rate data. The analysis unit can also analyze current data by referring to the animal's past body temperature data. The analysis unit can also analyze current data by referring to the animal's past movement pattern data. For example, the analysis unit can analyze the animal's past heart rate data and compare 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. The analysis unit can also analyze the animal's past movement pattern data and compare it with the current movement pattern data. In this way, the accuracy of the analysis is improved by referring to the animal's past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the animal's past data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0073] The analysis unit can apply different analysis methods to each type of animal during analysis. The analysis unit applies different analysis methods to each type of animal during analysis. For example, when analyzing dog data, an analysis method that takes into account dog-specific behavioral patterns can be applied. Furthermore, when analyzing cat data, an analysis method that takes into account cat-specific behavioral patterns can be applied. Furthermore, when analyzing bird data, an analysis method that takes into account bird-specific behavioral patterns can be applied. For example, the analysis unit analyzes dog behavior patterns and applies an analysis method that takes into account dog-specific behavioral patterns. Furthermore, the analysis unit can analyze cat behavior patterns and apply an analysis method that takes into account cat-specific behavioral patterns. Furthermore, the analysis unit can analyze bird behavior patterns and apply an analysis method that takes into account bird-specific behavioral patterns. In this way, by applying different analysis methods to each type of animal, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data for each type of animal into the generation AI and cause the generation AI to apply different analysis methods.

[0074] The analysis unit can estimate the animal's emotion and adjust the display method of the analysis results based on the estimated emotion. The analysis unit can estimate the animal's emotion and adjust the display method of the analysis results based on the estimated emotion. For example, if the animal is excited, the heart rate data can be emphasized. If the animal is relaxed, the body temperature data can be displayed in detail. Furthermore, if the animal is anxious, the movement pattern data can be emphasized. For example, the analysis unit can analyze the animal's heart rate data and emphasize the heart rate data when the heart rate fluctuations are large. The analysis unit can also analyze the animal's body temperature data and display the body temperature data in detail when the body temperature fluctuations are large. Furthermore, the analysis unit can analyze the animal's movement pattern data and emphasize the movement pattern data when the movement fluctuations are large. This allows the display method of the analysis results to be adjusted according to the animal's emotion, making it easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input emotion data of an animal to the generation AI and have the generation AI adjust the display method of the analysis results.

[0075] The analysis unit can take the geographical distribution of animals into consideration when performing analysis. The analysis unit can take the geographical distribution of animals into consideration when performing analysis. For example, if an animal is in a specific area, it can prioritize analyzing data from that area. Also, if an animal is moving, it can prioritize analyzing data along its movement route. Furthermore, if an animal exhibits abnormal behavior in a specific location, it can prioritize analyzing data from that location. For example, the analysis unit analyzes the animal's GPS data and, if the animal is in a specific area, prioritizes analyzing data from that area. Also, the analysis unit can analyze the animal's movement route data and, if the animal is moving, prioritize analyzing data along its movement route. Furthermore, the analysis unit can analyze the animal's behavior pattern data and, if the animal exhibits abnormal behavior in a specific location, prioritize analyzing data from that location. This enables more relevant analysis by taking the geographical distribution of animals into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the geographical distribution data of animals to the generation AI and cause the generation AI to perform preferential analysis of highly relevant data.

[0076] The analysis unit can improve the accuracy of the analysis by referring to literature related to animals during the analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to animals during the analysis. For example, the analysis can be performed by referring to the latest research papers on animal behavior. The analysis can also be performed by referring to literature on physiological changes in animals. Furthermore, the analysis can be performed by referring to past research data on earthquake prediction by animals. For example, the analysis unit can refer to the latest research papers on animal behavior and reflect the results in the analysis. The analysis unit can also refer to literature on physiological changes in animals and reflect the results in the analysis. Furthermore, the analysis unit can refer to past research data on earthquake prediction by animals and reflect the results in the analysis. In this way, the accuracy of the analysis is improved by referring to literature related to animals. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature data related to animals into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] The generation unit can estimate the emotion of the animal and adjust the method for generating the prediction information based on the estimated emotion of the animal. The generation unit can estimate the emotion of the animal and adjust the method for generating the prediction information based on the estimated emotion of the animal. For example, if the animal is excited, the generation unit can generate the prediction information based on heart rate data. Also, if the animal is relaxed, the generation unit can generate the prediction information based on body temperature data. Furthermore, if the animal is anxious, the generation unit can generate the prediction information based on movement pattern data. For example, the generation unit can analyze the heart rate data of the animal and generate the prediction information when the heart rate fluctuations are large. Also, the generation unit can analyze the body temperature data of the animal and generate the prediction information when the body temperature fluctuations are large. Furthermore, the generation unit can analyze the movement pattern data of the animal and generate the prediction information when the movement fluctuations are large. In this way, by adjusting the method for generating the prediction information according to the emotion of the animal, more accurate prediction information can be generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input emotion data of the animal to the generation AI and cause the generation AI to adjust the method for generating prediction information.

