System

Abstract

An object of a system according to an embodiment is to quickly send an alert about the appearance and disappearance of bears and provide a manual for coping with the appearance and disappearance of bears.SOLUTION: A system according to an embodiment includes an activity log collection unit, an analysis unit, an alert transmission unit, and a manual generation unit. The activity log collection unit collects an activity log of a bear. The analysis unit analyzes the activity log collected by the activity log collection unit. The alert transmission unit transmits a dark circle appearance alert based on a result of the analysis by the analysis unit. The manual generation unit generates a response manual when encountering the bear.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 makes it difficult to respond quickly to bear sightings, and there is room for improvement to minimize damage.

[0005] The system of the embodiment aims to quickly issue alerts in the event of a bear sighting and provide a response manual. [Means for solving the problem]

[0006] The system according to the embodiment includes an activity log collection unit, an analysis unit, an alert transmission unit, and a manual generation unit. The activity log collection unit collects bear activity logs. The analysis unit analyzes the activity logs collected by the activity log collection unit. The alert transmission unit transmits a bear sighting alert based on the results of the analysis by the analysis unit. The manual generation unit generates a manual for responding to bear encounters. [Effects of the Invention]

[0007] The system according to the embodiment can quickly issue alerts in the event of a bear sighting and provide a response manual. [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 urban bear sighting alert system according to an embodiment of the present invention utilizes an urban bear activity log to send out bear sighting alerts and generate a manual for responding to bear encounters. This makes it possible to reduce bear-related harm to humans to zero.

[0029] An urban bear sighting alert system according to an embodiment includes an activity log collection unit, an analysis unit, an alert transmission unit, and a manual generation unit. The activity log collection unit collects urban bear activity logs. For example, it collects bear movement routes using GPS data. The activity log collection unit can also collect data on bear sighting times and locations. For example, it identifies bear sighting locations using sensors and collects that data. The analysis unit analyzes the collected activity logs. For example, a generation AI analyzes bear behavior patterns and identifies a tendency for bears to appear in specific locations at specific times. The analysis unit can also continuously learn and update bear behavior patterns. For example, each time a new activity log is collected, the generation AI analyzes the data and updates the behavior pattern. The alert transmission unit issues a bear sighting alert based on the analysis results. For example, if there is a high possibility that a bear will appear in a specific area, the generation AI issues an alert to residents of that area. This alert is sent via smartphone notifications, email, social media, etc. The manual generation unit generates a manual for what to do when encountering a bear. For example, the AI ​​generation unit generates a manual with content such as "If you encounter a bear, back away slowly and do not turn your back on the bear." The manual generation unit can also generate a manual that includes specific guidelines and precautions based on bear behavior patterns. This allows the urban bear sighting alert system according to the embodiment to reduce harm to people caused by bears to zero. For example, residents can avoid bear encounters by receiving bear sighting alerts. Also, by referring to the manual for what to do when encountering a bear, they can take appropriate action.

[0030] The analysis unit can collect surrounding environmental data in addition to the activity log and perform an integrated analysis of this data. For example, the analysis unit can collect surrounding environmental data (temperature, humidity, vegetation, etc.) in addition to the bear activity log, and the generation AI can perform an integrated analysis of this data. For example, it can analyze fluctuations in temperature and humidity in bear haunts and identify factors that affect bear behavior patterns. This allows for more accurate prediction of bear behavior patterns.

[0031] The analysis unit can also collect data on human activity that may affect bear behavior and incorporate this data into the analysis. For example, the analysis unit collects data on human activity that may affect bear behavior (events, traffic volume, etc.), and the generation AI incorporates this data into the analysis. For example, it can analyze the tendency for bear sightings to increase when local events are held and reflect this in sighting predictions. This allows for more accurate predictions of bear behavior patterns.

[0032] The activity log collection unit can use a drone or a sensor to collect data in real time to collect bear activity logs. The activity log collection unit, for example, uses a drone or a sensor to collect data in real time to collect bear activity logs. For example, a drone can be used to track the bear's movement route and collect data in real time. This allows the bear's activity log to be collected in real time, enabling a quick response.

