System

Abstract

An object of a system according to an embodiment is to predict appearance and disappearance of bears and provide an appropriate countermeasure.SOLUTION: A system includes an appearance record acquisition part, a prediction part, a sound generation part, and a voice guidance part. The appearance record acquisition part acquires an appearance record of the bear. The prediction part predicts an appearance area and an appearance time of the bear based on the record acquired by the appearance record acquisition part. The sound generation part generates a sound and a frequency disliked by the bear. The voice guide part guides a method for escaping from the bear and a method for exterminating the bear by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology did not provide sufficient preventive measures or countermeasures against bear sightings, posing a high risk of human casualties.

[0005] The system according to the embodiment aims to predict bear sightings and provide appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a sighting record acquisition unit, a prediction unit, a sound generation unit, and an audio guidance unit. The sighting record acquisition unit acquires bear sighting records. The prediction unit predicts bear sighting areas and times based on the records acquired by the sighting record acquisition unit. The sound generation unit generates sounds and frequencies that bears dislike. The audio guidance unit provides audio guidance on how to escape from and defeat bears. [Effects of the Invention]

[0007] The system according to the embodiment can predict bear sightings and provide appropriate countermeasures. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The application according to the embodiment of the present invention is a system that inputs records of bear sightings in residential and agricultural areas into AI, predicts areas and times of bear sightings, generates sounds and frequencies that bears dislike, and provides audio guidance on how to escape from or exterminate bears. This makes it possible for the application to prevent human harm caused by bears and to provide an environment where humans and bears can coexist.

[0029] The application according to the embodiment includes a sighting record acquisition unit, a prediction unit, a sound generation unit, and a voice guidance unit. The sighting record acquisition unit acquires bear sighting records. For example, it collects sighting information, footprint records, camera trap data, and the like. The sighting record acquisition unit also has a function for accepting reports from local residents. For example, residents can report bear sightings through the application. The prediction unit predicts bear sighting areas and times based on the records acquired by the sighting record acquisition unit. For example, the generation AI analyzes past sighting records to identify areas where bears are likely to appear during specific seasons or times of day. The prediction unit can also incorporate weather and vegetation data to make more accurate predictions. For example, it predicts the risk of bear sightings on rainy days based on weather data. The sound generation unit generates sounds and frequencies that bears dislike. For example, generating specific high-frequency sounds can keep bears away. The sound generation unit is activated when a user launches the application and presses a specific button. For example, pressing the button when a bear approaches generates a sound. The voice guidance section provides audio guidance on how to escape from or repel a bear. For example, if a user encounters a bear, they can speak to the application and the AI ​​will provide audio guidance on appropriate measures. For example, in response to the question, "I encountered a bear. What should I do?", the AI ​​would respond with, "Stay calm and slowly back away from the bear." This allows the application to prevent human harm caused by bears and provide an environment where humans and bears can coexist. For example, by predicting areas and times where bears are likely to appear, residents and farmers can take precautions in advance. Furthermore, by emitting sounds and frequencies that bears dislike, encounters with bears can be avoided. Furthermore, audio guidance on how to escape from or repel a bear enables appropriate responses in emergencies.

[0030] The prediction unit incorporates weather data and vegetation data into the generation AI in addition to bear sighting records, enabling more accurate sighting predictions. For example, the prediction unit collects local weather data in addition to bear sighting records and incorporates this data into the generation AI. For example, it correlates and analyzes past sighting records with the weather conditions at the time to predict the risk of sightings under specific weather conditions. The prediction unit also collects vegetation data and incorporates it into the generation AI. For example, it analyzes the distribution of plants that bears prefer and predicts the risk of sightings in that area. In this way, by incorporating weather data and vegetation data, more accurate sighting predictions become possible.

