system

Abstract

An object of a system according to an embodiment is to predict danger in a bathing beach in advance and prevent a marine accident.SOLUTION: A system according to an embodiment includes a GPS apparatus, a water flow measurement apparatus, a water temperature measurement apparatus, and a AI analysis unit. The GPS device is mounted on a buoy floating on the sea. The water flow measurement device is mounted on a buoy floating on the sea. The water temperature measuring device is mounted on a buoy floating on the sea. The AI analysis part learns the danger of the bathing beach based on the date collected from the GPS device, the water flow measuring device and the water temperature measuring device, and predicts the danger in the future.SELECTED DRAWING: Figure 1

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 adequately predicted dangers at beaches and prevented marine accidents, and there is room for improvement.

[0005] The system according to the embodiment aims to predict dangers at beaches in advance and prevent marine accidents. [Means for solving the problem]

[0006] The system according to the embodiment comprises a GPS device, a water current measuring device, a water temperature measuring device, and an AI analysis unit. The GPS device is mounted on a buoy floating in the sea. The water current measuring device is mounted on a buoy floating in the sea. The water temperature measuring device is mounted on a buoy floating in the sea. The AI ​​analysis unit learns about dangers at swimming beaches based on data collected from the GPS device, the water current measuring device, and the water temperature measuring device, and predicts future dangers in advance. [Effects of the Invention]

[0007] The system according to the embodiment can predict dangers at beaches in advance and prevent marine accidents. [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) The marine accident prevention system according to an embodiment of the present invention is a system in which a buoy floating in the sea is equipped with a GPS, a water current measuring device, and a water temperature measuring device, and this data is analyzed by a generating AI, which learns the dangers at swimming beaches and predicts dangers in advance in the future to prevent marine accidents. As a result, the marine accident prevention system learns the dangers at swimming beaches and predicts dangers in advance in the future to prevent marine accidents.

[0029] A marine accident prevention system according to an embodiment includes a GPS device, a water current measurement device, a water temperature measurement device, and an AI analysis unit. The GPS device, for example, determines the exact position of a buoy. The water current measurement device, for example, measures the current of seawater. The water temperature measurement device, for example, measures the temperature of seawater. The AI ​​analysis unit, for example, learns about dangers at swimming beaches based on data collected from the GPS device, the water current measurement device, and the water temperature measurement device. The AI ​​analysis unit also identifies under what conditions dangers will occur, for example, by comparing the data with past data. Furthermore, the AI ​​analysis unit, for example, predicts future dangers based on the learned data. In this way, by learning about dangers at swimming beaches and predicting future dangers in advance, marine accidents can be prevented.

[0030] Sensors installed on buoys can detect the movements of marine life and predict danger from abnormal behavior of marine life. By adding sensors to detect the movements of marine life to buoys, for example, abnormal movements of schools of fish or mass outbreaks of jellyfish can be monitored in real time. This allows abnormal behavior of marine life to be detected and potential danger to be predicted. For example, a system can be built to detect the approach of sharks at an early stage. Furthermore, sensors to detect the movements of marine life can be used to detect, for example, sudden movements of schools of fish or abnormal outbreaks of jellyfish and predict danger. This allows potential danger to be predicted from abnormal behavior of marine life, ensuring the safety of beachgoers.

[0031] The AI ​​analysis unit can combine data collected from buoys with past weather data to predict ocean changes due to abnormal weather. For example, the AI ​​analysis unit can combine buoy data with past weather data to build a system that predicts ocean changes due to abnormal weather. For example, it can analyze the effects of typhoons and strong winds and evaluate the safety of beaches. Furthermore, based on past weather data, the AI ​​can learn, for example, the impact of abnormal weather on the ocean. This can predict ocean changes during abnormal weather and warn of danger in advance. Furthermore, by integrating buoy data with weather data, a system can be developed that issues warnings in response to sudden weather changes, for example. This can improve the safety of beaches by predicting ocean changes due to abnormal weather.

[0032] A sensor mounted on a buoy can detect underwater sound waves and predict danger from changes in the sound waves. By adding a sensor to a buoy that detects underwater sound waves, for example, changes in sound waves can be monitored in real time. For example, abnormal sound wave patterns can be detected and potential danger can be predicted. Furthermore, sensors that detect underwater sound waves can be used to build a system that can detect early warning signs of earthquakes and tsunamis, for example. For example, abnormal sound waves can be analyzed and a warning issued. Furthermore, using sound wave detection sensors, for example, AI can analyze underwater sound wave data. This can detect changes in sound waves and predict danger. This can ensure the safety of beachgoers by predicting potential danger from changes in underwater sound waves.

[0033] The AI ​​analysis unit can integrate data collected from buoys with other ocean observation data to make more accurate predictions. The AI ​​analysis unit, for example, integrates buoy data with other ocean observation data to build a system that makes more accurate predictions. For example, it combines tidal data and ocean current data for analysis. Furthermore, it complements buoy data, for example, with other ocean observation data to improve prediction accuracy. For example, it analyzes tidal fluctuations and ocean current movements to predict danger. Furthermore, it integrates buoy data with ocean observation data in real time to develop a system that makes more accurate predictions, for example. For example, it combines and analyzes multiple data sources. This makes it possible to integrate it with other ocean observation data and make more accurate predictions.

