system

Abstract

The system according to the embodiment aims to detect natural disasters in advance by analyzing bird calls. [Solution] A system according to an embodiment includes a sensor unit, an analysis unit, a detection unit, and a warning unit. The sensor unit collects bird calls. The analysis unit analyzes the call data collected by the sensor unit using a generation AI. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The warning unit issues a warning based on the abnormality detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenges of making it difficult to predict natural disasters and lacking effective means to minimize damage.

[0005] The system according to the embodiment aims to detect natural disasters in advance by analyzing bird calls. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, a detection unit, and a warning unit. The sensor unit collects bird calls. The analysis unit analyzes the call data collected by the sensor unit using a generation AI. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The warning unit issues a warning based on the abnormality detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect natural disasters in advance by analyzing bird calls. [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) A natural disaster prediction system according to an embodiment of the present invention collects bird calls and analyzes them with a generation AI to detect signs of natural disasters and issue warnings. The natural disaster prediction system collects bird calls and analyzes them with a generation AI to detect signs of natural disasters and issue warnings. For example, the natural disaster prediction system installs sensors to collect bird calls. The sensors collect bird calls in real time and transmit the data to the cloud. For example, installing sensors in bird habitats, such as mountainous areas and forested areas, allows for wide-area data collection. The collected call data is then input into a generation AI. The generation AI analyzes the collected data and analyzes the call patterns. For example, it identifies abnormal patterns by detecting changes in specific frequencies or rhythms. The generation AI analyzes the call patterns and detects abnormal patterns. If an abnormal pattern is detected, it warns of the possibility of a natural disaster, such as an earthquake. For example, if an unusual change in the rhythm or frequency of the bird calls is detected, it is determined to be a sign of an earthquake and issues a warning. This allows the natural disaster prediction system to minimize the damage caused by natural disasters. This allows the natural disaster prediction system to detect early warning signs of natural disasters, giving people time to prepare for evacuation. It can also detect warning signs of other natural disasters by detecting abnormal patterns of bird calls.

[0029] A natural disaster prediction system according to an embodiment includes a sensor unit, an analysis unit, a detection unit, and an alert unit. The sensor unit collects bird calls. Bird calls include, but are not limited to, bird calls of a specific bird species or a specific frequency range. The sensor unit, for example, collects bird calls in real time and transmits the data to the cloud. Furthermore, the sensor unit is often installed in bird habitats, such as mountainous areas or forested areas. For example, the sensor unit collects bird calls with a high-sensitivity microphone and transmits the data to the cloud. The analysis unit uses a generation AI to analyze the call data collected by the sensor unit. The analysis unit analyzes the call patterns, for example, by detecting changes in specific frequencies or rhythms. The generation AI analyzes the call patterns with high accuracy using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit inputs a prompt to the generation AI, such as "Please analyze the pattern of this call," and the generation AI analyzes the call patterns. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit identifies abnormal bird call patterns, for example, by detecting changes in specific frequencies or rhythms. For example, the detection unit detects changes in the rhythm or frequency of bird calls that are different from normal and identifies the anomaly. The warning unit issues a warning based on the anomaly detected by the detection unit. For example, if an abnormal pattern is detected, the warning unit warns of the possibility of a natural disaster such as an earthquake. For example, the warning unit notifies the user via a smartphone app. As a result, the natural disaster prediction system according to the embodiment can collect bird calls and analyze them with a generation AI to detect precursors to natural disasters and issue a warning. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, if an abnormal pattern is detected, the warning unit can input a prompt to the generation AI, such as "Please warn me of this abnormality," and the generation AI can generate a warning message.

[0030] The sensor unit can collect bird calls in real time and transmit the data to the cloud. Real time includes, but is not limited to, for example, seconds or minutes. The sensor unit, for example, collects bird calls in real time and transmits the data to the cloud. For example, the sensor unit collects bird calls with a high-sensitivity microphone and transmits the data to the cloud. The sensor unit can also efficiently collect a wide range of data by transmitting the collected data to the cloud. This allows for efficient collection of a wide range of data by collecting bird calls in real time and transmitting the data to the cloud. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the collected data to a generation AI, and the generation AI can manage the transmission of the data.

[0031] The analysis unit can analyze the collected data using the generation AI and analyze the call pattern. Call patterns include, but are not limited to, frequency patterns and rhythm patterns. The analysis unit can analyze the collected data using, for example, the generation AI and analyze the call pattern. For example, the analysis unit can input a prompt to the generation AI, such as "Please analyze this call pattern," and the generation AI can analyze the call pattern. The analysis unit can also detect changes in specific frequencies or rhythms using the generation AI. For example, the analysis unit can have the generation AI detect changes in specific frequencies or rhythms and analyze the call pattern. This allows the generation AI to analyze the call pattern with high accuracy. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input collected data to the generation AI, and the generation AI can analyze the data.

[0032] The detection unit can detect a change in a specific frequency or rhythm. The specific frequency includes, for example, a range in Hz, but is not limited to such examples. The change in rhythm includes, for example, a change in tempo, a change in pattern, etc. The detection unit, for example, detects a change in a specific frequency or rhythm to identify an abnormal call pattern. For example, the detection unit detects a change in the rhythm or frequency of the call that is different from normal and identifies the abnormality. The detection unit can also detect a change in a specific frequency or rhythm using a generation AI. For example, the detection unit detects a change in a specific frequency or rhythm using the generation AI and identifies the abnormality. In this way, by detecting a change in a specific frequency or rhythm, an abnormal call pattern can be identified. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input data analyzed by the analysis unit into the generation AI, and the generation AI can detect the abnormality.

[0033] The warning unit can warn of the possibility of a natural disaster, such as an earthquake, when an abnormal pattern is detected. Examples of abnormal patterns include, but are not limited to, comparison with a normal pattern and changes exceeding a specific threshold. For example, the warning unit warns of the possibility of a natural disaster, such as an earthquake, when an abnormal pattern is detected. For example, the warning unit notifies the user via a smartphone app. The warning unit can also issue a warning using a generation AI when an abnormal pattern is detected. For example, the warning unit inputs a prompt, such as "Please warn of this abnormality," to the generation AI, which then generates a warning message. This allows the issuance of a warning when an abnormal pattern is detected to minimize damage from natural disasters. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, when an abnormal pattern is detected, the warning unit inputs a prompt, such as "Please warn of this abnormality," to the generation AI, which then generates a warning message.

[0034] The warning unit can notify the user through a smartphone app. Examples of smartphone apps include, but are not limited to, push notifications and alert sounds. The warning unit can notify the user through, for example, the smartphone app. For example, the warning unit can send a push notification through the smartphone app to notify the user of the warning. The warning unit can also sound an alert through the smartphone app to notify the user of the warning. This allows the warning to be quickly conveyed by notifying the user through the smartphone app. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input a prompt to the generation AI, such as "Please notify me of the warning through the smartphone app," and the generation AI can generate a notification message.

[0035] The sensor unit can estimate the bird's emotion and adjust the timing of collecting bird calls based on the estimated emotion. Examples of bird emotions include, but are not limited to, changes in the tone and frequency of the bird's call. The sensor unit can estimate the bird's emotion and adjust the timing of collecting bird calls based on the estimated emotion. For example, the sensor unit can increase the frequency of bird call collection when the bird is excited. The sensor unit can also decrease the frequency of bird call collection when the bird is relaxed. The sensor unit can also immediately set the timing of bird call collection when the bird is alert. This allows for more effective data collection by adjusting the collection timing based on the bird's emotion. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, the sensor unit can input bird emotion data into a generation AI, which can then adjust the collection timing.

[0036] When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. Specific bird species include, but are not limited to, representative bird species for each region. When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. For example, the sensor unit can be set to collect only bird calls of a specific bird species. The sensor unit can also filter and collect different frequency bands for each bird species. The sensor unit can also use a bird species identification algorithm to preferentially collect bird calls of a specific bird species. This enables more accurate data collection by focusing on a specific bird species. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input a bird species identification algorithm into the generation AI, which can then filter the data.

[0037] The sensor unit can apply noise canceling technology to remove environmental sounds when collecting bird calls. Examples of noise canceling technology include, but are not limited to, active noise canceling and passive noise canceling. The sensor unit can apply noise canceling technology to remove environmental sounds when collecting bird calls. For example, the sensor unit uses filtering technology to separate bird calls from environmental sounds. The sensor unit can also remove environmental sounds in real time using a noise-canceling microphone. The sensor unit can also apply an algorithm that emphasizes the frequency band of bird calls and reduces environmental sounds. By removing environmental sounds, more accurate bird call data can be collected. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input a noise canceling algorithm to a generation AI, which then performs noise cancellation.