[0031] The prediction unit tracks bear behavior patterns in real time, and the generation AI can make instantaneous predictions of bear appearances based on that data. For example, the prediction unit attaches a GPS tracker to the bear and collects its location information in real time. The generation AI uses this data to analyze the bear's movement patterns and make instantaneous predictions of bear appearances. The prediction unit also installs sensors to track bear behavior patterns in real time. For example, camera traps and sensors that detect animal movement are used to monitor bear behavior. This makes it possible to track bear behavior patterns in real time and make instantaneous predictions of bear appearances.

[0032] The prediction unit can apply bear sighting prediction data to predict the sightings of other wild animals, developing a comprehensive wildlife management application. For example, the prediction unit collects sighting records of deer and wild boar along with bear sighting records and incorporates them into the generation AI. This allows the development of an application that predicts the sightings of multiple wild animals. The prediction unit can also analyze the behavioral patterns of other wild animals and make sighting predictions. For example, it can analyze the movement patterns of deer and wild boar and predict the risk of their sighting. This can be applied to predicting the sightings of wild animals other than bears, making comprehensive wildlife management possible.

[0033] The prediction unit can add a function to link bear sighting prediction data with a local disaster prevention system and issue simultaneous alerts to residents. For example, the prediction unit links bear sighting prediction data to a local disaster prevention system and issues simultaneous alerts to residents when there is a high risk of a bear sighting. For example, this can be done using smartphone push notifications or disaster prevention radio. The prediction unit also links with the local disaster prevention system to provide evacuation guidance. For example, it can guide residents living in areas with a high risk of bear sightings along evacuation routes. By linking with the local disaster prevention system, this makes it possible to issue alerts to residents quickly.

[0034] The sound generating unit can be equipped with a function to generate specific light and vibration in addition to the sound and frequency that bears dislike, allowing for a combined approach to keeping bears away. For example, the sound generating unit can be equipped with a function to generate strong light in addition to the high-frequency sound that bears dislike. For example, a flashlight can be used to scare and keep bears away. The sound generating unit can also be equipped with a function to generate vibration. For example, a vibration device can be used to keep bears away. This makes it possible to more effectively keep bears away by combining sound, light, and vibration.

[0035] The sound generation unit monitors the bear's reaction in real time, and the generation AI can automatically adjust the optimal sound and frequency based on that reaction. For example, the sound generation unit is equipped with a sensor that monitors the bear's reaction in real time, and the generation AI analyzes the data and automatically adjusts the optimal sound and frequency. For example, it increases the sound intensity when a bear approaches. The sound generation unit also monitors changes in the bear's behavior and adjusts the type of sound based on that reaction. For example, if a bear is sensitive to a particular sound, it will generate that sound. This makes it possible to effectively keep bears away by monitoring the bear's reaction in real time and automatically adjusting the optimal sound and frequency.

[0036] The sound generation unit's function of generating sounds and frequencies that bears dislike can be applied to other wild animals to develop a comprehensive wild animal repellent application. The sound generation unit can also apply data on sounds and frequencies that bears dislike to other wild animals to develop a comprehensive repellent application. For example, it can generate sounds that are effective against wolves and coyotes. The sound generation unit can also analyze the reactions of other wild animals and generate optimal sounds and frequencies based on that data. For example, it can generate specific sounds that wolves dislike. This makes it possible to repel wild animals other than bears, thereby enabling comprehensive wild animal repellent applications.

[0037] The sound generator can be equipped on agricultural drones with a device that generates sounds and frequencies that bears dislike, enabling bear repellent over a wide area. For example, the sound generator can be equipped on agricultural drones with a device that generates sounds and frequencies that bears dislike, enabling bear repellent over a wide area. For example, the drone can generate sounds while patrolling farmland. The sound generator can also set the drone's flight route and generate sounds in specific areas. For example, the drone can focus its patrols on areas where bears are likely to appear. This makes it possible to repell bears over a wide area using agricultural drones.