[0033] When analyzing the bear activity log, the analysis unit can also analyze the activity data of other wild animals to evaluate their mutual influence. For example, when analyzing the bear activity log, the analysis unit simultaneously analyzes the activity data of other wild animals to evaluate their mutual influence. For example, the analysis unit analyzes the overlapping locations and time periods of appearance of bears and other wild animals to evaluate their mutual influence. This allows for the evaluation of the mutual influence between bears and other wild animals and more accurate appearance predictions.

[0034] The alert sending unit can customize the content of the alert based on the characteristics of the area when sending a bear sighting alert. For example, when sending a bear sighting alert, the alert sending unit customizes the content of the alert to suit the characteristics of the area. For example, the alert content can be set according to the topography of the area and the lifestyles of the residents. In this way, by sending an alert that suits the characteristics of the area, residents' response can be more effective.

[0035] In order to optimize the timing of alert transmission, the generation AI analyzes the effectiveness of past alerts and can automatically learn the optimal timing for sending alerts. In order to optimize the timing of alert transmission, for example, the generation AI analyzes the effectiveness of past alerts and learns the optimal timing for sending alerts. For example, it analyzes the time when past alerts were sent and the responses of residents to identify the optimal timing for sending alerts. This allows alerts to be sent at the optimal time, enabling residents to respond quickly and effectively.

[0036] The alert sending unit can use a voice assistant to send a voice alert when sending a bear sighting alert. For example, the alert sending unit can send a voice alert to residents via a smart speaker. In this way, by using a voice assistant, alerts can be sent effectively to visually impaired people and the elderly.

[0037] When issuing an alert, the alert sending unit can cooperate with the local disaster prevention system and simultaneously provide information for evacuation guidance and ensuring safety. For example, when issuing an alert, the alert sending unit can cooperate with the local disaster prevention system and simultaneously provide information for evacuation guidance and ensuring safety. For example, information on evacuation routes and evacuation locations can be notified along with the alert. In this way, by coordinating with the local disaster prevention system, the safety of residents can be ensured more effectively.

[0038] The manual generation unit can add specific scenario-specific response methods to the manual for when a bear is encountered, and the generation AI can generate a manual that corresponds to the scenario. The manual generation unit can add specific scenario-specific response methods to the manual for when a bear is encountered, and the generation AI can generate a manual that corresponds to the scenario. For example, it can describe how to respond when encountering a bear on a mountain trail and how to respond when encountering a bear in a residential area. By providing specific scenario-specific response methods, residents can take appropriate action.

[0039] The manual generation unit can customize the contents of the response manual to suit the characteristics of the area and the attributes of the residents. For example, the manual generation unit customizes the contents of the response manual for when an encounter with a bear is made to suit the characteristics of the area and the attributes of the residents. For example, it describes how to respond depending on the topography of the area and the age group of the residents. In this way, by providing a response manual that suits the characteristics of the area and the attributes of the residents, residents can take appropriate action.

[0040] The manual generation unit can provide the response manual in a video or animation format to make it visually easy to understand. For example, the manual generation unit can provide the response manual for when a bear is encountered in a video or animation format to make it visually easy to understand. For example, specific actions to take when encountering a bear are shown in a video. In this way, by providing a visually easy-to-understand manual, residents can take appropriate action.

[0041] The manual generation unit can make the contents of the response manual available for practical learning in workshops at local schools and community centers. For example, the manual generation unit makes it possible to practically learn the contents of the response manual for when a bear is encountered in workshops at local schools and community centers. For example, specific actions to take when encountering a bear are practiced in the workshops. This allows residents to take appropriate action through practical workshops.

[0042] The analysis unit can take into account seasonal variations and the influence of breeding seasons when learning bear behavior patterns. For example, the analysis unit can take into account seasonal variations and the influence of breeding seasons when learning bear behavior patterns. For example, the analysis unit can analyze how bear behavior changes during the breeding seasons of spring and autumn. By taking into account seasonal variations and the influence of breeding seasons, bear behavior patterns can be predicted more accurately.