[0034] The AI ​​analysis unit can learn the behavioral patterns of beachgoers and predict danger from those patterns. The AI ​​analysis unit, for example, learns the behavioral patterns of beachgoers and builds a system that predicts danger from those patterns. For example, it analyzes the possibility that specific behaviors will cause danger. Furthermore, for example, the AI ​​predicts danger based on the behavioral patterns of beachgoers. For example, it analyzes the possibility that behavior in specific areas will cause danger. Furthermore, the AI ​​can learn the behavioral pattern data and develop a system that predicts danger, for example. For example, it can detect signs that specific behaviors will cause danger. This makes it possible to predict danger from the behavioral patterns of beachgoers and respond quickly.

[0035] The AI ​​analysis unit learns topographical data around a beach and can predict danger from changes in the topography. For example, the AI ​​analysis unit learns topographical data around a beach and builds a system that predicts danger from changes in the topography. For example, it analyzes beach erosion and changes in the topography. Furthermore, based on the topographical data, the AI ​​predicts danger, for example. For example, it analyzes the possibility that changes in the topography may affect the occurrence of rip currents. Furthermore, the AI ​​learns the topographical data and develops a system that predicts danger, for example. For example, it detects signs that changes in the topography may cause danger. In this way, by predicting danger from changes in the topography, it is possible to improve the safety of beaches.

[0036] The AI ​​analysis unit can learn data on fluctuations in the number of users at a beach and predict danger from the congestion situation. The AI ​​analysis unit, for example, learns data on fluctuations in the number of users at a beach and builds a system that predicts danger from the congestion situation. For example, it analyzes dangers when it is crowded. Furthermore, for example, the AI ​​predicts danger based on data on fluctuations in the number of users. For example, it analyzes dangers that are likely to occur when it is crowded. Furthermore, the AI ​​learns data on fluctuations in the number of users and develops a system that predicts danger, for example, it detects danger when it is crowded. In this way, it is possible to improve the safety of beaches by predicting danger from the congestion situation.

[0037] The AI ​​analysis unit can predict danger by referencing past accident data and detecting similar patterns. The AI ​​analysis unit, for example, references past accident data and builds a system that detects similar patterns. For example, it analyzes the conditions under which past accidents occurred. Furthermore, based on the past accident data, for example, the AI ​​predicts danger. For example, it detects similar accident patterns and issues a warning. Furthermore, the accident data can be trained by the AI ​​to develop, for example, a system that predicts danger. For example, it detects patterns of past accident occurrence and predicts danger. This makes it possible to detect similar patterns and predict danger by referring to past accident data.

[0038] The AI ​​analysis unit can refer to real-time weather data and predict danger from changes in weather conditions. The AI ​​analysis unit, for example, references real-time weather data and builds a system that predicts danger from changes in weather conditions. For example, it analyzes sudden changes in weather. Furthermore, based on the real-time weather data, for example, the AI ​​predicts danger. For example, it analyzes the possibility that changes in weather conditions will cause danger. Furthermore, the AI ​​learns the weather data and, for example, develops a system that predicts danger. For example, it detects signs that changes in weather conditions will cause danger. This makes it possible to predict danger from changes in weather conditions by referring to real-time weather data.

[0039] The AI ​​analysis unit can refer to other ocean observation data to make more accurate predictions. The AI ​​analysis unit, for example, references other ocean observation data to build a system that makes more accurate predictions. For example, it combines tidal data and ocean current data for analysis. Furthermore, based on other ocean observation data, for example, the AI ​​predicts danger. For example, it analyzes tidal fluctuations and ocean current movements to predict danger. Furthermore, the AI ​​learns the ocean observation data, and a system that makes more accurate predictions, for example, is developed. For example, it combines and analyzes multiple data sources. In this way, by referring to other ocean observation data, more accurate predictions become possible.

[0040] The AI ​​analysis unit can refer to data on fluctuations in the number of users at a beach and predict danger from the congestion situation. The AI ​​analysis unit, for example, references data on fluctuations in the number of users at a beach and builds a system that predicts danger from the congestion situation. For example, it analyzes danger when it is crowded. Furthermore, for example, AI predicts danger based on data on fluctuations in the number of users. For example, it analyzes dangers that are likely to occur when it is crowded. Furthermore, AI learns data on fluctuations in the number of users and develops a system that predicts danger, for example, it detects danger when it is crowded. In this way, it is possible to improve the safety of beaches by predicting danger from the congestion situation.

[0041] Sensors installed in the watch measure heart rate and body temperature, making it possible to predict danger based on health status. By adding sensors to the watch that measure heart rate and body temperature, a system can be built that predicts danger based on health status. For example, sudden changes in heart rate and abnormal body temperature can be detected. Furthermore, the watch can predict danger based on health status data. For example, it can analyze the possibility that abnormal heart rate or body temperature may cause danger. Furthermore, the watch can learn health status data and develop a system that predicts danger. For example, it can detect signs that changes in heart rate or body temperature may cause danger. This makes it possible to understand health status from changes in heart rate and body temperature, predict danger, and respond quickly.