[0038] The audio guidance unit can provide audio guidance on measures to prevent bear encounters in addition to methods to escape from and repel bears. For example, the audio guidance unit provides audio guidance on how to manage food at a campsite in addition to methods to escape from and repel bears. For example, the audio guidance unit instructs not to leave food inside the tent. The audio guidance unit also provides guidance on how to select a campsite as a preventive measure to prevent bear encounters. For example, the audio guidance unit instructs not to leave food inside the tent. In this way, by providing audio guidance on preventive measures to prevent bear encounters, it is possible to reduce the risk of encountering a bear.

[0039] The voice guidance unit can acquire the user's location information in real time and provide voice guidance on the optimal escape route and evacuation site based on that location. The voice guidance unit, for example, uses the GPS function of the user's smartphone to acquire location information in real time and provides voice guidance on the optimal escape route based on that location. For example, it may provide guidance on the nearest safe place. The voice guidance unit also provides guidance on evacuation sites based on the user's location information. For example, it may provide guidance on locations away from areas where bears are likely to appear. This allows for quick response in emergencies by providing guidance on the optimal escape route and evacuation site based on the user's location information.

[0040] The audio guidance unit can apply the audio guidance on how to escape from and kill bears to other wild animals, allowing the development of a comprehensive wildlife control application. The audio guidance unit can apply the audio guidance on how to escape from and kill bears to other wild animals such as snakes and scorpions, allowing the development of a comprehensive wildlife control application. For example, the audio guidance unit provides audio guidance on what to do when encountering a snake. The audio guidance unit can also provide advance guidance on measures to take against other wild animals. For example, the audio guidance unit can provide guidance on precautions to take in areas where scorpions are likely to appear. This makes it possible to implement comprehensive wildlife control measures that also cover wild animals other than bears.

[0041] The voice guidance unit can be equipped with a voice guidance function in a wearable device, allowing the user to use it easily. For example, the voice guidance unit can be equipped with a voice guidance function for how to escape from or defeat a bear in a smartwatch, allowing the user to use it easily. For example, guidance can be provided through the voice assistant of the smartwatch. The voice guidance unit can also be equipped with a voice guidance function in a fitness tracker. For example, guidance on how to escape from a bear can be provided using the voice function of the fitness tracker. This allows the user to easily use voice guidance by using the wearable device.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] In addition to bear sighting records, the prediction unit can collect local traffic data and incorporate it into the generation AI. For example, it can correlate past sighting records with the traffic conditions at the time and analyze them to predict the risk of bear sightings under specific traffic conditions. The prediction unit can also collect local event information and incorporate it into the generation AI. For example, it can predict the risk of bear sightings when a large-scale event is held in the area. By incorporating traffic data and event information, this makes it possible to make more accurate sighting predictions.

[0044] The sound generator can add a function to emit a specific scent in addition to sounds and frequencies that bears dislike, allowing for a combined approach to keep bears away. For example, a device can be installed that emits a specific scent that bears dislike. The sound generator can also be equipped with a function to adjust the scent intensity. For example, the scent intensity can be increased when a bear approaches. This combination of sound and scent makes it possible to more effectively keep bears away.

[0045] The sound generator, a device that emits sounds and frequencies that bears dislike, can be incorporated into a home security system. For example, it can be linked to a home security camera to automatically emit a sound when a bear approaches. The sound generator can also be linked to a home smart home device. For example, it can notify users of an approaching bear through a smart speaker. In this way, by linking it with a home security system, bear repellent measures can be implemented within the home.

[0046] The prediction unit can add a function to link bear sighting prediction data with local tourism businesses and guide tourists to safe tourist routes. For example, it can guide tourists to tourist routes that avoid areas with a high risk of bear sightings. The prediction unit can also provide safety information for tourist destinations. For example, it can guide tourists to avoid times when bears are more likely to appear. In this way, by linking with local tourism businesses, it becomes possible to provide tourists with safe tourist routes.