[0038] The sensor unit can dynamically change the placement of the sensors when collecting bird calls to select an optimal collection point. The optimal collection point may be based on, for example, sound intensity, the surrounding environment, etc., but is not limited to these examples. The sensor unit can dynamically change the placement of the sensors when collecting bird calls to select an optimal collection point. For example, the sensor unit automatically adjusts the position of the sensors based on the intensity of the bird calls. The sensor unit can also track bird movement patterns and place sensors at optimal collection points. The sensor unit can also dynamically change the placement of the sensors based on environmental conditions (e.g., wind direction, temperature). This allows the optimal collection point to be selected by dynamically changing the placement of the sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input a sensor placement algorithm to a generation AI, which can dynamically change the placement of the sensors.

[0039] The sensor unit can estimate the bird's emotion and determine the priority of the bird's calls to be collected based on the estimated emotion. The priority of the bird's calls includes, but is not limited to, the intensity of the emotion and the frequency of the bird's calls. The sensor unit can estimate the bird's emotion and determine the priority of the bird's calls to be collected based on the estimated emotion. For example, if the bird is excited, the sensor unit can prioritize collecting the bird's calls. Also, if the bird is relaxed, the sensor unit can postpone collecting the bird's calls. Also, if the bird is alert, the sensor unit can prioritize collecting the bird's calls. Thus, by prioritizing the bird's calls to be collected based on the bird's emotion, important data can be collected preferentially. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input the bird's emotion data into the generation AI and determine the priority of the bird's calls to be collected by the generation AI.

[0040] The sensor unit can improve the accuracy of bird song collection based on meteorological data. Examples of meteorological data include, but are not limited to, temperature, humidity, and wind speed. For example, the sensor unit can improve the accuracy of bird song collection by taking meteorological data into consideration. For example, the sensor unit can improve the accuracy of bird song collection by strengthening noise cancellation during rainy weather. Furthermore, when the wind is strong, the sensor unit can adjust the sensitivity of the sensor according to the wind direction. Furthermore, when the temperature is low, the sensor unit can optimize the sensor operation to maintain the accuracy of collection. This improves the accuracy of collection based on meteorological data, thereby enabling more accurate data collection. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input meteorological data into a generation AI, which can improve the accuracy of collection.

[0041] The sensor unit can simultaneously collect the sounds of other animals while collecting bird calls to detect abnormalities. Examples of other animal sounds include, but are not limited to, mammals and insects. For example, the sensor unit simultaneously collects the sounds of other animals while collecting bird calls to detect abnormalities. For example, the sensor unit simultaneously collects the sounds of birds and other animals. The sensor unit can also analyze the sounds of multiple animals to detect abnormal call patterns. The sensor unit can also identify abnormalities by analyzing correlations between the sounds of birds and other animals. By simultaneously collecting the sounds of other animals, the accuracy of abnormality detection is improved. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without AI. For example, the sensor unit can input data on the sounds of other animals into a generation AI, which can then detect abnormalities.

[0042] The sensor unit can implement an energy-saving mode to optimize the energy consumption of the sensor when collecting bird sounds. Examples of the energy-saving mode include, but are not limited to, limiting the operating time of the sensor and adjusting the frequency of data collection. For example, the sensor unit can enable the energy-saving mode during times when there are few bird sounds. The sensor unit can also operate the sensor intermittently to reduce energy consumption. The sensor unit can also optimize energy consumption by adjusting the frequency of bird sound collection. In this way, by implementing the energy-saving mode, the energy consumption of the sensor can be optimized. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input an algorithm for the energy-saving mode into the generation AI, and the generation AI can manage the energy-saving mode.

[0043] The analysis unit can estimate the bird's emotion and adjust the analysis algorithm based on the estimated emotion. Examples of the analysis algorithm include, but are not limited to, machine learning algorithms and statistical analysis algorithms. The analysis unit can estimate the bird's emotion and adjust the analysis algorithm based on the estimated emotion. For example, the analysis unit can increase the sensitivity of the analysis algorithm when the bird is excited. The analysis unit can also decrease the sensitivity of the analysis algorithm when the bird is relaxed. The analysis unit can also instantly adjust the sensitivity of the analysis algorithm when the bird is alert. This improves the analysis accuracy by adjusting the analysis algorithm based on the bird's emotion. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the bird's emotion data into a generation AI, which can then adjust the analysis algorithm.

[0044] During analysis, the analysis unit can classify the call patterns by time of day to detect abnormalities. Examples of time periods include, but are not limited to, morning, afternoon, and night. During analysis, the analysis unit can, for example, classify the call patterns by time of day to detect abnormalities. For example, the analysis unit can detect abnormalities by comparing morning and evening call patterns. The analysis unit can also identify abnormalities by analyzing seasonal call patterns. The analysis unit can also detect abnormalities by analyzing changes in calls during specific time periods. Thus, by classifying by time of day, the accuracy of abnormality detection is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input call data for each time period into a generation AI, which can detect abnormalities.

[0045] During analysis, the analysis unit can identify abnormalities by analyzing the frequency spectrum of the bird's cry in detail. Examples of frequency spectrum include, but are not limited to, FFT analysis and power spectral density. During analysis, the analysis unit can identify abnormalities by analyzing the frequency spectrum of the bird's cry in detail. For example, the analysis unit can analyze the frequency spectrum of the bird's cry to detect abnormal peaks. The analysis unit can also identify abnormalities by analyzing fluctuations in the frequency spectrum. The analysis unit can also detect abnormalities by analyzing the energy distribution of each frequency band of the bird's cry. This improves the accuracy of identifying abnormalities by analyzing the frequency spectrum in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input frequency spectrum data to a generation AI, which can identify abnormalities.

[0046] During analysis, the analysis unit can compare the data with past data to analyze the frequency of anomalies. Past data includes, but is not limited to, data from the past year and data from specific events. During analysis, the analysis unit can compare the data with past data to analyze the frequency of anomalies. For example, the analysis unit compares past data with current data to analyze the frequency of anomalies. The analysis unit can also aggregate and analyze the frequency of anomalies by time. The analysis unit can also analyze the frequency of anomalies by season to identify patterns. This allows for accurate analysis of the frequency of anomalies by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI, which then analyzes the frequency of anomalies.

[0047] The analysis unit can estimate the bird's emotions and adjust the display method of the analysis results based on the estimated bird's emotions. Examples of display methods for the analysis results include, but are not limited to, graph display and text display. The analysis unit can estimate the bird's emotions and adjust the display method of the analysis results based on the estimated bird's emotions. For example, the analysis unit can highlight the analysis results when the bird is excited. The analysis unit can also display the analysis results more subtly when the bird is relaxed. The analysis unit can also immediately display the analysis results when the bird is alert. This allows for a user-friendly display by adjusting the display method of the analysis results based on the bird's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the bird's emotion data into a generation AI, which can then adjust the display method of the analysis results.

[0048] During analysis, the analysis unit can identify the location of the abnormality based on geographical data. Geographical data includes, but is not limited to, GPS data and map data. During analysis, the analysis unit, for example, identifies the location of the abnormality by taking the geographical data into consideration. For example, the analysis unit can identify the location of the abnormality by plotting the location where the bird calls were collected on a map. The analysis unit can also analyze the geographical data to identify the location of the abnormality. The analysis unit can also identify the location of the abnormality by comparing it with the geographical data. In this way, by identifying the location of the abnormality based on the geographical data, the location of the abnormality can be accurately determined. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical data into a generation AI, which can identify the location of the abnormality.

[0049] During analysis, the analysis unit can integrate data from other sensors to improve analysis accuracy. Data from other sensors includes, but is not limited to, seismometer data and weather sensor data. During analysis, the analysis unit can integrate data from other sensors to improve analysis accuracy. For example, the analysis unit can integrate seismometer data with bird song data to improve analysis accuracy. The analysis unit can also integrate data from other sensors (e.g., weather sensors) to improve analysis accuracy. The analysis unit can also integrate data from multiple sensors to identify anomalies. In this way, integrating data from other sensors improves analysis accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other sensors to a generation AI, which then integrates the data.