[0043] The analysis unit can use a wide range of data sets, with the generation AI also referencing data on bears from different regions to learn bear behavior patterns. For example, the analysis unit can use a wide range of data sets, with the generation AI also referencing data on bears from different regions to learn bear behavior patterns. For example, the analysis unit can compare bear behavior patterns from different regions and analyze similarities and differences. In this way, by using a wide range of data sets, bear behavior patterns can be predicted more accurately.

[0044] The analysis unit can also apply the learning results of bear behavior patterns to conservation activities for other wild animals. For example, the analysis unit can apply the learning results of bear behavior patterns to conservation activities for other wild animals. For example, the analysis unit can analyze the behavior patterns of other wild animals that live in the same habitat as bears and use this information in conservation activities. In this way, applying the learning results of bear behavior patterns to conservation activities for other wild animals will enable widespread ecosystem protection.

[0045] The analysis unit can utilize the learning results for local tourism and agriculture and propose measures to promote coexistence with bears. For example, the analysis unit can utilize the learning results of bear behavior patterns for local tourism and agriculture and propose measures to promote coexistence with bears. For example, it can propose tourist routes and farmland layouts that avoid areas where bears are found. In this way, by proposing measures to promote coexistence with bears, it is possible to achieve both the development of local tourism and agriculture and the protection of ecosystems.

[0046] The educational material generation unit can reflect the latest research results on the ecology and behavior patterns of bears in the educational materials. For example, the educational material generation unit reflects the latest research results on the ecology and behavior patterns of bears in the educational materials. For example, the educational materials may provide a detailed explanation of the ecology and behavior patterns of bears based on the latest research data. In this way, by reflecting the latest research results, accurate and up-to-date information can be provided to residents.

[0047] The educational material generation unit can customize educational materials to suit the characteristics of the area and the attributes of the residents. For example, the educational material generation unit customizes educational materials to suit the characteristics of the area and the attributes of the residents. For example, educational materials are created according to the topography of the area and the age groups of the residents. This allows residents to deepen their understanding by providing educational materials that suit the characteristics of the area and the attributes of the residents.

[0048] The educational material generation unit can provide educational materials as an interactive online course to enable residents to learn independently. The educational material generation unit, for example, provides educational materials as an interactive online course to enable residents to learn independently. For example, the educational material generation unit creates an online course that includes quizzes and simulations. This improves the effectiveness of education by providing an interactive online course that enables residents to learn independently.

[0049] The educational material generation unit can make the educational materials available for practical learning in workshops at local schools or community centers. For example, the educational material generation unit makes the educational materials available for practical learning in workshops at local schools or community centers. For example, specific actions to take when encountering a bear are practiced in the workshop. This allows residents to take appropriate action through the practical workshop.

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

[0051] When analyzing bear behavior patterns, the analysis unit can collect data on the bear's health and incorporate this data into the analysis. For example, health data such as the bear's weight, nutritional status, and whether or not it is sick is collected, and the generation AI incorporates this data into the analysis. This allows for the evaluation of the impact of the bear's health on behavior patterns, enabling more accurate predictions of bear appearances.

[0052] The alert sending unit can use a voice assistant to send out a bear sighting alert. For example, it can send an audio alert to residents via a smart speaker. This makes it possible to effectively send alerts to the visually impaired and elderly.

[0053] The manual generation unit can provide response manuals in video or animation format to make them easier to understand visually. For example, a video could be used to show specific actions to take when encountering a bear. This provides a visually easy-to-understand manual, allowing residents to take appropriate action.

[0054] The analysis unit can apply the results of learning about bear behavior patterns to conservation efforts for other wild animals. For example, it can analyze the behavior patterns of other wild animals living in the same habitat as bears and use this information to aid in conservation efforts. This makes it possible to apply the results of learning about bear behavior patterns to conservation efforts for other wild animals, thereby enabling widespread ecosystem protection.