[0042] The sensors installed in the watch can track location information in real time and predict danger from that location information. By adding a sensor that tracks location information in real time to the watch, a system can be built that predicts danger from location information, for example. For example, danger in a specific area can be analyzed. Furthermore, the watch can predict danger based on location data, for example. For example, it can analyze the possibility that behavior in a specific area will cause danger. Furthermore, the watch can learn location data and a system can be developed that predicts danger, for example. For example, it can detect signs that behavior in a specific area will cause danger. In this way, by tracking location information in real time, danger can be predicted and a quick response can be made.

[0043] The sensors installed in the watch can use the voice recognition function to issue warnings via voice commands. By adding a voice recognition function to the watch, for example, a system can be built that issues warnings via voice commands. For example, the user receives the warning via voice. Furthermore, based on the voice recognition function, for example, the watch can predict danger. For example, it can issue a warning via voice command. Furthermore, by having the watch learn the voice recognition function, a system can be developed that can predict danger. For example, it can issue a warning via voice command. This makes it possible to respond quickly by issuing a warning via voice command.

[0044] The sensor installed in the watch can use a vibration sensor to issue a warning by vibration. By adding a vibration sensor to the watch, for example, a system can be built that issues a warning by vibration. For example, the user receives the warning by vibration. Furthermore, based on the vibration sensor, for example, the watch can predict danger. For example, it can issue a warning by vibration. Furthermore, by having the watch learn the vibration sensor, for example, a system can be developed that predicts danger. For example, it can issue a warning by vibration. This allows for a quick response by issuing a warning by vibration.

[0045] The watch can monitor the user's health condition and predict danger based on the health condition. For example, the watch can monitor the user's health condition and build a system that predicts danger based on the health condition. For example, it can detect abnormalities in heart rate or body temperature. Furthermore, the watch can predict danger based on the health condition data. For example, it can analyze the possibility that abnormalities in heart rate or body temperature could cause danger. Furthermore, the watch can learn the health condition data and develop a system that predicts danger. For example, it can detect signs that changes in heart rate or body temperature could cause danger. This makes it possible to predict danger based on the user's health condition and respond quickly.

[0046] The watch can track location information in real time and predict danger from that information. For example, the watch can track location information in real time and build a system that predicts danger from that information. For example, it can analyze danger in a specific area. Furthermore, the watch can predict danger based on the location data. For example, it can analyze the possibility that behavior in a specific area will cause danger. Furthermore, the watch can learn the location data and develop a system that predicts danger. For example, it can detect signs that behavior in a specific area will cause danger. In this way, by tracking location information in real time, danger can be predicted and a rapid response can be made.

[0047] The watch can issue a warning via voice command using a voice recognition function. For example, a voice recognition function can be added to the watch to build a system that issues a warning via voice command. For example, the user receives the warning via voice. Furthermore, based on the voice recognition function, for example, the watch can predict danger. For example, it can issue a warning via voice command. Furthermore, the watch can be trained to use the voice recognition function to develop a system that predicts danger. For example, it can issue a warning via voice command. This makes it possible to respond quickly by issuing a warning via voice command.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The marine accident prevention system can further be equipped with an acoustic wave detection unit. The acoustic wave detection unit monitors underwater acoustic waves in real time and detects abnormal acoustic wave patterns. For example, a system can be built to detect early warning signs of earthquakes and tsunamis. By analyzing abnormal acoustic waves and issuing warnings, the safety of beachgoers can be ensured. Furthermore, the acoustic wave detection unit can use AI to analyze underwater acoustic data, detect changes in acoustic waves, and predict potential dangers.

[0050] The marine accident prevention system can further include a marine life detection unit. The marine life detection unit monitors the movements of marine life in real time and detects any abnormal behavior. For example, it can detect the sudden movement of a school of fish or a mass outbreak of jellyfish and predict potential danger. Furthermore, a system can be constructed that can detect the approach of sharks at an early stage. This makes it possible to predict potential danger from abnormal behavior of marine life and ensure the safety of beachgoers.

[0051] The maritime accident prevention system can further be equipped with a meteorological data integration unit. This unit combines data collected from buoys with past meteorological data to predict ocean changes due to abnormal weather. For example, it can analyze the effects of typhoons and strong winds and evaluate the safety of beaches. By using past meteorological data, the AI ​​can learn the impact of abnormal weather on the ocean, predicting ocean changes during abnormal weather and providing advance warning of danger.

[0052] The marine accident prevention system can further include a behavioral pattern learning unit. The behavioral pattern learning unit learns the behavioral patterns of beachgoers and analyzes the possibility that specific behaviors may pose a danger. For example, it can detect signs that behavior in a specific area may pose a danger and issue a warning. This makes it possible to predict danger from behavioral patterns and respond quickly.