[0050] The analysis unit can provide a dashboard for visualizing abnormality patterns in real time during analysis. Examples of dashboards include, but are not limited to, real-time updates and visualization of abnormality patterns. For example, the analysis unit can provide a dashboard that displays abnormality patterns in real time during analysis. The analysis unit can also provide a dashboard that displays the frequency of abnormality occurrence in a graph. The analysis unit can also provide a dashboard that displays the location of abnormality occurrence on a map. This allows the abnormality pattern to be visualized in real time, allowing the occurrence status of the abnormality to be immediately grasped. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input abnormality pattern data to a generation AI, which can then generate a dashboard.

[0051] The detection unit can estimate the bird's emotion and adjust the abnormality detection criteria based on the estimated bird's emotion. Examples of the abnormality detection criteria include, but are not limited to, threshold setting and pattern matching. The detection unit can estimate the bird's emotion and adjust the abnormality detection criteria based on the estimated bird's emotion. For example, the detection unit can tighten the abnormality detection criteria when the bird is excited. The detection unit can also loosen the abnormality detection criteria when the bird is relaxed. The detection unit can also instantly adjust the abnormality detection criteria when the bird is alert. Adjusting the abnormality detection criteria based on the bird's emotion improves the accuracy of abnormality detection. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input the bird's emotion data to a generation AI, which can then adjust the abnormality detection criteria.

[0052] The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. Examples of multi-class classification algorithms include, but are not limited to, SVM and random forest. The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. For example, the detection unit can analyze the pattern of the bird's cry to identify the type of abnormality. The detection unit can also identify the type of abnormality using a multi-class classification algorithm. The detection unit can also set different detection criteria for each type of abnormality. This allows appropriate response by identifying the type of abnormality. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input a multi-class classification algorithm into a generation AI, which can identify the type of abnormality.

[0053] The detection unit may introduce a scoring system for quantitatively evaluating the intensity of the abnormality at the time of detection. The scoring system may include, but is not limited to, a score range and evaluation items. For example, the detection unit may evaluate the intensity of the abnormality using the scoring system at the time of detection. The detection unit may also quantify and display the intensity of the abnormality. The detection unit may also set a warning level based on the intensity of the abnormality. This allows the severity of the abnormality to be understood by quantitatively evaluating the intensity of the abnormality. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input the scoring system into a generation AI, which may evaluate the intensity of the abnormality.

[0054] The detection unit can perform time series analysis to predict the time of occurrence of an anomaly upon detection. Examples of time series analysis include, but are not limited to, an ARIMA model and LSTM. The detection unit can perform time series analysis to predict the time of occurrence of an anomaly upon detection. For example, the detection unit predicts the time of occurrence of an anomaly using time series analysis. The detection unit can also predict the time of occurrence of an anomaly based on past data. The detection unit can also predict the time of occurrence of an anomaly and issue a warning. In this way, by predicting the time of occurrence of an anomaly, countermeasures can be taken in advance. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input time series data to a generation AI, which can predict the time of occurrence of an anomaly.

[0055] The detection unit can estimate the bird's emotions and determine the priority of abnormalities based on the estimated bird's emotions. The priority of abnormalities can include, but is not limited to, the severity and frequency of occurrence of the abnormality. The detection unit can estimate the bird's emotions and determine the priority of abnormalities based on the estimated bird's emotions. For example, the detection unit can increase the priority of abnormalities when the bird is excited. The detection unit can also decrease the priority of abnormalities when the bird is relaxed. The detection unit can also immediately set the priority of abnormalities when the bird is alert. By determining the priority of abnormalities based on the bird's emotions, important abnormalities can be addressed with priority. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input bird emotion data to a generation AI, which can then determine the priority of abnormalities.

[0056] The detection unit may add a function to display the location of an abnormality on a map upon detection. Displaying on a map may include, but is not limited to, plotting GPS coordinates, using a variety of maps, and the like. For example, the detection unit may plot and display the location of an abnormality on a map upon detection. The detection unit may also display the location of an abnormality on a map in real time. The detection unit may also display the location of an abnormality on a map and issue an alert. By displaying the location of an abnormality on a map, the location of the abnormality can be visually grasped. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input map data to a generation AI, which may then display the location of the abnormality on a map.

[0057] The detection unit may perform causal analysis to estimate the cause of the anomaly upon detection. Examples of causal analysis include, but are not limited to, Granger causality testing and regression analysis. The detection unit may perform causal analysis to estimate the cause of the anomaly upon detection. For example, the detection unit may associate changes in bird calls with meteorological data to estimate the cause of the anomaly. The detection unit may also associate changes in bird calls with seismograph data to estimate the cause of the anomaly. The detection unit may also associate changes in bird calls with other environmental data to estimate the cause of the anomaly. This allows appropriate countermeasures to be taken by estimating the cause of the anomaly. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit may input causal data into a generation AI, which may then estimate the cause of the anomaly.

[0058] The detection unit can statistically analyze the frequency of anomalies at the time of detection and propose preventive measures. Statistical analysis includes, but is not limited to, average value, standard deviation, and regression analysis. For example, the detection unit can statistically analyze the frequency of anomalies at the time of detection and propose preventive measures. For example, the detection unit can statistically analyze the frequency of anomalies and propose preventive measures. The detection unit can also aggregate the frequency of anomalies by time and propose preventive measures. The detection unit can also analyze the frequency of anomalies by season and propose preventive measures. In this way, effective preventive measures can be proposed by statistically analyzing the frequency of anomalies. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input statistical data to a generation AI, which can then propose preventive measures.

[0059] The warning unit can estimate the bird's emotion and adjust the way the warning is expressed based on the estimated bird's emotion. Examples of warning expression methods include, but are not limited to, text messages and audio alerts. The warning unit can estimate the bird's emotion and adjust the way the warning is expressed based on the estimated bird's emotion. For example, the warning unit can emphasize the warning if the bird is excited. The warning unit can also subtly display the warning if the bird is relaxed. The warning unit can also immediately display the warning if the bird is alert. This allows the warning to be easily understood by adjusting the way the warning is expressed based on the bird's emotion. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input bird emotion data into a generation AI, which can then adjust the way the warning is expressed.

[0060] The warning unit can generate different warning messages depending on the type of abnormality when issuing a warning. Examples of different warning messages include, but are not limited to, earthquake warnings and storm warnings. For example, the warning unit can generate different warning messages depending on the type of abnormality when issuing a warning. For example, if a precursor to an earthquake is detected, the warning unit can generate an earthquake warning message. Furthermore, if a precursor to a storm is detected, the warning unit can also generate a storm warning message. Furthermore, if a precursor to another natural disaster is detected, the warning unit can also generate an appropriate warning message. Thus, by generating warning messages depending on the type of abnormality, appropriate responses can be taken. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input abnormality type data into a generation AI, which can then generate different warning messages.

[0061] The warning unit can propose an optimal evacuation route based on the user's location information when issuing a warning. The optimal evacuation route includes, but is not limited to, distance and the location of a shelter. For example, the warning unit can propose an optimal evacuation route by taking the user's location information into consideration when issuing a warning. For example, the warning unit proposes an optimal evacuation route based on the user's current location. The warning unit can also update the user's location information in real time and adjust the evacuation route. The warning unit can also propose an evacuation route by taking the user's location information and surrounding geographic data into consideration. This enables rapid evacuation by proposing an optimal evacuation route based on the user's location information. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input the user's location information into a generation AI, which can then propose an optimal evacuation route.

[0062] The warning unit can improve the accuracy of the warning by referring to past warning history when issuing a warning. Past warning history includes, but is not limited to, warning history from the past year and warning history from specific events, for example. The warning unit can improve the accuracy of the warning by referring to past warning history when issuing a warning. For example, the warning unit can analyze past warning history to improve the accuracy of the warning. The warning unit can also optimize the warning message based on the past warning history. The warning unit can also adjust the timing of the warning by referring to the past warning history. In this way, the accuracy of the warning is improved by referring to the past warning history. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input past warning history data to the generation AI, which can improve the accuracy of the warning.