[0055] The educational material generator can provide educational materials as interactive online courses to enable residents to learn independently. For example, it can create online courses that include quizzes and simulations. This improves the effectiveness of education by providing interactive online courses that allow residents to learn independently.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The activity log collection unit collects activity logs of urban bears. For example, GPS data is used to collect bear movement routes. Data on bear appearance times and locations can also be collected. For example, a sensor is used to identify bear appearance locations and collect that data. Step 2: The analysis unit analyzes the collected activity logs. For example, the generation AI analyzes bear behavior patterns and identifies their tendency to appear in specific locations at specific times. The analysis unit can also continuously learn and update bear behavior patterns. For example, each time a new activity log is collected, the generation AI analyzes the data and updates the behavior patterns. Step 3: The alert sending unit sends out bear sighting alerts based on the analysis results. For example, if there is a high possibility that a bear will be seen in a specific area, the AI ​​generator will send out an alert to residents of that area. This alert will be sent via smartphone notifications, email, social media, etc. Step 4: The manual generation unit generates a manual for what to do when encountering a bear. For example, the AI ​​could generate a manual such as, "If you encounter a bear, back away slowly and do not turn your back on the bear." It can also generate a manual that includes specific guidelines and precautions based on bear behavior patterns.

[0058] (Example 2) The urban bear sighting alert system according to an embodiment of the present invention utilizes an urban bear activity log to send out bear sighting alerts and generate a manual for responding to bear encounters. This makes it possible to reduce bear-related harm to humans to zero.

[0059] An urban bear sighting alert system according to an embodiment includes an activity log collection unit, an analysis unit, an alert transmission unit, and a manual generation unit. The activity log collection unit collects urban bear activity logs. For example, it collects bear movement routes using GPS data. The activity log collection unit can also collect data on bear sighting times and locations. For example, it identifies bear sighting locations using sensors and collects that data. The analysis unit analyzes the collected activity logs. For example, a generation AI analyzes bear behavior patterns and identifies a tendency for bears to appear in specific locations at specific times. The analysis unit can also continuously learn and update bear behavior patterns. For example, each time a new activity log is collected, the generation AI analyzes the data and updates the behavior pattern. The alert transmission unit issues a bear sighting alert based on the analysis results. For example, if there is a high possibility that a bear will appear in a specific area, the generation AI issues an alert to residents of that area. This alert is sent via smartphone notifications, email, social media, etc. The manual generation unit generates a manual for what to do when encountering a bear. For example, the AI ​​generation unit generates a manual with content such as "If you encounter a bear, back away slowly and do not turn your back on the bear." The manual generation unit can also generate a manual that includes specific guidelines and precautions based on bear behavior patterns. This allows the urban bear sighting alert system according to the embodiment to reduce harm to people caused by bears to zero. For example, residents can avoid bear encounters by receiving bear sighting alerts. Also, by referring to the manual for what to do when encountering a bear, they can take appropriate action.

[0060] The analysis unit can collect surrounding environmental data in addition to the activity log and perform an integrated analysis of this data. For example, the analysis unit can collect surrounding environmental data (temperature, humidity, vegetation, etc.) in addition to the bear activity log, and the generation AI can perform an integrated analysis of this data. For example, it can analyze fluctuations in temperature and humidity in bear haunts and identify factors that affect bear behavior patterns. This allows for more accurate prediction of bear behavior patterns.

[0061] The analysis unit can also collect data on human activity that may affect bear behavior and incorporate this data into the analysis. For example, the analysis unit collects data on human activity that may affect bear behavior (events, traffic volume, etc.), and the generation AI incorporates this data into the analysis. For example, it can analyze the tendency for bear sightings to increase when local events are held and reflect this in sighting predictions. This allows for more accurate predictions of bear behavior patterns.

[0062] The analysis unit can use the emotion estimation function to estimate the bear's stress level from the activity log and identify factors that increase stress. For example, the analysis unit can use the emotion estimation function to estimate the bear's stress level from the bear's behavior log and identify factors that increase stress. For example, the analysis unit can analyze the impact that specific environmental conditions and human activity have on the bear's stress level. This makes it possible to understand the bear's stress level and take appropriate measures.

[0063] The activity log collection unit can use a drone or a sensor to collect data in real time to collect bear activity logs. The activity log collection unit, for example, uses a drone or a sensor to collect data in real time to collect bear activity logs. For example, a drone can be used to track the bear's movement route and collect data in real time. This allows the bear's activity log to be collected in real time, enabling a quick response.