[0053] The marine accident prevention system can further include a topographical data learning unit. The topographical data learning unit learns topographical data around the beach and predicts danger from changes in the topography. For example, it can analyze the possibility that beach erosion or topographical changes may affect the occurrence of rip currents and issue a warning. This makes it possible to predict danger from changes in topography and improve the safety of beaches.

[0054] The marine accident prevention system can further include a user number fluctuation learning unit. The user number fluctuation learning unit learns data on fluctuations in the number of users at the beach and predicts danger based on the congestion situation. For example, it can analyze dangers that are likely to occur when the beach is crowded and issue warnings. This makes it possible to predict danger based on the congestion situation and improve the safety of the beach.

[0055] The marine accident prevention system can further include a past accident data learning unit. The past accident data learning unit refers to past accident data and detects similar patterns. For example, it can analyze the conditions under which past accidents occurred, detect similar accident patterns, and issue a warning. This makes it possible to detect similar patterns and predict danger by referring to past accident data.

[0056] The maritime accident prevention system can further include a real-time weather data reference unit. The real-time weather data reference unit references real-time weather data and predicts danger from changes in weather conditions. For example, it can analyze sudden weather changes, detect signs that changes in weather conditions may cause danger, and issue a warning. This makes it possible to predict danger from changes in weather conditions by referring to real-time weather data.

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

[0058] Step 1: The GPS device determines the exact location of the buoy. The water current measuring device measures the current of the seawater. The water temperature measuring device measures the temperature of the seawater. Step 2: The AI ​​analysis unit learns about the dangers at the beach based on data collected from GPS devices, water current measuring devices, and water temperature measuring devices, and identifies the conditions under which dangers occur. Step 3: The AI ​​analysis unit uses the learned data to predict future dangers, thereby preventing water accidents.

[0059] (Example 2) The marine accident prevention system according to an embodiment of the present invention is a system in which a buoy floating in the sea is equipped with a GPS, a water current measuring device, and a water temperature measuring device, and this data is analyzed by a generating AI, which learns the dangers at swimming beaches and predicts dangers in advance in the future to prevent marine accidents. As a result, the marine accident prevention system learns the dangers at swimming beaches and predicts dangers in advance in the future to prevent marine accidents.

[0060] A marine accident prevention system according to an embodiment includes a GPS device, a water current measurement device, a water temperature measurement device, and an AI analysis unit. The GPS device, for example, determines the exact position of a buoy. The water current measurement device, for example, measures the current of seawater. The water temperature measurement device, for example, measures the temperature of seawater. The AI ​​analysis unit, for example, learns about dangers at swimming beaches based on data collected from the GPS device, the water current measurement device, and the water temperature measurement device. The AI ​​analysis unit also identifies under what conditions dangers will occur, for example, by comparing the data with past data. Furthermore, the AI ​​analysis unit, for example, predicts future dangers based on the learned data. In this way, by learning about dangers at swimming beaches and predicting future dangers in advance, marine accidents can be prevented.

[0061] Sensors installed on buoys can detect the movements of marine life and predict danger from abnormal behavior of marine life. By adding sensors to detect the movements of marine life to buoys, for example, abnormal movements of schools of fish or mass outbreaks of jellyfish can be monitored in real time. This allows abnormal behavior of marine life to be detected and potential danger to be predicted. For example, a system can be built to detect the approach of sharks at an early stage. Furthermore, sensors to detect the movements of marine life can be used to detect, for example, sudden movements of schools of fish or abnormal outbreaks of jellyfish and predict danger. This allows potential danger to be predicted from abnormal behavior of marine life, ensuring the safety of beachgoers.

[0062] The AI ​​analysis unit can combine data collected from buoys with past weather data to predict ocean changes due to abnormal weather. For example, the AI ​​analysis unit can combine buoy data with past weather data to build a system that predicts ocean changes due to abnormal weather. For example, it can analyze the effects of typhoons and strong winds and evaluate the safety of beaches. Furthermore, based on past weather data, the AI ​​can learn, for example, the impact of abnormal weather on the ocean. This can predict ocean changes during abnormal weather and warn of danger in advance. Furthermore, by integrating buoy data with weather data, a system can be developed that issues warnings in response to sudden weather changes, for example. This can improve the safety of beaches by predicting ocean changes due to abnormal weather.

[0063] The emotion estimation function can collect emotional data from users bathing in the sea and detect potential danger from changes in emotion. The emotion estimation function can be used, for example, to build a system that collects emotional data from users bathing in the sea. For example, it can analyze users' facial expressions and voices and monitor changes in emotion in real time. Furthermore, based on the emotional data of users bathing in the sea, it can detect potential danger from changes in emotion, for example, by issuing a warning if feelings of fear or anxiety intensify. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of users bathing in the sea. This can detect changes in emotion and predict potential danger. This makes it possible to detect potential danger from changes in the emotions of users bathing in the sea and respond quickly.