[0047] The sound generator can be a device that generates sounds and frequencies that bears dislike and is installed in a vehicle so that the driver can use it when they encounter a bear. For example, the sound can be generated in conjunction with the vehicle's horn. The sound generator can also be linked to a vehicle sensor. For example, it can automatically generate a sound when the vehicle approaches a bear. By installing the device in a vehicle, the driver can avoid a bear when they encounter one.

[0048] The sound generator, which generates sounds and frequencies that repel bears, can be installed in a portable outdoor device and used while camping or hiking. For example, the sound can be generated through a speaker built into the portable device. The sound generator can also be linked to the GPS function of the portable device. For example, a sound can be automatically generated when approaching an area where bears are likely to be found. This makes it possible to use the portable outdoor device to repel bears while camping or hiking.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The bear sighting record acquisition unit acquires bear sighting records, such as sighting information, footprint records, and camera trap data. The application also has a function to accept reports from local residents, allowing them to report bear sightings. Step 2: The prediction unit predicts bear sighting areas and times based on the records acquired by the sighting record acquisition unit. For example, the generation AI analyzes past sighting records to identify areas where bears are likely to appear during specific seasons or times of day. It can also incorporate weather and vegetation data to make more accurate predictions. Step 3: The sound generator generates sounds and frequencies that bears dislike. For example, it can keep bears away by generating a specific high-frequency sound. The sound generator is activated when the user launches the application and presses a specific button. For example, when a bear approaches, pressing the button will generate a sound. Step 4: The voice guidance section provides voice guidance on how to escape from or defeat the bear. For example, if a user encounters a bear and speaks to the application, the AI ​​will provide voice guidance on appropriate measures. For example, in response to the question, "I've encountered a bear. What should I do?", the AI ​​will respond with something like, "Stay calm and slowly back away from the bear."

[0051] (Example 2) The application according to the embodiment of the present invention is a system that inputs records of bear sightings in residential and agricultural areas into AI, predicts areas and times of bear sightings, generates sounds and frequencies that bears dislike, and provides audio guidance on how to escape from or exterminate bears. This makes it possible for the application to prevent human harm caused by bears and to provide an environment where humans and bears can coexist.

[0052] The application according to the embodiment includes a sighting record acquisition unit, a prediction unit, a sound generation unit, and a voice guidance unit. The sighting record acquisition unit acquires bear sighting records. For example, it collects sighting information, footprint records, camera trap data, and the like. The sighting record acquisition unit also has a function for accepting reports from local residents. For example, residents can report bear sightings through the application. The prediction unit predicts bear sighting areas and times based on the records acquired by the sighting record acquisition unit. For example, the generation AI analyzes past sighting records to identify areas where bears are likely to appear during specific seasons or times of day. The prediction unit can also incorporate weather and vegetation data to make more accurate predictions. For example, it predicts the risk of bear sightings on rainy days based on weather data. The sound generation unit generates sounds and frequencies that bears dislike. For example, generating specific high-frequency sounds can keep bears away. The sound generation unit is activated when a user launches the application and presses a specific button. For example, pressing the button when a bear approaches generates a sound. The voice guidance section provides audio guidance on how to escape from or repel a bear. For example, if a user encounters a bear, they can speak to the application and the AI ​​will provide audio guidance on appropriate measures. For example, in response to the question, "I encountered a bear. What should I do?", the AI ​​would respond with, "Stay calm and slowly back away from the bear." This allows the application to prevent human harm caused by bears and provide an environment where humans and bears can coexist. For example, by predicting areas and times where bears are likely to appear, residents and farmers can take precautions in advance. Furthermore, by emitting sounds and frequencies that bears dislike, encounters with bears can be avoided. Furthermore, audio guidance on how to escape from or repel a bear enables appropriate responses in emergencies.

[0053] The prediction unit incorporates weather data and vegetation data into the generation AI in addition to bear sighting records, enabling more accurate sighting predictions. For example, the prediction unit collects local weather data in addition to bear sighting records and incorporates this data into the generation AI. For example, it correlates and analyzes past sighting records with the weather conditions at the time to predict the risk of sightings under specific weather conditions. The prediction unit also collects vegetation data and incorporates it into the generation AI. For example, it analyzes the distribution of plants that bears prefer and predicts the risk of sightings in that area. In this way, by incorporating weather data and vegetation data, more accurate sighting predictions become possible.