[0063] The warning unit can estimate the bird's emotions and determine the priority of the warning based on the estimated bird's emotions. The priority of the warning includes, but is not limited to, the severity and frequency of the warning. The warning unit can estimate the bird's emotions and determine the priority of the warning based on the estimated bird's emotions. For example, the warning unit can increase the priority of the warning if the bird is excited. The warning unit can also decrease the priority of the warning if the bird is relaxed. The warning unit can also immediately set the priority of the warning if the bird is alert. By determining the priority of the warning based on the bird's emotions, important warnings can be issued preferentially. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input bird emotion data into a generation AI, which can then determine the priority of the warning.

[0064] The warning unit may be added with a function to notify the user via email or social media in addition to the smartphone app when a warning is issued. Examples of the email and social media include, but are not limited to, the subject of the email and the content of a post on the social media. For example, the warning unit may notify the user via email in addition to the smartphone app when a warning is issued. The warning unit may also notify the user via social media (e.g., LINE (registered trademark), Twitter (registered trademark), Facebook (registered trademark)). The warning unit may also notify the user simultaneously via multiple channels (e.g., app, email, social media). By notifying the user via multiple channels, the user can quickly receive the warning. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input a notification message to a generation AI, which may then notify the user via multiple channels.

[0065] The warning unit may provide a function to update the abnormality occurrence prediction in real time when issuing a warning. Examples of real-time updates include, but are not limited to, update frequency and data type. For example, the warning unit may update the abnormality occurrence prediction in real time and issue a warning when issuing a warning. The warning unit may also adjust the warning message in real time based on the abnormality occurrence prediction. The warning unit may also provide a dashboard that displays the abnormality occurrence prediction in real time. By updating the abnormality occurrence prediction in real time, it is possible to issue a warning based on the latest information. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input the occurrence prediction data to a generation AI, which may update the data in real time.

[0066] The warning unit can customize the warning message by reflecting user feedback when issuing a warning. User feedback includes, but is not limited to, surveys, user reviews, and the like. The warning unit, for example, customizes the warning message based on user feedback when issuing a warning. The warning unit can also collect user feedback to improve the accuracy of the warning message. The warning unit can also adjust the content of the warning message by reflecting user feedback. In this way, a more appropriate warning message can be provided by reflecting user feedback. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input feedback data to a generation AI, which can then customize the warning message.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. Specific bird species include, but are not limited to, representative bird species for each region. When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. For example, the sensor unit can be set to collect only bird calls of a specific bird species. The sensor unit can also filter and collect different frequency bands for each bird species. The sensor unit can also use a bird species identification algorithm to preferentially collect bird calls of a specific bird species. This enables more accurate data collection by focusing on a specific bird species. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input a bird species identification algorithm into the generation AI, which can then filter the data.

[0069] During analysis, the analysis unit can classify the call patterns by time of day to detect abnormalities. Examples of time periods include, but are not limited to, morning, afternoon, and night. During analysis, the analysis unit can, for example, classify the call patterns by time of day to detect abnormalities. For example, the analysis unit can detect abnormalities by comparing morning and evening call patterns. The analysis unit can also identify abnormalities by analyzing seasonal call patterns. The analysis unit can also detect abnormalities by analyzing changes in calls during specific time periods. Thus, by classifying by time of day, the accuracy of abnormality detection is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input call data for each time period into a generation AI, which can detect abnormalities.

[0070] The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. Examples of multi-class classification algorithms include, but are not limited to, SVM and random forest. The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. For example, the detection unit can analyze the pattern of the bird's cry to identify the type of abnormality. The detection unit can also identify the type of abnormality using a multi-class classification algorithm. The detection unit can also set different detection criteria for each type of abnormality. This allows appropriate response by identifying the type of abnormality. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input a multi-class classification algorithm into a generation AI, which can identify the type of abnormality.

[0071] The warning unit can propose an optimal evacuation route based on the user's location information when issuing a warning. The optimal evacuation route includes, but is not limited to, distance and the location of a shelter. For example, the warning unit can propose an optimal evacuation route by taking the user's location information into consideration when issuing a warning. For example, the warning unit proposes an optimal evacuation route based on the user's current location. The warning unit can also update the user's location information in real time and adjust the evacuation route. The warning unit can also propose an evacuation route by taking the user's location information and surrounding geographic data into consideration. This enables rapid evacuation by proposing an optimal evacuation route based on the user's location information. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input the user's location information into a generation AI, which can then propose an optimal evacuation route.

[0072] The warning unit may be added with a function to notify the user via email or social media in addition to the smartphone app when a warning is issued. Examples of the email and social media include, but are not limited to, the subject of the email and the content of a post on the social media. For example, the warning unit may notify the user via email in addition to the smartphone app when a warning is issued. The warning unit may also notify the user via social media (e.g., Twitter, Facebook). The warning unit may also notify the user simultaneously via multiple channels (e.g., app, email, social media). This allows the user to receive the warning quickly by notifying the user via multiple channels. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input a notification message to a generation AI, which may then notify the user via multiple channels.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The sensor unit collects bird calls. Bird calls include specific bird species and specific frequency ranges. The sensor unit collects bird calls in real time and sends the data to the cloud. The sensor unit is often installed in bird habitats such as mountainous areas and forested areas. For example, a highly sensitive microphone is used to collect bird calls and send the data to the cloud. Step 2: The analysis unit uses the generation AI to analyze the call data collected by the sensor unit. The analysis unit analyzes the call pattern by detecting changes in specific frequencies and rhythms. The generation AI analyzes the call pattern with high accuracy using text generation AI, multimodal generation AI, etc. For example, the analysis unit inputs a prompt to the generation AI such as "Please analyze this call pattern," and the generation AI analyzes the call pattern. Step 3: The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit identifies abnormal call patterns by detecting changes in specific frequencies or rhythms. For example, the detection unit detects changes in the rhythm or frequency of calls that are different from normal and identifies the abnormality. Step 4: The warning unit issues a warning based on the anomaly detected by the detection unit. If an abnormal pattern is detected, the warning unit warns of the possibility of a natural disaster such as an earthquake. For example, the warning unit notifies the user via a smartphone app. The processing in the warning unit may be performed using AI, and the generation AI may be prompted to "warn of this anomaly," which then generates a warning message.

[0075] (Example 2) A natural disaster prediction system according to an embodiment of the present invention collects bird calls and analyzes them with a generation AI to detect signs of natural disasters and issue warnings. The natural disaster prediction system collects bird calls and analyzes them with a generation AI to detect signs of natural disasters and issue warnings. For example, the natural disaster prediction system installs sensors to collect bird calls. The sensors collect bird calls in real time and transmit the data to the cloud. For example, installing sensors in bird habitats, such as mountainous areas and forested areas, allows for wide-area data collection. The collected call data is then input into a generation AI. The generation AI analyzes the collected data and analyzes the call patterns. For example, it identifies abnormal patterns by detecting changes in specific frequencies or rhythms. The generation AI analyzes the call patterns and detects abnormal patterns. If an abnormal pattern is detected, it warns of the possibility of a natural disaster, such as an earthquake. For example, if an unusual change in the rhythm or frequency of the bird calls is detected, it is determined to be a sign of an earthquake and issues a warning. This allows the natural disaster prediction system to minimize the damage caused by natural disasters. This allows the natural disaster prediction system to detect early warning signs of natural disasters, giving people time to prepare for evacuation. It can also detect warning signs of other natural disasters by detecting abnormal patterns of bird calls.

[0076] A natural disaster prediction system according to an embodiment includes a sensor unit, an analysis unit, a detection unit, and an alert unit. The sensor unit collects bird calls. Bird calls include, but are not limited to, bird calls of a specific bird species or a specific frequency range. The sensor unit, for example, collects bird calls in real time and transmits the data to the cloud. Furthermore, the sensor unit is often installed in bird habitats, such as mountainous areas or forested areas. For example, the sensor unit collects bird calls with a high-sensitivity microphone and transmits the data to the cloud. The analysis unit uses a generation AI to analyze the call data collected by the sensor unit. The analysis unit analyzes the call patterns, for example, by detecting changes in specific frequencies or rhythms. The generation AI analyzes the call patterns with high accuracy using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit inputs a prompt to the generation AI, such as "Please analyze the pattern of this call," and the generation AI analyzes the call patterns. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit identifies abnormal bird call patterns, for example, by detecting changes in specific frequencies or rhythms. For example, the detection unit detects changes in the rhythm or frequency of bird calls that are different from normal and identifies the anomaly. The warning unit issues a warning based on the anomaly detected by the detection unit. For example, if an abnormal pattern is detected, the warning unit warns of the possibility of a natural disaster such as an earthquake. For example, the warning unit notifies the user via a smartphone app. As a result, the natural disaster prediction system according to the embodiment can collect bird calls and analyze them with a generation AI to detect precursors to natural disasters and issue a warning. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, if an abnormal pattern is detected, the warning unit can input a prompt to the generation AI, such as "Please warn me of this abnormality," and the generation AI can generate a warning message.