[0064] A sensor mounted on a buoy can detect underwater sound waves and predict danger from changes in the sound waves. By adding a sensor to a buoy that detects underwater sound waves, for example, changes in sound waves can be monitored in real time. For example, abnormal sound wave patterns can be detected and potential danger can be predicted. Furthermore, sensors that detect underwater sound waves can be used to build a system that can detect early warning signs of earthquakes and tsunamis, for example. For example, abnormal sound waves can be analyzed and a warning issued. Furthermore, using sound wave detection sensors, for example, AI can analyze underwater sound wave data. This can detect changes in sound waves and predict danger. This can ensure the safety of beachgoers by predicting potential danger from changes in underwater sound waves.

[0065] The AI ​​analysis unit can integrate data collected from buoys with other ocean observation data to make more accurate predictions. The AI ​​analysis unit, for example, integrates buoy data with other ocean observation data to build a system that makes more accurate predictions. For example, it combines tidal data and ocean current data for analysis. Furthermore, it complements buoy data, for example, with other ocean observation data to improve prediction accuracy. For example, it analyzes tidal fluctuations and ocean current movements to predict danger. Furthermore, it integrates buoy data with ocean observation data in real time to develop a system that makes more accurate predictions, for example. For example, it combines and analyzes multiple data sources. This makes it possible to integrate it with other ocean observation data and make more accurate predictions.

[0066] The emotion estimation function can analyze the emotional data of beachgoers in real time and issue warnings according to changes in their emotions. The emotion estimation function can be used, for example, to build a system that analyzes the emotional data of beachgoers in real time. For example, it can analyze the user's facial expressions and voice and monitor changes in their emotions. Furthermore, based on the emotional data of beachgoers, it can issue warnings according to changes in their emotions. For example, it can issue a warning if feelings of fear or anxiety become stronger. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of beachgoers in real time. This allows it to detect changes in emotions and issue appropriate warnings. This makes it possible to issue warnings according to changes in the emotions of beachgoers, enabling rapid response.

[0067] The AI ​​analysis unit can learn the behavioral patterns of beachgoers and predict danger from those patterns. The AI ​​analysis unit, for example, learns the behavioral patterns of beachgoers and builds a system that predicts danger from those patterns. For example, it analyzes the possibility that specific behaviors will cause danger. Furthermore, for example, the AI ​​predicts danger based on the behavioral patterns of beachgoers. For example, it analyzes the possibility that behavior in specific areas will cause danger. Furthermore, the AI ​​can learn the behavioral pattern data and develop a system that predicts danger, for example. For example, it can detect signs that specific behaviors will cause danger. This makes it possible to predict danger from the behavioral patterns of beachgoers and respond quickly.

[0068] The AI ​​analysis unit learns topographical data around a beach and can predict danger from changes in the topography. For example, the AI ​​analysis unit learns topographical data around a beach and builds a system that predicts danger from changes in the topography. For example, it analyzes beach erosion and changes in the topography. Furthermore, based on the topographical data, the AI ​​predicts danger, for example. For example, it analyzes the possibility that changes in the topography may affect the occurrence of rip currents. Furthermore, the AI ​​learns the topographical data and develops a system that predicts danger, for example. For example, it detects signs that changes in the topography may cause danger. In this way, by predicting danger from changes in the topography, it is possible to improve the safety of beaches.

[0069] The emotion estimation function learns the emotional data of users bathing in the sea and can predict danger from changes in emotion. The emotion estimation function, for example, builds a system that learns the emotional data of users bathing in the sea. For example, it analyzes the user's facial expressions and voice and learns changes in emotion. Furthermore, based on the emotional data of users bathing in the sea, it predicts danger from changes in emotion, for example. For example, it predicts danger when feelings of fear or anxiety become stronger. Furthermore, by utilizing the emotion estimation function, for example, AI learns the emotional data of users bathing in the sea. This allows it to detect changes in emotion and predict danger. This makes it possible to predict danger from changes in the emotions of users bathing in the sea and respond quickly.

[0070] The AI ​​analysis unit can learn data on fluctuations in the number of users at a beach and predict danger from the congestion situation. The AI ​​analysis unit, for example, learns data on fluctuations in the number of users at a beach and builds a system that predicts danger from the congestion situation. For example, it analyzes dangers when it is crowded. Furthermore, for example, the AI ​​predicts danger based on data on fluctuations in the number of users. For example, it analyzes dangers that are likely to occur when it is crowded. Furthermore, the AI ​​learns data on fluctuations in the number of users and develops a system that predicts danger, for example, it detects danger when it is crowded. In this way, it is possible to improve the safety of beaches by predicting danger from the congestion situation.

[0071] The emotion estimation function can learn the emotional data of beachgoers and issue warnings according to changes in their emotions. The emotion estimation function, for example, builds a system that learns the emotional data of beachgoers. For example, it analyzes the user's facial expressions and voice to learn changes in their emotions. Furthermore, based on the emotional data of beachgoers, it issues warnings according to changes in their emotions. For example, it issues a warning when feelings of fear or anxiety become stronger. Furthermore, by utilizing the emotion estimation function, for example, AI learns the emotional data of beachgoers. This allows it to detect changes in emotions and issue appropriate warnings. This makes it possible to issue warnings according to changes in the emotions of beachgoers, enabling rapid response.