[0064] When analyzing the bear activity log, the analysis unit can also analyze the activity data of other wild animals to evaluate their mutual influence. For example, when analyzing the bear activity log, the analysis unit simultaneously analyzes the activity data of other wild animals to evaluate their mutual influence. For example, the analysis unit analyzes the overlapping locations and time periods of appearance of bears and other wild animals to evaluate their mutual influence. This allows for the evaluation of the mutual influence between bears and other wild animals and more accurate appearance predictions.

[0065] The analysis unit can use the emotion estimation function to collect emotional data of local residents when analyzing the activity log and analyze their emotional reactions to bear sightings. For example, the analysis unit can use the emotion estimation function to collect emotional data of local residents when analyzing the bear activity log and analyze their emotional reactions to bear sightings. For example, the analysis unit can analyze residents' social media posts and survey results to evaluate their emotional reactions to bear sightings. This makes it possible to understand residents' emotional reactions to bear sightings and take appropriate measures.

[0066] The alert sending unit can customize the content of the alert based on the characteristics of the area when sending a bear sighting alert. For example, when sending a bear sighting alert, the alert sending unit customizes the content of the alert to suit the characteristics of the area. For example, the alert content can be set according to the topography of the area and the lifestyles of the residents. In this way, by sending an alert that suits the characteristics of the area, residents' response can be more effective.

[0067] In order to optimize the timing of alert transmission, the generation AI analyzes the effectiveness of past alerts and can automatically learn the optimal timing for sending alerts. In order to optimize the timing of alert transmission, for example, the generation AI analyzes the effectiveness of past alerts and learns the optimal timing for sending alerts. For example, it analyzes the time when past alerts were sent and the responses of residents to identify the optimal timing for sending alerts. This allows alerts to be sent at the optimal time, enabling residents to respond quickly and effectively.

[0068] The alert sending unit can use the emotion estimation function to analyze the emotional reactions of residents who receive the alert and improve the content or format of the alert. For example, the alert sending unit uses the emotion estimation function to analyze the emotional reactions of residents who receive the alert and improve the content or format of the alert. For example, the alert content or format is adjusted based on the resident's emotion score. In this way, the effectiveness of the alert is improved by improving the content or format of the alert based on the resident's emotional reactions.

[0069] The alert sending unit can use a voice assistant to send a voice alert when sending a bear sighting alert. For example, the alert sending unit can send a voice alert to residents via a smart speaker. In this way, by using a voice assistant, alerts can be sent effectively to visually impaired people and the elderly.

[0070] When issuing an alert, the alert sending unit can cooperate with the local disaster prevention system and simultaneously provide information for evacuation guidance and ensuring safety. For example, when issuing an alert, the alert sending unit can cooperate with the local disaster prevention system and simultaneously provide information for evacuation guidance and ensuring safety. For example, information on evacuation routes and evacuation locations can be notified along with the alert. In this way, by coordinating with the local disaster prevention system, the safety of residents can be ensured more effectively.

[0071] The alert sending unit can use the emotion estimation function to collect emotion data of residents after an alert is sent and evaluate the effectiveness of the alert. The alert sending unit, for example, uses the emotion estimation function to collect emotion data of residents after an alert is sent and evaluates the effectiveness of the alert. For example, the alert effectiveness is evaluated based on the emotion score of residents and areas for improvement are identified. This allows the effectiveness of the alert to be evaluated and reflected in the next alert, thereby improving the effectiveness of the alert.

[0072] The manual generation unit can add specific scenario-specific response methods to the manual for when a bear is encountered, and the generation AI can generate a manual that corresponds to the scenario. The manual generation unit can add specific scenario-specific response methods to the manual for when a bear is encountered, and the generation AI can generate a manual that corresponds to the scenario. For example, it can describe how to respond when encountering a bear on a mountain trail and how to respond when encountering a bear in a residential area. By providing specific scenario-specific response methods, residents can take appropriate action.

[0073] The manual generation unit can customize the contents of the response manual to suit the characteristics of the area and the attributes of the residents. For example, the manual generation unit customizes the contents of the response manual for when an encounter with a bear is made to suit the characteristics of the area and the attributes of the residents. For example, it describes how to respond depending on the topography of the area and the age group of the residents. In this way, by providing a response manual that suits the characteristics of the area and the attributes of the residents, residents can take appropriate action.