[0054] The prediction unit tracks bear behavior patterns in real time, and the generation AI can make instantaneous predictions of bear appearances based on that data. For example, the prediction unit attaches a GPS tracker to the bear and collects its location information in real time. The generation AI uses this data to analyze the bear's movement patterns and make instantaneous predictions of bear appearances. The prediction unit also installs sensors to track bear behavior patterns in real time. For example, camera traps and sensors that detect animal movement are used to monitor bear behavior. This makes it possible to track bear behavior patterns in real time and make instantaneous predictions of bear appearances.

[0055] The prediction unit uses the emotion estimation function to analyze the user's feelings of anxiety and fear, and can adjust the frequency and content of the appearance prediction notifications based on those feelings. For example, the prediction unit collects emotional data when the user uses the app, and the generation AI analyzes that data. For example, if the user is feeling anxious, the prediction unit increases the frequency of appearance prediction notifications. The prediction unit also analyzes the user's feelings of fear and adjusts the notification content based on those emotions. For example, if the user is feeling fear, more detailed information is provided. This makes it possible to provide more appropriate information by adjusting the notification frequency and content based on the user's emotions.

[0056] The prediction unit can apply bear sighting prediction data to predict the sightings of other wild animals, developing a comprehensive wildlife management application. For example, the prediction unit collects sighting records of deer and wild boar along with bear sighting records and incorporates them into the generation AI. This allows the development of an application that predicts the sightings of multiple wild animals. The prediction unit can also analyze the behavioral patterns of other wild animals and make sighting predictions. For example, it can analyze the movement patterns of deer and wild boar and predict the risk of their sighting. This can be applied to predicting the sightings of wild animals other than bears, making comprehensive wildlife management possible.

[0057] The prediction unit can add a function to link bear sighting prediction data with a local disaster prevention system and issue simultaneous alerts to residents. For example, the prediction unit links bear sighting prediction data to a local disaster prevention system and issues simultaneous alerts to residents when there is a high risk of a bear sighting. For example, this can be done using smartphone push notifications or disaster prevention radio. The prediction unit also links with the local disaster prevention system to provide evacuation guidance. For example, it can guide residents living in areas with a high risk of bear sightings along evacuation routes. By linking with the local disaster prevention system, this makes it possible to issue alerts to residents quickly.

[0058] The sound generating unit can be equipped with a function to generate specific light and vibration in addition to the sound and frequency that bears dislike, allowing for a combined approach to keeping bears away. For example, the sound generating unit can be equipped with a function to generate strong light in addition to the high-frequency sound that bears dislike. For example, a flashlight can be used to scare and keep bears away. The sound generating unit can also be equipped with a function to generate vibration. For example, a vibration device can be used to keep bears away. This makes it possible to more effectively keep bears away by combining sound, light, and vibration.

[0059] The sound generation unit monitors the bear's reaction in real time, and the generation AI can automatically adjust the optimal sound and frequency based on that reaction. For example, the sound generation unit is equipped with a sensor that monitors the bear's reaction in real time, and the generation AI analyzes the data and automatically adjusts the optimal sound and frequency. For example, it increases the sound intensity when a bear approaches. The sound generation unit also monitors changes in the bear's behavior and adjusts the type of sound based on that reaction. For example, if a bear is sensitive to a particular sound, it will generate that sound. This makes it possible to effectively keep bears away by monitoring the bear's reaction in real time and automatically adjusting the optimal sound and frequency.