[0077] The sensor unit can collect bird calls in real time and transmit the data to the cloud. Real time includes, but is not limited to, for example, seconds or minutes. The sensor unit, for example, collects bird calls in real time and transmits the data to the cloud. For example, the sensor unit collects bird calls with a high-sensitivity microphone and transmits the data to the cloud. The sensor unit can also efficiently collect a wide range of data by transmitting the collected data to the cloud. This allows for efficient collection of a wide range of data by collecting bird calls in real time and transmitting the data to the cloud. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the collected data to a generation AI, and the generation AI can manage the transmission of the data.

[0078] The analysis unit can analyze the collected data using the generation AI and analyze the call pattern. Call patterns include, but are not limited to, frequency patterns and rhythm patterns. The analysis unit can analyze the collected data using, for example, the generation AI and analyze the call pattern. For example, the analysis unit can input a prompt to the generation AI, such as "Please analyze this call pattern," and the generation AI can analyze the call pattern. The analysis unit can also detect changes in specific frequencies or rhythms using the generation AI. For example, the analysis unit can have the generation AI detect changes in specific frequencies or rhythms and analyze the call pattern. This allows the generation AI to analyze the call pattern with high accuracy. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input collected data to the generation AI, and the generation AI can analyze the data.

[0079] The detection unit can detect a change in a specific frequency or rhythm. The specific frequency includes, for example, a range in Hz, but is not limited to such examples. The change in rhythm includes, for example, a change in tempo, a change in pattern, etc. The detection unit, for example, detects a change in a specific frequency or rhythm to identify an abnormal call pattern. For example, the detection unit detects a change in the rhythm or frequency of the call that is different from normal and identifies the abnormality. The detection unit can also detect a change in a specific frequency or rhythm using a generation AI. For example, the detection unit detects a change in a specific frequency or rhythm using the generation AI and identifies the abnormality. In this way, by detecting a change in a specific frequency or rhythm, an abnormal call pattern can be identified. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input data analyzed by the analysis unit into the generation AI, and the generation AI can detect the abnormality.

[0080] The warning unit can warn of the possibility of a natural disaster, such as an earthquake, when an abnormal pattern is detected. Examples of abnormal patterns include, but are not limited to, comparison with a normal pattern and changes exceeding a specific threshold. For example, the warning unit warns of the possibility of a natural disaster, such as an earthquake, when an abnormal pattern is detected. For example, the warning unit notifies the user via a smartphone app. The warning unit can also issue a warning using a generation AI when an abnormal pattern is detected. For example, the warning unit inputs a prompt, such as "Please warn of this abnormality," to the generation AI, which then generates a warning message. This allows the issuance of a warning when an abnormal pattern is detected to minimize damage from natural disasters. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, when an abnormal pattern is detected, the warning unit inputs a prompt, such as "Please warn of this abnormality," to the generation AI, which then generates a warning message.

[0081] The warning unit can notify the user through a smartphone app. Examples of smartphone apps include, but are not limited to, push notifications and alert sounds. The warning unit can notify the user through, for example, the smartphone app. For example, the warning unit can send a push notification through the smartphone app to notify the user of the warning. The warning unit can also sound an alert through the smartphone app to notify the user of the warning. This allows the warning to be quickly conveyed by notifying the user through the smartphone app. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input a prompt to the generation AI, such as "Please notify me of the warning through the smartphone app," and the generation AI can generate a notification message.

[0082] The sensor unit can estimate the bird's emotion and adjust the timing of collecting bird calls based on the estimated emotion. Examples of bird emotions include, but are not limited to, changes in the tone and frequency of the bird's call. The sensor unit can estimate the bird's emotion and adjust the timing of collecting bird calls based on the estimated emotion. For example, the sensor unit can increase the frequency of bird call collection when the bird is excited. The sensor unit can also decrease the frequency of bird call collection when the bird is relaxed. The sensor unit can also immediately set the timing of bird call collection when the bird is alert. This allows for more effective data collection by adjusting the collection timing based on the bird's emotion. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, the sensor unit can input bird emotion data into a generation AI, which can then adjust the collection timing.

[0083] When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. Specific bird species include, but are not limited to, representative bird species for each region. When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. For example, the sensor unit can be set to collect only bird calls of a specific bird species. The sensor unit can also filter and collect different frequency bands for each bird species. The sensor unit can also use a bird species identification algorithm to preferentially collect bird calls of a specific bird species. This enables more accurate data collection by focusing on a specific bird species. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input a bird species identification algorithm into the generation AI, which can then filter the data.

[0084] The sensor unit can apply noise canceling technology to remove environmental sounds when collecting bird calls. Examples of noise canceling technology include, but are not limited to, active noise canceling and passive noise canceling. The sensor unit can apply noise canceling technology to remove environmental sounds when collecting bird calls. For example, the sensor unit uses filtering technology to separate bird calls from environmental sounds. The sensor unit can also remove environmental sounds in real time using a noise-canceling microphone. The sensor unit can also apply an algorithm that emphasizes the frequency band of bird calls and reduces environmental sounds. By removing environmental sounds, more accurate bird call data can be collected. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input a noise canceling algorithm to a generation AI, which then performs noise cancellation.

[0085] The sensor unit can dynamically change the placement of the sensors when collecting bird calls to select an optimal collection point. The optimal collection point may be based on, for example, sound intensity, the surrounding environment, etc., but is not limited to these examples. The sensor unit can dynamically change the placement of the sensors when collecting bird calls to select an optimal collection point. For example, the sensor unit automatically adjusts the position of the sensors based on the intensity of the bird calls. The sensor unit can also track bird movement patterns and place sensors at optimal collection points. The sensor unit can also dynamically change the placement of the sensors based on environmental conditions (e.g., wind direction, temperature). This allows the optimal collection point to be selected by dynamically changing the placement of the sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input a sensor placement algorithm to a generation AI, which can dynamically change the placement of the sensors.

[0086] The sensor unit can estimate the bird's emotion and determine the priority of the bird's calls to be collected based on the estimated emotion. The priority of the bird's calls includes, but is not limited to, the intensity of the emotion and the frequency of the bird's calls. The sensor unit can estimate the bird's emotion and determine the priority of the bird's calls to be collected based on the estimated emotion. For example, if the bird is excited, the sensor unit can prioritize collecting the bird's calls. Also, if the bird is relaxed, the sensor unit can postpone collecting the bird's calls. Also, if the bird is alert, the sensor unit can prioritize collecting the bird's calls. Thus, by prioritizing the bird's calls to be collected based on the bird's emotion, important data can be collected preferentially. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input the bird's emotion data into the generation AI and determine the priority of the bird's calls to be collected by the generation AI.

[0087] The sensor unit can improve the accuracy of bird song collection based on meteorological data. Examples of meteorological data include, but are not limited to, temperature, humidity, and wind speed. For example, the sensor unit can improve the accuracy of bird song collection by taking meteorological data into consideration. For example, the sensor unit can improve the accuracy of bird song collection by strengthening noise cancellation during rainy weather. Furthermore, when the wind is strong, the sensor unit can adjust the sensitivity of the sensor according to the wind direction. Furthermore, when the temperature is low, the sensor unit can optimize the sensor operation to maintain the accuracy of collection. This improves the accuracy of collection based on meteorological data, thereby enabling more accurate data collection. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input meteorological data into a generation AI, which can improve the accuracy of collection.

[0088] The sensor unit can simultaneously collect the sounds of other animals while collecting bird calls to detect abnormalities. Examples of other animal sounds include, but are not limited to, mammals and insects. For example, the sensor unit simultaneously collects the sounds of other animals while collecting bird calls to detect abnormalities. For example, the sensor unit simultaneously collects the sounds of birds and other animals. The sensor unit can also analyze the sounds of multiple animals to detect abnormal call patterns. The sensor unit can also identify abnormalities by analyzing correlations between the sounds of birds and other animals. By simultaneously collecting the sounds of other animals, the accuracy of abnormality detection is improved. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without AI. For example, the sensor unit can input data on the sounds of other animals into a generation AI, which can then detect abnormalities.