[0074] The manual generation unit can use the emotion estimation function to analyze the emotional reactions of residents who read the corresponding manual and improve the expression and format to make it easier to understand. The manual generation unit can, for example, use the emotion estimation function to analyze the emotional reactions of residents who read the corresponding manual and improve the expression and format to make it easier to understand. For example, the manual content and format can be adjusted based on the residents' emotion scores. In this way, by improving the content and format of the manual based on the residents' emotional reactions, it is possible to provide a manual that is easy to understand.

[0075] The manual generation unit can provide the response manual in a video or animation format to make it visually easy to understand. For example, the manual generation unit can provide the response manual for when a bear is encountered in a video or animation format to make it visually easy to understand. For example, specific actions to take when encountering a bear are shown in a video. In this way, by providing a visually easy-to-understand manual, residents can take appropriate action.

[0076] The manual generation unit can make the contents of the response manual available for practical learning in workshops at local schools and community centers. For example, the manual generation unit makes it possible to practically learn the contents of the response manual for when a bear is encountered in workshops at local schools and community centers. For example, specific actions to take when encountering a bear are practiced in the workshops. This allows residents to take appropriate action through practical workshops.

[0077] The manual generation unit can use the emotion estimation function to collect residents' emotional reactions to the contents of the response manual and evaluate the effectiveness of the manual. The manual generation unit, for example, uses the emotion estimation function to collect residents' emotional reactions to the contents of the response manual and evaluates the effectiveness of the manual. For example, the manual's effectiveness is evaluated based on the residents' emotion scores and areas for improvement are identified. This allows the effectiveness of the manual to be evaluated and reflected in the creation of the next manual, thereby improving the effectiveness of the manual.

[0078] The analysis unit can take into account seasonal variations and the influence of breeding seasons when learning bear behavior patterns. For example, the analysis unit can take into account seasonal variations and the influence of breeding seasons when learning bear behavior patterns. For example, the analysis unit can analyze how bear behavior changes during the breeding seasons of spring and autumn. By taking into account seasonal variations and the influence of breeding seasons, bear behavior patterns can be predicted more accurately.

[0079] The analysis unit can use a wide range of data sets, with the generation AI also referencing data on bears from different regions to learn bear behavior patterns. For example, the analysis unit can use a wide range of data sets, with the generation AI also referencing data on bears from different regions to learn bear behavior patterns. For example, the analysis unit can compare bear behavior patterns from different regions and analyze similarities and differences. In this way, by using a wide range of data sets, bear behavior patterns can be predicted more accurately.

[0080] The analysis unit can use the emotion estimation function to analyze the emotional reactions of residents to changes in the bear's behavior pattern, thereby improving prediction accuracy. The analysis unit can, for example, use the emotion estimation function to analyze the emotional reactions of residents to changes in the bear's behavior pattern, thereby improving prediction accuracy. For example, the analysis unit predicts changes in the bear's behavior pattern based on the residents' emotion scores. This can improve prediction accuracy of the bear's behavior pattern based on the residents' emotional reactions.

[0081] The analysis unit can also apply the learning results of bear behavior patterns to conservation activities for other wild animals. For example, the analysis unit can apply the learning results of bear behavior patterns to conservation activities for other wild animals. For example, the analysis unit can analyze the behavior patterns of other wild animals that live in the same habitat as bears and use this information in conservation activities. In this way, applying the learning results of bear behavior patterns to conservation activities for other wild animals will enable widespread ecosystem protection.

[0082] The analysis unit can utilize the learning results for local tourism and agriculture and propose measures to promote coexistence with bears. For example, the analysis unit can utilize the learning results of bear behavior patterns for local tourism and agriculture and propose measures to promote coexistence with bears. For example, it can propose tourist routes and farmland layouts that avoid areas where bears are found. In this way, by proposing measures to promote coexistence with bears, it is possible to achieve both the development of local tourism and agriculture and the protection of ecosystems.