[0072] The AI ​​analysis unit can predict danger by referencing past accident data and detecting similar patterns. The AI ​​analysis unit, for example, references past accident data and builds a system that detects similar patterns. For example, it analyzes the conditions under which past accidents occurred. Furthermore, based on the past accident data, for example, the AI ​​predicts danger. For example, it detects similar accident patterns and issues a warning. Furthermore, the accident data can be trained by the AI ​​to develop, for example, a system that predicts danger. For example, it detects patterns of past accident occurrence and predicts danger. This makes it possible to detect similar patterns and predict danger by referring to past accident data.

[0073] The AI ​​analysis unit can refer to real-time weather data and predict danger from changes in weather conditions. The AI ​​analysis unit, for example, references real-time weather data and builds a system that predicts danger from changes in weather conditions. For example, it analyzes sudden changes in weather. Furthermore, based on the real-time weather data, for example, the AI ​​predicts danger. For example, it analyzes the possibility that changes in weather conditions will cause danger. Furthermore, the AI ​​learns the weather data and, for example, develops a system that predicts danger. For example, it detects signs that changes in weather conditions will cause danger. This makes it possible to predict danger from changes in weather conditions by referring to real-time weather data.

[0074] The emotion estimation function can analyze the emotional data of beachgoers and predict danger from changes in emotion. The emotion estimation function can be used, for example, to build a system that analyzes the emotional data of beachgoers. For example, it can analyze the user's facial expressions and voice and monitor changes in emotion. Furthermore, based on the emotional data of beachgoers, it can predict danger from changes in emotion. For example, it can predict danger when emotions of fear or anxiety intensify. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of beachgoers. This can detect changes in emotion and predict danger. This makes it possible to predict danger from changes in the emotions of beachgoers and respond quickly.

[0075] The AI ​​analysis unit can refer to other ocean observation data to make more accurate predictions. The AI ​​analysis unit, for example, references other ocean observation data to build a system that makes more accurate predictions. For example, it combines tidal data and ocean current data for analysis. Furthermore, based on other ocean observation data, for example, the AI ​​predicts danger. For example, it analyzes tidal fluctuations and ocean current movements to predict danger. Furthermore, the AI ​​learns the ocean observation data, and a system that makes more accurate predictions, for example, is developed. For example, it combines and analyzes multiple data sources. In this way, by referring to other ocean observation data, more accurate predictions become possible.

[0076] The AI ​​analysis unit can refer to data on fluctuations in the number of users at a beach and predict danger from the congestion situation. The AI ​​analysis unit, for example, references data on fluctuations in the number of users at a beach and builds a system that predicts danger from the congestion situation. For example, it analyzes danger when it is crowded. Furthermore, for example, AI predicts danger based on data on fluctuations in the number of users. For example, it analyzes dangers that are likely to occur when it is crowded. Furthermore, AI learns data on fluctuations in the number of users and develops a system that predicts danger, for example, it detects danger when it is crowded. In this way, it is possible to improve the safety of beaches by predicting danger from the congestion situation.

[0077] The emotion estimation function can analyze the emotional data of beachgoers in real time and issue warnings according to changes in their emotions. The emotion estimation function can be used, for example, to build a system that analyzes the emotional data of beachgoers in real time. For example, it can analyze the user's facial expressions and voice and monitor changes in their emotions. Furthermore, based on the emotional data of beachgoers, it can issue warnings according to changes in their emotions. For example, it can issue a warning if feelings of fear or anxiety become stronger. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of beachgoers in real time. This allows it to detect changes in emotions and issue appropriate warnings. This makes it possible to issue warnings according to changes in the emotions of beachgoers, enabling rapid response.

[0078] Sensors installed in the watch measure heart rate and body temperature, making it possible to predict danger based on health status. By adding sensors to the watch that measure heart rate and body temperature, a system can be built that predicts danger based on health status. For example, sudden changes in heart rate and abnormal body temperature can be detected. Furthermore, the watch can predict danger based on health status data. For example, it can analyze the possibility that abnormal heart rate or body temperature may cause danger. Furthermore, the watch can learn health status data and develop a system that predicts danger. For example, it can detect signs that changes in heart rate or body temperature may cause danger. This makes it possible to understand health status from changes in heart rate and body temperature, predict danger, and respond quickly.

[0079] The sensors installed in the watch can track location information in real time and predict danger from that location information. By adding a sensor that tracks location information in real time to the watch, a system can be built that predicts danger from location information, for example. For example, danger in a specific area can be analyzed. Furthermore, the watch can predict danger based on location data, for example. For example, it can analyze the possibility that behavior in a specific area will cause danger. Furthermore, the watch can learn location data and a system can be developed that predicts danger, for example. For example, it can detect signs that behavior in a specific area will cause danger. In this way, by tracking location information in real time, danger can be predicted and a quick response can be made.