[0078] The generation unit can improve the accuracy of generation by referring to the animal's past behavioral patterns when generating the prediction information. The generation unit can improve the accuracy of generation by referring to the animal's past behavioral patterns when generating the prediction information. For example, the generation unit generates current prediction information by referring to the animal's past heart rate data. The current prediction information can also be generated by referring to the animal's past body temperature data. The current prediction information can also be generated by referring to the animal's past movement pattern data. 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. The generation unit can also analyze the animal's past movement pattern data and compare it with the current movement pattern data to generate prediction information. In this way, the accuracy of the prediction information is improved by referring to the animal's past behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the animal's past behavioral pattern data to the generation AI and cause the generation AI to improve the accuracy of generating the prediction information.

[0079] The generation unit can apply different generation algorithms to each type of animal when generating prediction information. The generation unit applies different generation algorithms to each type of animal when generating prediction information. For example, when generating prediction information based on dog data, a generation algorithm that takes into account dog-specific behavioral patterns can be applied. Furthermore, when generating prediction information based on cat data, a generation algorithm that takes into account cat-specific behavioral patterns can be applied. Furthermore, when generating prediction information based on bird data, a generation algorithm that takes into account bird-specific behavioral patterns can be applied. For example, the generation unit analyzes dog behavior patterns and applies a generation algorithm that takes into account dog-specific behavioral patterns. Furthermore, the generation unit can analyze cat behavior patterns and apply a generation algorithm that takes into account cat-specific behavioral patterns. Furthermore, the generation unit can analyze bird behavior patterns and apply a generation algorithm that takes into account bird-specific behavioral patterns. In this way, applying different generation algorithms to each type of animal improves the accuracy of the prediction information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data for each type of animal into the generation AI and cause the generation AI to apply different generation algorithms.

[0080] The generation unit can estimate the animal's emotion and adjust the display method of the predicted information based on the estimated emotion. The generation unit can estimate the animal's emotion and adjust the display method of the predicted information based on the estimated emotion. For example, if the animal is excited, the heart rate data can be emphasized when displayed. Also, if the animal is relaxed, the body temperature data can be displayed in detail. Furthermore, if the animal is anxious, the movement pattern data can be emphasized when displayed. For example, the generation unit can analyze the animal's heart rate data and emphasize the heart rate data when the heart rate fluctuations are large. Also, the generation unit can analyze the animal's body temperature data and display the body temperature data in detail when the body temperature fluctuations are large. Furthermore, the generation unit can analyze the animal's movement pattern data and emphasize the movement pattern data when the movement fluctuations are large. This allows the display method of the predicted information to be more easily understood by adjusting it according to the animal's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input emotion data of the animal to the generation AI and cause the generation AI to adjust the display method of the predicted information.

[0081] The generation unit can take the geographical distribution of animals into consideration when generating the prediction information. The generation unit can take the geographical distribution of animals into consideration when generating the prediction information. For example, if an animal is in a specific area, the generation unit can generate the prediction information based on data for that area. Also, if an animal is moving, the generation unit can generate the prediction information based on data along its movement route. Furthermore, if an animal exhibits abnormal behavior in a specific location, the generation unit can generate the prediction information based on data for that location. For example, the generation unit can analyze the animal's GPS data and, if the animal is in a specific area, generate the prediction information based on data for that area. Also, the generation unit can analyze the animal's movement route data and, if the animal is moving, generate the prediction information based on data along its movement route. Furthermore, the generation unit can analyze the animal's behavior pattern data and, if the animal exhibits abnormal behavior in a specific location, generate the prediction information based on data for that location. In this way, by taking the geographical distribution of animals into consideration, more relevant prediction information can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the geographical distribution data of animals into the generation AI and cause the generation AI to prioritize the generation of highly relevant data.