[0060] The sound generation unit can use the emotion estimation function to analyze the user's sense of security when generating a sound that bears dislike, and adjust the type and intensity of the sound based on that emotion. For example, the sound generation unit uses the emotion estimation function to analyze the user's sense of security when generating a sound that bears dislike. For example, if the sense of security is strong, the sound intensity is reduced. The sound generation unit also adjusts the type of sound based on the user's sense of security. For example, it generates a sound that gives the user a sense of security. This allows for more effective bear repellency by adjusting the type and intensity of the sound based on the user's sense of security.

[0061] The sound generation unit's function of generating sounds and frequencies that bears dislike can be applied to other wild animals to develop a comprehensive wild animal repellent application. The sound generation unit can also apply data on sounds and frequencies that bears dislike to other wild animals to develop a comprehensive repellent application. For example, it can generate sounds that are effective against wolves and coyotes. The sound generation unit can also analyze the reactions of other wild animals and generate optimal sounds and frequencies based on that data. For example, it can generate specific sounds that wolves dislike. This makes it possible to repel wild animals other than bears, thereby enabling comprehensive wild animal repellent applications.

[0062] The sound generator can be equipped on agricultural drones with a device that generates sounds and frequencies that bears dislike, enabling bear repellent over a wide area. For example, the sound generator can be equipped on agricultural drones with a device that generates sounds and frequencies that bears dislike, enabling bear repellent over a wide area. For example, the drone can generate sounds while patrolling farmland. The sound generator can also set the drone's flight route and generate sounds in specific areas. For example, the drone can focus its patrols on areas where bears are likely to appear. This makes it possible to repell bears over a wide area using agricultural drones.

[0063] The audio guidance unit can provide audio guidance on measures to prevent bear encounters in addition to methods to escape from and repel bears. For example, the audio guidance unit provides audio guidance on how to manage food at a campsite in addition to methods to escape from and repel bears. For example, the audio guidance unit instructs not to leave food inside the tent. The audio guidance unit also provides guidance on how to select a campsite as a preventive measure to prevent bear encounters. For example, the audio guidance unit instructs not to leave food inside the tent. In this way, by providing audio guidance on preventive measures to prevent bear encounters, it is possible to reduce the risk of encountering a bear.

[0064] The voice guidance unit can acquire the user's location information in real time and provide voice guidance on the optimal escape route and evacuation site based on that location. The voice guidance unit, for example, uses the GPS function of the user's smartphone to acquire location information in real time and provides voice guidance on the optimal escape route based on that location. For example, it may provide guidance on the nearest safe place. The voice guidance unit also provides guidance on evacuation sites based on the user's location information. For example, it may provide guidance on locations away from areas where bears are likely to appear. This allows for quick response in emergencies by providing guidance on the optimal escape route and evacuation site based on the user's location information.

[0065] The voice guidance unit can use the emotion estimation function to analyze the user's emotion in an emergency and adjust the content and tone of the voice guidance based on the emotion. For example, the voice guidance unit can use the emotion estimation function to analyze the user's emotion in an emergency and adjust the tone of the voice guidance based on the emotion. For example, if the user is in a panic, the voice guidance unit can provide guidance in a calm tone. The voice guidance unit can also adjust the content of the voice guidance based on the user's emotion. For example, if the user is feeling anxious, the voice guidance unit can provide more detailed instructions. This allows for a more appropriate response by adjusting the content and tone of the voice guidance based on the user's emotion in an emergency.

[0066] The audio guidance unit can apply the audio guidance on how to escape from and kill bears to other wild animals, allowing the development of a comprehensive wildlife control application. The audio guidance unit can apply the audio guidance on how to escape from and kill bears to other wild animals such as snakes and scorpions, allowing the development of a comprehensive wildlife control application. For example, the audio guidance unit provides audio guidance on what to do when encountering a snake. The audio guidance unit can also provide advance guidance on measures to take against other wild animals. For example, the audio guidance unit can provide guidance on precautions to take in areas where scorpions are likely to appear. This makes it possible to implement comprehensive wildlife control measures that also cover wild animals other than bears.