[0083] The analysis unit can use the emotion estimation function to collect residents' emotional responses to the learning results of bear behavior patterns and improve coexistence measures. For example, the analysis unit can use the emotion estimation function to collect residents' emotional responses to the learning results of bear behavior patterns and improve coexistence measures. For example, the effectiveness of coexistence measures can be evaluated based on residents' emotion scores and areas for improvement identified. In this way, improving coexistence measures based on residents' emotional responses makes it easier to gain residents' understanding and cooperation.

[0084] The educational material generation unit can reflect the latest research results on the ecology and behavior patterns of bears in the educational materials. For example, the educational material generation unit reflects the latest research results on the ecology and behavior patterns of bears in the educational materials. For example, the educational materials may provide a detailed explanation of the ecology and behavior patterns of bears based on the latest research data. In this way, by reflecting the latest research results, accurate and up-to-date information can be provided to residents.

[0085] The educational material generation unit can customize educational materials to suit the characteristics of the area and the attributes of the residents. For example, the educational material generation unit customizes educational materials to suit the characteristics of the area and the attributes of the residents. For example, educational materials are created according to the topography of the area and the age groups of the residents. This allows residents to deepen their understanding by providing educational materials that suit the characteristics of the area and the attributes of the residents.

[0086] The educational material generation unit can use the emotion estimation function to analyze the emotional reactions of residents who read the educational materials and improve the expressions and formats to make them easier to understand. The educational material generation unit, for example, uses the emotion estimation function to analyze the emotional reactions of residents who read the educational materials and improve the expressions and formats to make them easier to understand. For example, the content and format of the educational materials can be adjusted based on the residents' emotion scores. In this way, by improving the content and format of the educational materials based on the residents' emotional reactions, it is possible to provide educational materials that are easier to understand.

[0087] The educational material generation unit can provide educational materials as an interactive online course to enable residents to learn independently. The educational material generation unit, for example, provides educational materials as an interactive online course to enable residents to learn independently. For example, the educational material generation unit creates an online course that includes quizzes and simulations. This improves the effectiveness of education by providing an interactive online course that enables residents to learn independently.

[0088] The educational material generation unit can make the educational materials available for practical learning in workshops at local schools or community centers. For example, the educational material generation unit makes the educational materials available for practical learning in workshops at local schools or community centers. For example, specific actions to take when encountering a bear are practiced in the workshop. This allows residents to take appropriate action through the practical workshop.

[0089] The educational material generation unit can use the emotion estimation function to collect residents' emotional responses to the educational materials and evaluate the educational effectiveness. The educational material generation unit, for example, uses the emotion estimation function to collect residents' emotional responses to the educational materials and evaluate the educational effectiveness. For example, the educational material generation unit evaluates the effectiveness of the educational materials based on the residents' emotion scores and identifies areas for improvement. This allows the educational effectiveness to be evaluated and reflected in the creation of the next educational materials, thereby improving the educational effectiveness.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When analyzing bear behavior patterns, the analysis unit can collect data on the bear's health and incorporate this data into the analysis. For example, health data such as the bear's weight, nutritional status, and whether or not it is sick is collected, and the generation AI incorporates this data into the analysis. This allows for the evaluation of the impact of the bear's health on behavior patterns, enabling more accurate predictions of bear appearances.

[0092] The analysis unit can use the emotion estimation function to analyze the emotional reactions of residents to changes in bear behavior patterns, thereby improving prediction accuracy. For example, it can predict changes in bear behavior patterns based on the emotion scores of residents. This can improve prediction accuracy of bear behavior patterns based on the emotional reactions of residents.

[0093] The alert sending unit can use a voice assistant to send out a bear sighting alert. For example, it can send an audio alert to residents via a smart speaker. This makes it possible to effectively send alerts to the visually impaired and elderly.

[0094] The alert sending unit can use the emotion estimation function to analyze the emotional reactions of residents who receive the alert and improve the content and format of the alert. For example, the alert content and format can be adjusted based on the resident's emotion score. This improves the effectiveness of alerts by improving the content and format of the alert based on the resident's emotional reaction.

[0095] The manual generation unit can provide response manuals in video or animation format to make them easier to understand visually. For example, a video could be used to show specific actions to take when encountering a bear. This provides a visually easy-to-understand manual, allowing residents to take appropriate action.