[0082] The generation unit can improve the accuracy of the generation of prediction information by referring to literature related to animals when generating the prediction information. The generation unit can improve the accuracy of the generation by referring to literature related to animals when generating the prediction information. For example, the generation unit can generate prediction information by referring to the latest research papers on animal behavior. The generation unit can also generate prediction information by referring to literature on physiological changes in animals. Furthermore, the generation unit can generate prediction information by referring to past research data on earthquake prediction by animals. For example, the generation unit can refer to the latest research papers on animal behavior and reflect the results in the prediction information. The generation unit can also refer to literature on physiological changes in animals and reflect the results in the prediction information. Furthermore, the generation unit can refer to past research data on earthquake prediction by animals and reflect the results in the prediction information. In this way, the accuracy of the prediction information is improved by referring to literature related to animals. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input animal-related literature data into the generation AI and cause the generation AI to improve the accuracy of generating prediction information.

[0083] The providing unit can estimate the emotion of the animal and adjust the method of providing the predictive information based on the estimated emotion of the animal. The providing unit can estimate the emotion of the animal and adjust the method of providing the predictive information based on the estimated emotion of the animal. For example, if the animal is excited, it can emphasize the heart rate data when providing it. Also, if the animal is relaxed, it can provide detailed body temperature data. Furthermore, if the animal is anxious, it can emphasize the movement pattern data when providing it. For example, the providing unit can analyze the heart rate data of the animal and emphasize the heart rate data when the heart rate fluctuations are large. Also, the providing unit can analyze the body temperature data of the animal and provide detailed body temperature data when the body temperature fluctuations are large. Furthermore, the providing unit can analyze the movement pattern data of the animal and emphasize the movement pattern data when the movement fluctuations are large. This allows for more appropriate information to be provided by adjusting the method of providing the predictive information according to the emotion of the animal. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input emotion data of the animal to the generating AI and cause the generating AI to adjust the method of providing the prediction information.

[0084] The providing unit can improve the accuracy of the provision of forecast information by referring to past earthquake data for the region when providing the forecast information. The providing unit can improve the accuracy of the provision by referring to past earthquake data for the region when providing the forecast information. For example, the providing unit provides current forecast information based on past earthquake data for the region. The reliability of the forecast information can also be improved by referring to past earthquake data for the region. Furthermore, the method of providing the forecast information can be adjusted based on past earthquake data for the region. For example, the providing unit analyzes past earthquake records for the region and reflects them in the current forecast information. The providing unit can also analyze past seismic intensity data for the region and improve the reliability of the forecast information. Furthermore, the providing unit can adjust the method of providing the forecast information based on past earthquake data for the region. In this way, the reliability of the forecast information is improved by referring to past earthquake data for the region. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past earthquake data for the region into the generation AI and cause the generation AI to improve the accuracy of providing the forecast information.

[0085] The providing unit can apply different delivery methods to each local disaster prevention system when providing forecast information. The providing unit applies different delivery methods to each local disaster prevention system when providing forecast information. For example, if the local disaster prevention system uses SMS notification, the forecast information can be provided by SMS. Also, if the local disaster prevention system uses app notification, the forecast information can be provided by app notification. Furthermore, if the local disaster prevention system uses email notification, the forecast information can be provided by email. For example, the providing unit analyzes the type of local disaster prevention system and provides the forecast information by SMS when SMS notification is used. Also, the providing unit can provide the forecast information by app notification when the local disaster prevention system uses app notification. Furthermore, the providing unit can provide the forecast information by email when the local disaster prevention system uses email notification. This enables more effective information provision by applying a delivery method according to the local disaster prevention system. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input local disaster prevention system data to the generation AI and cause the generation AI to apply different delivery methods.

[0086] The providing unit can estimate the emotion of the animal and adjust the order in which the predicted information is provided based on the estimated emotion of the animal. The providing unit can estimate the emotion of the animal and adjust the order in which the predicted information is provided based on the estimated emotion of the animal. For example, if the animal is excited, it can provide heart rate data first. Also, if the animal is relaxed, it can provide body temperature data first. Furthermore, if the animal is anxious, it can provide movement pattern data first. For example, the providing unit can analyze the heart rate data of the animal and provide the heart rate data first if the heart rate fluctuations are large. Also, the providing unit can analyze the body temperature data of the animal and provide the body temperature data first if the body temperature fluctuations are large. Furthermore, the providing unit can analyze the movement pattern data of the animal and provide the movement pattern data first if the movement fluctuations are large. This allows for more appropriate information provision by adjusting the order in which the predicted information is provided based on the emotion of the animal. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input emotion data of the animal to the generating AI and cause the generating AI to adjust the order in which prediction information is provided.