[0067] The voice guidance unit can be equipped with a voice guidance function in a wearable device, allowing the user to use it easily. For example, the voice guidance unit can be equipped with a voice guidance function for how to escape from or defeat a bear in a smartwatch, allowing the user to use it easily. For example, guidance can be provided through the voice assistant of the smartwatch. The voice guidance unit can also be equipped with a voice guidance function in a fitness tracker. For example, guidance on how to escape from a bear can be provided using the voice function of the fitness tracker. This allows the user to easily use voice guidance by using the wearable device.

[0068] The voice guidance unit can use the emotion estimation function to analyze the emotion of the user when receiving voice guidance and optimize the content and method of guidance based on the results. The voice guidance unit, for example, uses the emotion estimation function to analyze the emotion of the user when receiving voice guidance and optimizes the content of guidance based on the results. For example, if the user is feeling anxious, more detailed instructions are provided. The voice guidance unit also adjusts the guidance method based on the user's emotion. For example, if the user is feeling relieved, simple instructions are provided. This makes it possible to optimize the content and method of guidance based on the user's emotion, thereby enabling more appropriate responses.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] In addition to bear sighting records, the prediction unit can collect local traffic data and incorporate it into the generation AI. For example, it can correlate past sighting records with the traffic conditions at the time and analyze them to predict the risk of bear sightings under specific traffic conditions. The prediction unit can also collect local event information and incorporate it into the generation AI. For example, it can predict the risk of bear sightings when a large-scale event is held in the area. By incorporating traffic data and event information, this makes it possible to make more accurate sighting predictions.

[0071] The sound generator can add a function to emit a specific scent in addition to sounds and frequencies that bears dislike, allowing for a combined approach to keep bears away. For example, a device can be installed that emits a specific scent that bears dislike. The sound generator can also be equipped with a function to adjust the scent intensity. For example, the scent intensity can be increased when a bear approaches. This combination of sound and scent makes it possible to more effectively keep bears away.

[0072] The audio guidance unit can provide audio guidance on psychological measures to take when encountering a bear, in addition to how to escape from or defeat the bear. For example, it can provide guidance on how to take deep breaths and stay calm. The audio guidance unit can also provide mental support to prevent the user from panicking. For example, it can provide guidance on simple exercises to help the user relax. In this way, audio guidance on psychological measures to take when encountering a bear can help the user respond calmly.

[0073] The prediction unit can use the emotion estimation function to analyze the user's sense of security and adjust the timing of the appearance prediction notification based on that emotion. For example, if the user feels a sense of security, the notification can be refrained from. The prediction unit can also adjust the content of the notification based on the user's sense of security. For example, if the user feels a strong sense of security, a brief notification can be sent. This makes it possible to provide more appropriate information by adjusting the notification timing and content based on the user's sense of security.

[0074] The sound generator, a device that emits sounds and frequencies that bears dislike, can be incorporated into a home security system. For example, it can be linked to a home security camera to automatically emit a sound when a bear approaches. The sound generator can also be linked to a home smart home device. For example, it can notify users of an approaching bear through a smart speaker. In this way, by linking it with a home security system, bear repellent measures can be implemented within the home.

[0075] The voice guidance unit can use the emotion estimation function to analyze the user's stress level and adjust the content and tone of the voice guidance based on the emotion. For example, if the user is feeling high stress, the voice guidance unit can provide guidance in a calmer tone. The voice guidance unit can also adjust the content of the voice guidance based on the user's stress level. For example, if the user is feeling high stress, more detailed instructions can be provided. This allows for more appropriate responses by adjusting the content and tone of the voice guidance based on the user's stress level.

[0076] The prediction unit can add a function to link bear sighting prediction data with local tourism businesses and guide tourists to safe tourist routes. For example, it can guide tourists to tourist routes that avoid areas with a high risk of bear sightings. The prediction unit can also provide safety information for tourist destinations. For example, it can guide tourists to avoid times when bears are more likely to appear. In this way, by linking with local tourism businesses, it becomes possible to provide tourists with safe tourist routes.