[0096] The manual generation unit uses the emotion estimation function to analyze the emotional reactions of residents who read the corresponding manual and can improve the expression and format to make it easier to understand. For example, the content and format of the manual can be adjusted based on the residents' emotional scores. In this way, by improving the content and format of the manual based on the residents' emotional reactions, it is possible to provide an easy-to-understand manual.

[0097] The analysis unit can apply the results of learning about bear behavior patterns to conservation efforts for other wild animals. For example, it can analyze the behavior patterns of other wild animals living in the same habitat as bears and use this information to aid in conservation efforts. This makes it possible to apply the results of learning about bear behavior patterns to conservation efforts for other wild animals, thereby enabling widespread ecosystem protection.

[0098] The analysis unit uses the emotion estimation function to collect residents' emotional responses to the learning results of bear behavior patterns, allowing it to improve coexistence measures. For example, the effectiveness of coexistence measures can be evaluated based on residents' emotional scores, and areas for improvement identified. This makes it easier to gain residents' understanding and cooperation by improving coexistence measures based on residents' emotional responses.

[0099] The educational material generator can provide educational materials as interactive online courses to enable residents to learn independently. For example, it can create online courses that include quizzes and simulations. This improves the effectiveness of education by providing interactive online courses that allow residents to learn independently.

[0100] The educational material generation unit can use the emotion estimation function to collect residents' emotional responses to educational materials and evaluate the effectiveness of the education. For example, the effectiveness of the educational materials can be evaluated based on the residents' emotion scores and areas for improvement identified. This allows the educational effectiveness to be evaluated and reflected in the creation of the next educational materials, thereby improving the educational effectiveness.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The activity log collection unit collects activity logs of urban bears. For example, GPS data is used to collect bear movement routes. Data on bear appearance times and locations can also be collected. For example, a sensor is used to identify bear appearance locations and collect that data. Step 2: The analysis unit analyzes the collected activity logs. For example, the generation AI analyzes bear behavior patterns and identifies their tendency to appear in specific locations at specific times. The analysis unit can also continuously learn and update bear behavior patterns. For example, each time a new activity log is collected, the generation AI analyzes the data and updates the behavior patterns. Step 3: The alert sending unit sends out bear sighting alerts based on the analysis results. For example, if there is a high possibility that a bear will be seen in a specific area, the AI ​​generator will send out an alert to residents of that area. This alert will be sent via smartphone notifications, email, social media, etc. Step 4: The manual generation unit generates a manual for what to do when encountering a bear. For example, the AI ​​could generate a manual such as, "If you encounter a bear, back away slowly and do not turn your back on the bear." It can also generate a manual that includes specific guidelines and precautions based on bear behavior patterns.

[0103] 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.

[0104] 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.

[0105] 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.

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

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

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

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

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

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

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. an activity log collection unit that collects bear activity logs; an analysis unit that analyzes the activity log collected by the activity log collection unit; an alert sending unit that sends a bear sighting alert based on the results of the analysis by the analysis unit; A manual generation unit that generates a manual for responding when encountering a bear. A system characterized by:

2. The activity log collection unit Use drones or sensors to collect real-time data to log bear activity.

2. The system of claim 1.

3. The alert sending unit When issuing the bear sighting alert, the content of the alert is customized based on the characteristics of the area.

2. The system of claim 1.

4. The manual generation unit Specific scenario-specific response methods will be added to the manual for dealing with bear encounters, and the AI ​​will generate a manual that corresponds to the scenario.

2. The system of claim 1.

5. The analysis unit Consider seasonal variations and the influence of breeding seasons when studying bear behavior patterns 2. The system of claim 1.

6. The analysis unit Estimate the bear's stress level from the activity log and clearly identify factors that increase stress.

2. The system of claim 1.

7. The alert sending unit Analyze the emotional reactions of residents who receive alerts and improve the content or format of the alerts 2. The system of claim 1.

8. The manual generation unit Analyze the emotional reactions of residents who read the manual and improve its expressions and format to make it easier to understand.

2. The system of claim 1.

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