[0087] The providing unit may provide forecast information taking into account the geographical characteristics of the region. The providing unit may provide forecast information taking into account the geographical characteristics of the region. For example, if the region is a mountainous area, the providing unit may provide forecast information taking into account the impact of earthquakes. Furthermore, if the region is an urban area, the providing unit may provide forecast information taking into account the density of buildings. Furthermore, if the region is a coastal area, the providing unit may provide forecast information taking into account the risk of tsunamis. For example, the providing unit may analyze topographical data of the region and provide forecast information taking into account the impact of earthquakes if the region is a mountainous area. Furthermore, the providing unit may analyze population distribution data of the region and provide forecast information taking into account the density of buildings if the region is an urban area. Furthermore, the providing unit may analyze coastal data of the region and provide forecast information taking into account the risk of tsunamis. This allows for more relevant information to be provided by taking into account the geographical characteristics of the region. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input geographical characteristic data of the region into the generating AI and cause the generating AI to provide highly relevant information.

[0088] The providing unit can improve the accuracy of the provided forecast information by referring to literature related to the region when providing the forecast information. The providing unit can improve the accuracy of the provided forecast information by referring to literature related to the region when providing the forecast information. For example, the providing unit can provide forecast information by referring to the latest research papers on earthquakes in the region. The providing unit can also provide forecast information by referring to literature on disaster prevention in the region. Furthermore, the providing unit can provide forecast information by referring to past earthquake data in the region. For example, the providing unit can refer to the latest research papers on earthquakes in the region and reflect the results in the forecast information. The providing unit can also refer to literature on disaster prevention in the region and reflect the results in the forecast information. Furthermore, the providing unit can refer to past earthquake data in the region and reflect the results in the forecast information. In this way, the accuracy of the forecast information is improved by referring to literature related to the region. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input literature data related to the region into the generating AI and cause the generating AI to improve the accuracy of the provided forecast information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors the animal's heart rate, body temperature, and movement patterns in real time using sensors in the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates predictive information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated predictive information to a local disaster prevention system. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit monitors the animal's heart rate, body temperature, and movement patterns in real time using sensors in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates predictive information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated predictive information to a local disaster prevention system. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit monitors the heart rate, body temperature, and movement patterns of an animal in real time using a sensor in the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates predictive information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated predictive information to a local disaster prevention system. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors the animal's heart rate, body temperature, and movement patterns in real time using sensors in the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates predictive information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated predictive information to a local disaster prevention system.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The collection unit can estimate the animal's emotions and adjust the sensitivity of the sensors based on the estimated emotions. For example, if the animal is excited, the sensitivity of the heart rate sensor can be increased to collect more detailed data. If the animal is relaxed, the sensitivity of the body temperature sensor can be adjusted to detect subtle changes. Furthermore, if the animal is feeling anxious, the sensitivity of the acceleration sensor can be increased to record the animal's movement patterns in detail. This allows for more accurate data collection by adjusting the sensitivity of the sensors according to the animal's emotions.

[0091] The collection unit can analyze the animal's past behavioral history and select the optimal sensor placement. For example, if the animal has previously exhibited abnormal behavior in a specific location, sensors can be concentrated in that location. The placement of heart rate sensors and body temperature sensors can also be optimized based on the animal's past behavioral patterns. Furthermore, if the animal exhibits abnormal behavior during a specific time period, the sensitivity of the sensors can be increased during that time period. This improves the accuracy of data collection by selecting the optimal sensor placement based on the animal's past behavioral history.

[0092] The collection unit can filter sensor data collection based on the animal's current health condition. For example, if the animal is healthy, normal data collection is performed. If the animal is sick, abnormal data can be filtered to collect accurate data. Furthermore, if the animal is tired, the frequency of data collection can be adjusted to reduce the burden on the animal. This allows accurate data to be collected by filtering data according to the animal's health condition.

[0093] The collection unit can estimate the emotion of the animal and determine the priority of data to be collected based on the estimated emotion of the animal. For example, if the animal is excited, heart rate data can be collected with priority. Also, if the animal is relaxed, body temperature data can be collected with priority. Furthermore, if the animal is feeling anxious, movement pattern data can be collected with priority. In this way, by determining the priority of data according to the emotion of the animal, important data can be collected with priority.