[0089] The sensor unit can implement an energy-saving mode to optimize the energy consumption of the sensor when collecting bird sounds. Examples of the energy-saving mode include, but are not limited to, limiting the operating time of the sensor and adjusting the frequency of data collection. For example, the sensor unit can enable the energy-saving mode during times when there are few bird sounds. The sensor unit can also operate the sensor intermittently to reduce energy consumption. The sensor unit can also optimize energy consumption by adjusting the frequency of bird sound collection. In this way, by implementing the energy-saving mode, the energy consumption of the sensor can be optimized. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input an algorithm for the energy-saving mode into the generation AI, and the generation AI can manage the energy-saving mode.

[0090] The analysis unit can estimate the bird's emotion and adjust the analysis algorithm based on the estimated emotion. Examples of the analysis algorithm include, but are not limited to, machine learning algorithms and statistical analysis algorithms. The analysis unit can estimate the bird's emotion and adjust the analysis algorithm based on the estimated emotion. For example, the analysis unit can increase the sensitivity of the analysis algorithm when the bird is excited. The analysis unit can also decrease the sensitivity of the analysis algorithm when the bird is relaxed. The analysis unit can also instantly adjust the sensitivity of the analysis algorithm when the bird is alert. This improves the analysis accuracy by adjusting the analysis algorithm based on the bird's emotion. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the bird's emotion data into a generation AI, which can then adjust the analysis algorithm.

[0091] During analysis, the analysis unit can classify the call patterns by time of day to detect abnormalities. Examples of time periods include, but are not limited to, morning, afternoon, and night. During analysis, the analysis unit can, for example, classify the call patterns by time of day to detect abnormalities. For example, the analysis unit can detect abnormalities by comparing morning and evening call patterns. The analysis unit can also identify abnormalities by analyzing seasonal call patterns. The analysis unit can also detect abnormalities by analyzing changes in calls during specific time periods. Thus, by classifying by time of day, the accuracy of abnormality detection is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input call data for each time period into a generation AI, which can detect abnormalities.

[0092] During analysis, the analysis unit can identify abnormalities by analyzing the frequency spectrum of the bird's cry in detail. Examples of frequency spectrum include, but are not limited to, FFT analysis and power spectral density. During analysis, the analysis unit can identify abnormalities by analyzing the frequency spectrum of the bird's cry in detail. For example, the analysis unit can analyze the frequency spectrum of the bird's cry to detect abnormal peaks. The analysis unit can also identify abnormalities by analyzing fluctuations in the frequency spectrum. The analysis unit can also detect abnormalities by analyzing the energy distribution of each frequency band of the bird's cry. This improves the accuracy of identifying abnormalities by analyzing the frequency spectrum in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input frequency spectrum data to a generation AI, which can identify abnormalities.

[0093] During analysis, the analysis unit can compare the data with past data to analyze the frequency of anomalies. Past data includes, but is not limited to, data from the past year and data from specific events. During analysis, the analysis unit can compare the data with past data to analyze the frequency of anomalies. For example, the analysis unit compares past data with current data to analyze the frequency of anomalies. The analysis unit can also aggregate and analyze the frequency of anomalies by time. The analysis unit can also analyze the frequency of anomalies by season to identify patterns. This allows for accurate analysis of the frequency of anomalies by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI, which then analyzes the frequency of anomalies.

[0094] The analysis unit can estimate the bird's emotions and adjust the display method of the analysis results based on the estimated bird's emotions. Examples of display methods for the analysis results include, but are not limited to, graph display and text display. The analysis unit can estimate the bird's emotions and adjust the display method of the analysis results based on the estimated bird's emotions. For example, the analysis unit can highlight the analysis results when the bird is excited. The analysis unit can also display the analysis results more subtly when the bird is relaxed. The analysis unit can also immediately display the analysis results when the bird is alert. This allows for a user-friendly display by adjusting the display method of the analysis results based on the bird's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the bird's emotion data into a generation AI, which can then adjust the display method of the analysis results.

[0095] During analysis, the analysis unit can identify the location of the abnormality based on geographical data. Geographical data includes, but is not limited to, GPS data and map data. During analysis, the analysis unit, for example, identifies the location of the abnormality by taking the geographical data into consideration. For example, the analysis unit can identify the location of the abnormality by plotting the location where the bird calls were collected on a map. The analysis unit can also analyze the geographical data to identify the location of the abnormality. The analysis unit can also identify the location of the abnormality by comparing it with the geographical data. In this way, by identifying the location of the abnormality based on the geographical data, the location of the abnormality can be accurately determined. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical data into a generation AI, which can identify the location of the abnormality.

[0096] During analysis, the analysis unit can integrate data from other sensors to improve analysis accuracy. Data from other sensors includes, but is not limited to, seismometer data and weather sensor data. During analysis, the analysis unit can integrate data from other sensors to improve analysis accuracy. For example, the analysis unit can integrate seismometer data with bird song data to improve analysis accuracy. The analysis unit can also integrate data from other sensors (e.g., weather sensors) to improve analysis accuracy. The analysis unit can also integrate data from multiple sensors to identify anomalies. In this way, integrating data from other sensors improves analysis accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other sensors to a generation AI, which then integrates the data.

[0097] The analysis unit can provide a dashboard for visualizing abnormality patterns in real time during analysis. Examples of dashboards include, but are not limited to, real-time updates and visualization of abnormality patterns. For example, the analysis unit can provide a dashboard that displays abnormality patterns in real time during analysis. The analysis unit can also provide a dashboard that displays the frequency of abnormality occurrence in a graph. The analysis unit can also provide a dashboard that displays the location of abnormality occurrence on a map. This allows the abnormality pattern to be visualized in real time, allowing the occurrence status of the abnormality to be immediately grasped. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input abnormality pattern data to a generation AI, which can then generate a dashboard.

[0098] The detection unit can estimate the bird's emotion and adjust the abnormality detection criteria based on the estimated bird's emotion. Examples of the abnormality detection criteria include, but are not limited to, threshold setting and pattern matching. The detection unit can estimate the bird's emotion and adjust the abnormality detection criteria based on the estimated bird's emotion. For example, the detection unit can tighten the abnormality detection criteria when the bird is excited. The detection unit can also loosen the abnormality detection criteria when the bird is relaxed. The detection unit can also instantly adjust the abnormality detection criteria when the bird is alert. Adjusting the abnormality detection criteria based on the bird's emotion improves the accuracy of abnormality detection. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input the bird's emotion data to a generation AI, which can then adjust the abnormality detection criteria.

[0099] The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. Examples of multi-class classification algorithms include, but are not limited to, SVM and random forest. The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. For example, the detection unit can analyze the pattern of the bird's cry to identify the type of abnormality. The detection unit can also identify the type of abnormality using a multi-class classification algorithm. The detection unit can also set different detection criteria for each type of abnormality. This allows appropriate response by identifying the type of abnormality. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input a multi-class classification algorithm into a generation AI, which can identify the type of abnormality.

[0100] The detection unit may introduce a scoring system for quantitatively evaluating the intensity of the abnormality at the time of detection. The scoring system may include, but is not limited to, a score range and evaluation items. For example, the detection unit may evaluate the intensity of the abnormality using the scoring system at the time of detection. The detection unit may also quantify and display the intensity of the abnormality. The detection unit may also set a warning level based on the intensity of the abnormality. This allows the severity of the abnormality to be understood by quantitatively evaluating the intensity of the abnormality. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input the scoring system into a generation AI, which may evaluate the intensity of the abnormality.

[0101] The detection unit can perform time series analysis to predict the time of occurrence of an anomaly upon detection. Examples of time series analysis include, but are not limited to, an ARIMA model and LSTM. The detection unit can perform time series analysis to predict the time of occurrence of an anomaly upon detection. For example, the detection unit predicts the time of occurrence of an anomaly using time series analysis. The detection unit can also predict the time of occurrence of an anomaly based on past data. The detection unit can also predict the time of occurrence of an anomaly and issue a warning. In this way, by predicting the time of occurrence of an anomaly, countermeasures can be taken in advance. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input time series data to a generation AI, which can predict the time of occurrence of an anomaly.