[0080] The emotion estimation function can analyze the emotional data of the watch user and predict danger from changes in emotion. The emotion estimation function can be used, for example, to build a system that analyzes the emotional data of the watch user. For example, it can analyze the user's facial expressions and voice and monitor changes in emotion. Furthermore, based on the emotional data of the watch user, it can predict danger from changes in emotion. For example, it can predict danger when emotions of fear or anxiety intensify. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of the watch user. This can detect changes in emotion and predict danger. This makes it possible to predict danger from changes in the watch user's emotions and respond quickly.

[0081] The sensors installed in the watch can use the voice recognition function to issue warnings via voice commands. By adding a voice recognition function to the watch, for example, a system can be built that issues warnings via voice commands. For example, the user receives the warning via voice. Furthermore, based on the voice recognition function, for example, the watch can predict danger. For example, it can issue a warning via voice command. Furthermore, by having the watch learn the voice recognition function, a system can be developed that can predict danger. For example, it can issue a warning via voice command. This makes it possible to respond quickly by issuing a warning via voice command.

[0082] The sensor installed in the watch can use a vibration sensor to issue a warning by vibration. By adding a vibration sensor to the watch, for example, a system can be built that issues a warning by vibration. For example, the user receives the warning by vibration. Furthermore, based on the vibration sensor, for example, the watch can predict danger. For example, it can issue a warning by vibration. Furthermore, by having the watch learn the vibration sensor, for example, a system can be developed that predicts danger. For example, it can issue a warning by vibration. This allows for a quick response by issuing a warning by vibration.

[0083] The emotion estimation function can analyze the emotional data of the watch user in real time and issue a warning according to changes in emotion. The emotion estimation function, for example, builds a system that analyzes the emotional data of the watch user in real time. For example, it analyzes the user's facial expressions and voice and monitors changes in emotion. Furthermore, based on the emotional data of the watch user, it can issue a warning according to changes in emotion, for example, if emotions of fear or anxiety increase. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of the watch user in real time. This allows changes in emotion to be detected and an appropriate warning to be issued. This enables a prompt response by issuing a warning according to changes in the watch user's emotion.

[0084] The watch can monitor the user's health condition and predict danger based on the health condition. For example, the watch can monitor the user's health condition and build a system that predicts danger based on the health condition. For example, it can detect abnormalities in heart rate or body temperature. Furthermore, the watch can predict danger based on the health condition data. For example, it can analyze the possibility that abnormalities in heart rate or body temperature could cause danger. Furthermore, the watch can learn the health condition data and develop a system that predicts danger. For example, it can detect signs that changes in heart rate or body temperature could cause danger. This makes it possible to predict danger based on the user's health condition and respond quickly.

[0085] The emotion estimation function can analyze the emotional data of the watch user and predict danger from changes in emotion. The emotion estimation function can be used, for example, to build a system that analyzes the emotional data of the watch user. For example, it can analyze the user's facial expressions and voice and monitor changes in emotion. Furthermore, based on the emotional data of the watch user, it can predict danger from changes in emotion. For example, it can predict danger when emotions of fear or anxiety intensify. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of the watch user. This can detect changes in emotion and predict danger. This makes it possible to predict danger from changes in the watch user's emotions and respond quickly.

[0086] The watch can track location information in real time and predict danger from that information. For example, the watch can track location information in real time and build a system that predicts danger from that information. For example, it can analyze danger in a specific area. Furthermore, the watch can predict danger based on the location data. For example, it can analyze the possibility that behavior in a specific area will cause danger. Furthermore, the watch can learn the location data and develop a system that predicts danger. For example, it can detect signs that behavior in a specific area will cause danger. In this way, by tracking location information in real time, danger can be predicted and a rapid response can be made.

[0087] The watch can issue a warning via voice command using a voice recognition function. For example, a voice recognition function can be added to the watch to build a system that issues a warning via voice command. For example, the user receives the warning via voice. Furthermore, based on the voice recognition function, for example, the watch can predict danger. For example, it can issue a warning via voice command. Furthermore, the watch can be trained to use the voice recognition function to develop a system that predicts danger. For example, it can issue a warning via voice command. This makes it possible to respond quickly by issuing a warning via voice command.

[0088] The emotion estimation function can analyze the emotional data of the watch user in real time and issue a warning according to changes in emotion. The emotion estimation function, for example, builds a system that analyzes the emotional data of the watch user in real time. For example, it analyzes the user's facial expressions and voice and monitors changes in emotion. Furthermore, based on the emotional data of the watch user, it can issue a warning according to changes in emotion, for example, if emotions of fear or anxiety increase. Furthermore, by utilizing the emotion estimation function, for example, AI can analyze the emotional data of the watch user in real time. This allows changes in emotion to be detected and an appropriate warning to be issued. This enables a prompt response by issuing a warning according to changes in the watch user's emotion.

[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 marine accident prevention system can further be equipped with an acoustic wave detection unit. The acoustic wave detection unit monitors underwater acoustic waves in real time and detects abnormal acoustic wave patterns. For example, a system can be built to detect early warning signs of earthquakes and tsunamis. By analyzing abnormal acoustic waves and issuing warnings, the safety of beachgoers can be ensured. Furthermore, the acoustic wave detection unit can use AI to analyze underwater acoustic data, detect changes in acoustic waves, and predict potential dangers.