[0094] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the animal. For example, if the animal is in a specific area, data from that area can be collected preferentially. Also, if the animal is moving, data along its path can be collected preferentially. Furthermore, if the animal exhibits abnormal behavior in a specific location, data from that location can be collected preferentially. In this way, by taking into account the geographical location information of the animal, highly relevant data can be collected preferentially.

[0095] The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. For example, if the animal is excited, the analysis algorithm of the heart rate data can be adjusted. Also, if the animal is relaxed, the analysis algorithm of the body temperature data can be adjusted. Furthermore, if the animal is feeling anxious, the analysis algorithm of the movement pattern data can be adjusted. In this way, the analysis accuracy can be improved by adjusting the analysis algorithm according to the emotion of the animal.

[0096] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the animal's past data. For example, the analysis unit can analyze the current data by referring to the animal's past heart rate data. The analysis unit can also analyze the current data by referring to the animal's past body temperature data. Furthermore, the analysis unit can analyze the current data by referring to the animal's past movement pattern data. In this way, by referring to the animal's past data, the accuracy of the analysis can be improved.

[0097] During analysis, the analysis unit can apply different analysis methods to each type of animal. For example, when analyzing dog data, an analysis method that takes into account dog-specific behavioral patterns can be applied. When analyzing cat data, an analysis method that takes into account cat-specific behavioral patterns can also be applied. Furthermore, when analyzing bird data, an analysis method that takes into account bird-specific behavioral patterns can also be applied. In this way, by applying different analysis methods to each type of animal, the accuracy of the analysis is improved.

[0098] The analysis unit can estimate the animal's emotions and adjust the display method of the analysis results based on the estimated animal's emotions. For example, if the animal is excited, the heart rate data can be emphasized. If the animal is relaxed, the body temperature data can be displayed in detail. Furthermore, if the animal is feeling anxious, the movement pattern data can be emphasized. By adjusting the display method of the analysis results according to the animal's emotions, it is possible to display them in a way that is easier to understand.

[0099] The analysis unit can take into account the geographical distribution of animals during analysis. For example, if an animal is in a specific area, it can prioritize analyzing data from that area. Also, if an animal is moving, it can prioritize analyzing data along its route. Furthermore, if an animal exhibits abnormal behavior in a specific location, it can prioritize analyzing data from that location. This allows for more relevant analysis by taking the geographical distribution of animals into account.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects data from sensors attached to the animal. The sensors attached to the animal include a heart rate sensor, a body temperature sensor, and an acceleration sensor. The collection unit monitors the animal's heart rate in real time using the heart rate sensor, measures the animal's body temperature using the body temperature sensor, and detects the animal's movement patterns using the acceleration sensor. Data is collected when there are abnormal fluctuations or changes. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms (e.g., K-means, random forest, neural network) and determines that any abnormal clusters, classification results, or patterns detected are precursors to earthquakes. Step 3: The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The generation unit generates prediction information based on the analysis results using a generation AI (e.g., a neural network or a generative model). Step 4: The providing unit provides the forecast information generated by the generating unit. The providing unit provides the generated forecast information to a local disaster prevention system (e.g., an alert system, an evacuation guidance system) and notifies local residents. The information can also be provided to local residents through a web application or a mobile application.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above 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 may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. 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. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including 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, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating 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 audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed 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 an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. 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. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including 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, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating 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 audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. 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. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed 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 an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may 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 in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data from sensors attached to the animal; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates prediction information based on the analysis result obtained by the analysis unit; a providing unit that provides the prediction information generated by the generating unit. A system characterized by:

2. The collecting unit Includes heart rate sensor, temperature sensor, and acceleration sensor 2. The system of claim 1.

3. The analysis unit Analyze data using machine learning algorithms 2. The system of claim 1.

4. The generation unit Generate predictive information using generative AI 2. The system of claim 1.

5. The providing unit Provide the generated forecast information to the local disaster prevention system 2. The system of claim 1.

6. The collecting unit To provide a method for estimating an animal's emotion and adjusting the sensitivity of a sensor based on the estimated animal's emotion.

2. The system of claim 1.

7. The collecting unit Analyzing the animal's past behavioral history and selecting appropriate sensor placement 2. The system of claim 1.

8. The collecting unit As sensor data is collected, it is filtered based on the animal's current health status.

2. The system of claim 1.

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