[0102] The detection unit can estimate the bird's emotions and determine the priority of abnormalities based on the estimated bird's emotions. The priority of abnormalities can include, but is not limited to, the severity and frequency of occurrence of the abnormality. The detection unit can estimate the bird's emotions and determine the priority of abnormalities based on the estimated bird's emotions. For example, the detection unit can increase the priority of abnormalities when the bird is excited. The detection unit can also decrease the priority of abnormalities when the bird is relaxed. The detection unit can also immediately set the priority of abnormalities when the bird is alert. By determining the priority of abnormalities based on the bird's emotions, important abnormalities can be addressed with priority. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input bird emotion data to a generation AI, which can then determine the priority of abnormalities.

[0103] The detection unit may add a function to display the location of an abnormality on a map upon detection. Displaying on a map may include, but is not limited to, plotting GPS coordinates, using a variety of maps, and the like. For example, the detection unit may plot and display the location of an abnormality on a map upon detection. The detection unit may also display the location of an abnormality on a map in real time. The detection unit may also display the location of an abnormality on a map and issue an alert. By displaying the location of an abnormality on a map, the location of the abnormality can be visually grasped. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input map data to a generation AI, which may then display the location of the abnormality on a map.

[0104] The detection unit may perform causal analysis to estimate the cause of the anomaly upon detection. Examples of causal analysis include, but are not limited to, Granger causality testing and regression analysis. The detection unit may perform causal analysis to estimate the cause of the anomaly upon detection. For example, the detection unit may associate changes in bird calls with meteorological data to estimate the cause of the anomaly. The detection unit may also associate changes in bird calls with seismograph data to estimate the cause of the anomaly. The detection unit may also associate changes in bird calls with other environmental data to estimate the cause of the anomaly. This allows appropriate countermeasures to be taken by estimating the cause of the anomaly. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit may input causal data into a generation AI, which may then estimate the cause of the anomaly.

[0105] The detection unit can statistically analyze the frequency of anomalies at the time of detection and propose preventive measures. Statistical analysis includes, but is not limited to, average value, standard deviation, and regression analysis. For example, the detection unit can statistically analyze the frequency of anomalies at the time of detection and propose preventive measures. For example, the detection unit can statistically analyze the frequency of anomalies and propose preventive measures. The detection unit can also aggregate the frequency of anomalies by time and propose preventive measures. The detection unit can also analyze the frequency of anomalies by season and propose preventive measures. In this way, effective preventive measures can be proposed by statistically analyzing the frequency of anomalies. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input statistical data to a generation AI, which can then propose preventive measures.

[0106] The warning unit can estimate the bird's emotion and adjust the way the warning is expressed based on the estimated bird's emotion. Examples of warning expression methods include, but are not limited to, text messages and audio alerts. The warning unit can estimate the bird's emotion and adjust the way the warning is expressed based on the estimated bird's emotion. For example, the warning unit can emphasize the warning if the bird is excited. The warning unit can also subtly display the warning if the bird is relaxed. The warning unit can also immediately display the warning if the bird is alert. This allows the warning to be easily understood by adjusting the way the warning is expressed based on the bird's emotion. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input bird emotion data into a generation AI, which can then adjust the way the warning is expressed.

[0107] The warning unit can generate different warning messages depending on the type of abnormality when issuing a warning. Examples of different warning messages include, but are not limited to, earthquake warnings and storm warnings. For example, the warning unit can generate different warning messages depending on the type of abnormality when issuing a warning. For example, if a precursor to an earthquake is detected, the warning unit can generate an earthquake warning message. Furthermore, if a precursor to a storm is detected, the warning unit can also generate a storm warning message. Furthermore, if a precursor to another natural disaster is detected, the warning unit can also generate an appropriate warning message. Thus, by generating warning messages depending on the type of abnormality, appropriate responses can be taken. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input abnormality type data into a generation AI, which can then generate different warning messages.

[0108] The warning unit can propose an optimal evacuation route based on the user's location information when issuing a warning. The optimal evacuation route includes, but is not limited to, distance and the location of a shelter. For example, the warning unit can propose an optimal evacuation route by taking the user's location information into consideration when issuing a warning. For example, the warning unit proposes an optimal evacuation route based on the user's current location. The warning unit can also update the user's location information in real time and adjust the evacuation route. The warning unit can also propose an evacuation route by taking the user's location information and surrounding geographic data into consideration. This enables rapid evacuation by proposing an optimal evacuation route based on the user's location information. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input the user's location information into a generation AI, which can then propose an optimal evacuation route.

[0109] The warning unit can improve the accuracy of the warning by referring to past warning history when issuing a warning. Past warning history includes, but is not limited to, warning history from the past year and warning history from specific events, for example. The warning unit can improve the accuracy of the warning by referring to past warning history when issuing a warning. For example, the warning unit can analyze past warning history to improve the accuracy of the warning. The warning unit can also optimize the warning message based on the past warning history. The warning unit can also adjust the timing of the warning by referring to the past warning history. In this way, the accuracy of the warning is improved by referring to the past warning history. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input past warning history data to the generation AI, which can improve the accuracy of the warning.

[0110] The warning unit can estimate the bird's emotions and determine the priority of the warning based on the estimated bird's emotions. The priority of the warning includes, but is not limited to, the severity and frequency of the warning. The warning unit can estimate the bird's emotions and determine the priority of the warning based on the estimated bird's emotions. For example, the warning unit can increase the priority of the warning if the bird is excited. The warning unit can also decrease the priority of the warning if the bird is relaxed. The warning unit can also immediately set the priority of the warning if the bird is alert. By determining the priority of the warning based on the bird's emotions, important warnings can be issued preferentially. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input bird emotion data into a generation AI, which can then determine the priority of the warning.

[0111] The warning unit may be added with a function to notify the user via email or social media in addition to the smartphone app when a warning is issued. Examples of the email and social media include, but are not limited to, the subject of the email and the content of a post on the social media. For example, the warning unit may notify the user via email in addition to the smartphone app when a warning is issued. The warning unit may also notify the user via social media (e.g., LINE, Twitter, Facebook). The warning unit may also notify the user via multiple channels (e.g., app, email, social media) simultaneously. This allows the user to receive the warning quickly by notifying the user via multiple channels. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input a notification message to a generation AI, which may then notify the user via multiple channels.

[0112] The warning unit may provide a function to update the abnormality occurrence prediction in real time when issuing a warning. Examples of real-time updates include, but are not limited to, update frequency and data type. For example, the warning unit may update the abnormality occurrence prediction in real time and issue a warning when issuing a warning. The warning unit may also adjust the warning message in real time based on the abnormality occurrence prediction. The warning unit may also provide a dashboard that displays the abnormality occurrence prediction in real time. By updating the abnormality occurrence prediction in real time, it is possible to issue a warning based on the latest information. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input the occurrence prediction data to a generation AI, which may update the data in real time.

[0113] The warning unit can customize the warning message by reflecting user feedback when issuing a warning. User feedback includes, but is not limited to, surveys, user reviews, and the like. The warning unit, for example, customizes the warning message based on user feedback when issuing a warning. The warning unit can also collect user feedback to improve the accuracy of the warning message. The warning unit can also adjust the content of the warning message by reflecting user feedback. In this way, a more appropriate warning message can be provided by reflecting user feedback. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input feedback data to a generation AI, which can then customize the warning message. === Hard Collateral 1-1 === Each of the multiple elements including the sensor unit, analysis unit, detection unit, and warning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensor unit collects bird sounds using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the cloud via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the bird sound data using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities based on the analyzed data. The warning unit is realized, for example, by the control unit 46A of the smart device 14 and issues a warning to the user when an abnormality is detected. === Hard Collateral 1-2 === Each of the multiple elements including the sensor unit, analysis unit, detection unit, and warning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit collects bird calls using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the cloud via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the call data using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities based on the analyzed data. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214 and issues a warning to the user when an abnormality is detected. === Hard Collateral 1-3 === Each of the multiple elements including the sensor unit, analysis unit, detection unit, and warning unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the sensor unit collects bird calls using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the cloud by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the call data using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities based on the analyzed data. The warning unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and issues a warning to the user when an abnormality is detected. === Hard Collateral 1-4 === Each of the multiple elements including the sensor unit, analysis unit, detection unit, and warning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit collects bird sounds using the camera 42 and microphone 238 of the robot 414 and transmits the data to the cloud by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the bird sound data using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities based on the analyzed data. The warning unit is realized, for example, by the control unit 46A of the robot 414 and issues a warning to the user when an abnormality is detected.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. Specific bird species include, but are not limited to, representative bird species for each region. When collecting bird calls, the sensor unit can filter the data by focusing on a specific bird species. For example, the sensor unit can be set to collect only bird calls of a specific bird species. The sensor unit can also filter and collect different frequency bands for each bird species. The sensor unit can also use a bird species identification algorithm to preferentially collect bird calls of a specific bird species. This enables more accurate data collection by focusing on a specific bird species. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input a bird species identification algorithm into the generation AI, which can then filter the data.