[0091] The marine accident prevention system can further include a marine life detection unit. The marine life detection unit monitors the movements of marine life in real time and detects any abnormal behavior. For example, it can detect the sudden movement of a school of fish or a mass outbreak of jellyfish and predict potential danger. Furthermore, a system can be constructed that can detect the approach of sharks at an early stage. This makes it possible to predict potential danger from abnormal behavior of marine life and ensure the safety of beachgoers.

[0092] The maritime accident prevention system can further be equipped with a meteorological data integration unit. This unit combines data collected from buoys with past meteorological data to predict ocean changes due to abnormal weather. For example, it can analyze the effects of typhoons and strong winds and evaluate the safety of beaches. By using past meteorological data, the AI ​​can learn the impact of abnormal weather on the ocean, predicting ocean changes during abnormal weather and providing advance warning of danger.

[0093] The marine accident prevention system can further include an emotion estimation unit. The emotion estimation unit collects emotional data from bathers and monitors emotional changes in real time. For example, it can analyze the user's facial expressions and voice and issue a warning if feelings of fear or anxiety increase. This makes it possible to detect potential danger from changes in emotion and respond quickly.

[0094] The marine accident prevention system can further include a behavioral pattern learning unit. The behavioral pattern learning unit learns the behavioral patterns of beachgoers and analyzes the possibility that specific behaviors may pose a danger. For example, it can detect signs that behavior in a specific area may pose a danger and issue a warning. This makes it possible to predict danger from behavioral patterns and respond quickly.

[0095] The marine accident prevention system can further include a topographical data learning unit. The topographical data learning unit learns topographical data around the beach and predicts danger from changes in the topography. For example, it can analyze the possibility that beach erosion or topographical changes may affect the occurrence of rip currents and issue a warning. This makes it possible to predict danger from changes in topography and improve the safety of beaches.

[0096] The marine accident prevention system can further include a user number fluctuation learning unit. The user number fluctuation learning unit learns data on fluctuations in the number of users at the beach and predicts danger based on the congestion situation. For example, it can analyze dangers that are likely to occur when the beach is crowded and issue warnings. This makes it possible to predict danger based on the congestion situation and improve the safety of the beach.

[0097] The marine accident prevention system can further include a past accident data learning unit. The past accident data learning unit refers to past accident data and detects similar patterns. For example, it can analyze the conditions under which past accidents occurred, detect similar accident patterns, and issue a warning. This makes it possible to detect similar patterns and predict danger by referring to past accident data.

[0098] The maritime accident prevention system can further include a real-time weather data reference unit. The real-time weather data reference unit references real-time weather data and predicts danger from changes in weather conditions. For example, it can analyze sudden weather changes, detect signs that changes in weather conditions may cause danger, and issue a warning. This makes it possible to predict danger from changes in weather conditions by referring to real-time weather data.

[0099] The marine accident prevention system can further include an emotion data learning unit. The emotion data learning unit learns the emotion data of bathers and predicts danger from changes in emotion. For example, it can analyze the user's facial expressions and voice, and if feelings of fear or anxiety increase, it can predict danger and issue a warning. This makes it possible to predict potential danger from changes in emotion and respond quickly.

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

[0101] Step 1: The GPS device determines the exact location of the buoy. The water current measuring device measures the current of the seawater. The water temperature measuring device measures the temperature of the seawater. Step 2: The AI ​​analysis unit learns about the dangers at the beach based on data collected from GPS devices, water current measuring devices, and water temperature measuring devices, and identifies the conditions under which dangers occur. Step 3: The AI ​​analysis unit uses the learned data to predict future dangers, thereby preventing water accidents.

[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 (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

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

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] In the robot 414, 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 robot 414 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.

[0147] 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.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0149] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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. [Explanation of symbols]

[0169] 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. The system is equipped with a GPS device mounted on a floating buoy, a water current measuring device, a water temperature measuring device, and an AI analysis unit that analyzes the data. The AI ​​analysis unit Based on the data collected from the GPS device, water current measuring device, and water temperature measuring device, the system learns about dangers at beaches and predicts future dangers in advance. A system characterized by:

2. The sensor mounted on the buoy includes: Detecting the movements of marine life and predicting danger based on abnormal behavior of said marine life 2. The system of claim 1.

3. The AI ​​analysis unit Combining the data collected from the buoy with past weather data to predict changes in the ocean due to abnormal weather 2. The system of claim 1.

4. The emotion estimation function is Collecting emotional data from beachgoers and detecting potential dangers from changes in their emotions 2. The system of claim 1.

5. The sensor mounted on the buoy includes: Detecting underwater acoustic waves and predicting the danger from changes in the acoustic waves 2. The system of claim 1.

6. The AI ​​analysis unit The data collected from the buoys is integrated with other ocean observation data to make more accurate predictions.

2. The system of claim 1.

7. The emotion estimation function is Analyzes beachgoers' emotional data in real time and issues warnings based on changes in their emotions 2. The system of claim 1.

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