[0077] The sound generator can be a device that generates sounds and frequencies that bears dislike and is installed in a vehicle so that the driver can use it when they encounter a bear. For example, the sound can be generated in conjunction with the vehicle's horn. The sound generator can also be linked to a vehicle sensor. For example, it can automatically generate a sound when the vehicle approaches a bear. By installing the device in a vehicle, the driver can avoid a bear when they encounter one.

[0078] The voice guidance unit can use the emotion estimation function to analyze the user's emotions in an emergency and adjust the content and tone of the voice guidance based on the emotions. For example, if the user is in a panic, the voice guidance unit can provide guidance in a calm tone. The voice guidance unit can also adjust the content of the voice guidance based on the user's emotions. For example, if the user is feeling anxious, the voice guidance unit can provide more detailed instructions. This allows for more appropriate responses by adjusting the content and tone of the voice guidance based on the user's emotions in an emergency.

[0079] The sound generator, which generates sounds and frequencies that repel bears, can be installed in a portable outdoor device and used while camping or hiking. For example, the sound can be generated through a speaker built into the portable device. The sound generator can also be linked to the GPS function of the portable device. For example, a sound can be automatically generated when approaching an area where bears are likely to be found. This makes it possible to use the portable outdoor device to repel bears while camping or hiking.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The bear sighting record acquisition unit acquires bear sighting records, such as sighting information, footprint records, and camera trap data. The application also has a function to accept reports from local residents, allowing them to report bear sightings. Step 2: The prediction unit predicts bear sighting areas and times based on the records acquired by the sighting record acquisition unit. For example, the generation AI analyzes past sighting records to identify areas where bears are likely to appear during specific seasons or times of day. It can also incorporate weather and vegetation data to make more accurate predictions. Step 3: The sound generator generates sounds and frequencies that bears dislike. For example, it can keep bears away by generating a specific high-frequency sound. The sound generator is activated when the user launches the application and presses a specific button. For example, when a bear approaches, pressing the button will generate a sound. Step 4: The voice guidance section provides voice guidance on how to escape from or defeat the bear. For example, if a user encounters a bear and speaks to the application, the AI ​​will provide voice guidance on appropriate measures. For example, in response to the question, "I've encountered a bear. What should I do?", the AI ​​will respond with something like, "Stay calm and slowly back away from the bear."

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 bear sighting record acquisition unit that acquires bear sighting records; a prediction unit that predicts areas and times when bears are seen based on the records acquired by the sighting record acquisition unit; a sound generating unit that generates sounds and frequencies that bears dislike; and a voice guidance unit that provides voice guidance on how to escape from and how to exterminate a bear. A system characterized by:

2. The prediction unit In addition to the bear sighting records acquired by the sighting record acquisition unit, weather data and vegetation data are input into the generation AI to make more accurate sighting predictions.

2. The system of claim 1.

3. The prediction unit Apply bear sighting prediction data to other wildlife sighting predictions and develop comprehensive wildlife management applications.

2. The system of claim 1.

4. The sound generating unit In addition to the sounds and frequencies that bears dislike, the device also emits specific lights and vibrations, providing a comprehensive approach to keeping bears away.

2. The system of claim 1.

5. The voice guidance unit In addition to how to escape from and repel bears, audio guidance is provided on precautions to take to avoid encountering a bear.

2. The system of claim 1.

6. The prediction unit Analyzes the user's feelings of anxiety and fear, and adjusts the frequency and content of notifications of infestation predictions based on those feelings.

2. The system of claim 1.

7. The sound generating unit Analyzes the user's sense of security when making sounds that dislike bears, and adjusts the type and intensity of the sound based on that emotion.

2. The system of claim 1.

8. The voice guidance unit Analyzes the user's emotions during an emergency and adjusts the content and tone of the voice guidance based on those emotions.

2. The system of claim 1.

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