[0116] During analysis, the analysis unit can classify the call patterns by time of day to detect abnormalities. Examples of time periods include, but are not limited to, morning, afternoon, and night. During analysis, the analysis unit can, for example, classify the call patterns by time of day to detect abnormalities. For example, the analysis unit can detect abnormalities by comparing morning and evening call patterns. The analysis unit can also identify abnormalities by analyzing seasonal call patterns. The analysis unit can also detect abnormalities by analyzing changes in calls during specific time periods. Thus, by classifying by time of day, the accuracy of abnormality detection is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input call data for each time period into a generation AI, which can detect abnormalities.

[0117] The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. Examples of multi-class classification algorithms include, but are not limited to, SVM and random forest. The detection unit can apply a multi-class classification algorithm to identify the type of abnormality during detection. For example, the detection unit can analyze the pattern of the bird's cry to identify the type of abnormality. The detection unit can also identify the type of abnormality using a multi-class classification algorithm. The detection unit can also set different detection criteria for each type of abnormality. This allows appropriate response by identifying the type of abnormality. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input a multi-class classification algorithm into a generation AI, which can identify the type of abnormality.

[0118] The warning unit can propose an optimal evacuation route based on the user's location information when issuing a warning. The optimal evacuation route includes, but is not limited to, distance and the location of a shelter. For example, the warning unit can propose an optimal evacuation route by taking the user's location information into consideration when issuing a warning. For example, the warning unit proposes an optimal evacuation route based on the user's current location. The warning unit can also update the user's location information in real time and adjust the evacuation route. The warning unit can also propose an evacuation route by taking the user's location information and surrounding geographic data into consideration. This enables rapid evacuation by proposing an optimal evacuation route based on the user's location information. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input the user's location information into a generation AI, which can then propose an optimal evacuation route.

[0119] The warning unit may be added with a function to notify the user via email or social media in addition to the smartphone app when a warning is issued. Examples of the email and social media include, but are not limited to, the subject of the email and the content of a post on the social media. For example, the warning unit may notify the user via email in addition to the smartphone app when a warning is issued. The warning unit may also notify the user via social media (e.g., Twitter, Facebook). The warning unit may also notify the user simultaneously via multiple channels (e.g., app, email, social media). This allows the user to receive the warning quickly by notifying the user via multiple channels. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input a notification message to a generation AI, which may then notify the user via multiple channels.

[0120] The sensor unit can estimate the bird's emotion and adjust the timing of collecting bird calls based on the estimated emotion. Examples of bird emotions include, but are not limited to, changes in the tone and frequency of the bird's call. The sensor unit can estimate the bird's emotion and adjust the timing of collecting bird calls based on the estimated emotion. For example, the sensor unit can increase the frequency of bird call collection when the bird is excited. The sensor unit can also decrease the frequency of bird call collection when the bird is relaxed. The sensor unit can also immediately set the timing of bird call collection when the bird is alert. This allows for more effective data collection by adjusting the collection timing based on the bird's emotion. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, the sensor unit can input bird emotion data into a generation AI, which can then adjust the collection timing.

[0121] The analysis unit can estimate the bird's emotion and adjust the analysis algorithm based on the estimated emotion. Examples of the analysis algorithm include, but are not limited to, machine learning algorithms and statistical analysis algorithms. The analysis unit can estimate the bird's emotion and adjust the analysis algorithm based on the estimated emotion. For example, the analysis unit can increase the sensitivity of the analysis algorithm when the bird is excited. The analysis unit can also decrease the sensitivity of the analysis algorithm when the bird is relaxed. The analysis unit can also instantly adjust the sensitivity of the analysis algorithm when the bird is alert. This improves the analysis accuracy by adjusting the analysis algorithm based on the bird's emotion. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the bird's emotion data into a generation AI, which can then adjust the analysis algorithm.

[0122] The detection unit can estimate the bird's emotion and adjust the abnormality detection criteria based on the estimated bird's emotion. Examples of the abnormality detection criteria include, but are not limited to, threshold setting and pattern matching. The detection unit can estimate the bird's emotion and adjust the abnormality detection criteria based on the estimated bird's emotion. For example, the detection unit can tighten the abnormality detection criteria when the bird is excited. The detection unit can also loosen the abnormality detection criteria when the bird is relaxed. The detection unit can also instantly adjust the abnormality detection criteria when the bird is alert. Adjusting the abnormality detection criteria based on the bird's emotion improves the accuracy of abnormality detection. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input the bird's emotion data to a generation AI, which can then adjust the abnormality detection criteria.

[0123] The warning unit can estimate the bird's emotion and adjust the way the warning is expressed based on the estimated bird's emotion. Examples of warning expression methods include, but are not limited to, text messages and audio alerts. The warning unit can estimate the bird's emotion and adjust the way the warning is expressed based on the estimated bird's emotion. For example, the warning unit can emphasize the warning if the bird is excited. The warning unit can also subtly display the warning if the bird is relaxed. The warning unit can also immediately display the warning if the bird is alert. This allows the warning to be easily understood by adjusting the way the warning is expressed based on the bird's emotion. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input bird emotion data into a generation AI, which can then adjust the way the warning is expressed.

[0124] The warning unit can estimate the bird's emotions and determine the priority of the warning based on the estimated bird's emotions. The priority of the warning includes, but is not limited to, the severity and frequency of the warning. The warning unit can estimate the bird's emotions and determine the priority of the warning based on the estimated bird's emotions. For example, the warning unit can increase the priority of the warning if the bird is excited. The warning unit can also decrease the priority of the warning if the bird is relaxed. The warning unit can also immediately set the priority of the warning if the bird is alert. By determining the priority of the warning based on the bird's emotions, important warnings can be issued preferentially. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input bird emotion data into a generation AI, which can then determine the priority of the warning.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The sensor unit collects bird calls. Bird calls include specific bird species and specific frequency ranges. The sensor unit collects bird calls in real time and sends the data to the cloud. The sensor unit is often installed in bird habitats such as mountainous areas and forested areas. For example, a highly sensitive microphone is used to collect bird calls and send the data to the cloud. Step 2: The analysis unit uses the generation AI to analyze the call data collected by the sensor unit. The analysis unit analyzes the call pattern by detecting changes in specific frequencies and rhythms. The generation AI analyzes the call pattern with high accuracy using text generation AI, multimodal generation AI, etc. For example, the analysis unit inputs a prompt to the generation AI such as "Please analyze this call pattern," and the generation AI analyzes the call pattern. Step 3: The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit identifies abnormal call patterns by detecting changes in specific frequencies or rhythms. For example, the detection unit detects changes in the rhythm or frequency of calls that are different from normal and identifies the abnormality. Step 4: The warning unit issues a warning based on the anomaly detected by the detection unit. If an abnormal pattern is detected, the warning unit warns of the possibility of a natural disaster such as an earthquake. For example, the warning unit notifies the user via a smartphone app. The processing in the warning unit may be performed using AI, and the generation AI may be prompted to "warn of this anomaly," which then generates a warning message.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sensor unit that collects bird calls; an analysis unit that analyzes the bird call data collected by the sensor unit using a generation AI; a detection unit that detects an abnormality based on the data analyzed by the analysis unit; a warning unit that issues a warning based on the abnormality detected by the detection unit. A system characterized by:

2. The sensor unit Collect bird calls in real time and send the data to the cloud 2. The system of claim 1.

3. The analysis unit Generative AI analyzes the collected data and analyzes the call patterns.

2. The system of claim 1.

4. The detection unit Detecting changes in specific frequencies or rhythms 2. The system of claim 1.

5. The warning unit If an unusual pattern is detected, it will warn of a possible natural disaster such as an earthquake.

2. The system of claim 1.

6. The warning unit Notify users via smartphone app 2. The system of claim 1.

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