System

Abstract

An object of a system according to an embodiment is to predict the risk of appearance of bears and promptly notify residents of the risk.SOLUTION: A system includes an action analysis part, a prediction part, and a notification part. The behavior analysis unit analyzes a behavior pattern of the bear. The prediction unit predicts a risk of appearance of a bear on the basis of the behavior pattern of the bear analyzed by the behavior analysis unit. The notification unit notifies the resident of the risk of appearance of the dark circle predicted by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately predict the risk of bear appearances and quickly notify residents.

[0005] The system according to the embodiment aims to predict the risk of bear appearances and quickly notify residents. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior analysis unit, a prediction unit, and a notification unit. The behavior analysis unit analyzes bear behavior patterns. The prediction unit predicts the risk of bear appearance based on the bear behavior patterns analyzed by the behavior analysis unit. The notification unit notifies residents of the risk of bear appearance predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the risk of bear appearances and quickly notify residents. [Brief explanation of the drawings]

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

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

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

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

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

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

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

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

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

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

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

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0028] (Example 1) The Coexistence Environment System according to an embodiment of the present invention is a system that reduces bear-related damage to zero and creates an environment where bears and humans can coexist harmoniously. As a result, the Coexistence Environment System predicts bear behavior and notifies residents, allowing them to avoid encounters with bears.

[0029] The coexistence environment system according to the embodiment includes a behavior analysis unit, a prediction unit, and a notification unit. The behavior analysis unit analyzes bear behavior patterns. For example, the behavior analysis unit collects bear movement data and an AI analyzes the data. The behavior analysis unit can also collect environmental data on bear habitats and an AI analyzes the data. The behavior analysis unit can analyze bear feeding behavior and identify behavior patterns based on the data. The prediction unit predicts the risk of bear appearance based on the bear behavior patterns analyzed by the behavior analysis unit. For example, the prediction unit identifies areas and time periods with a high risk of bear appearance. The prediction unit can also predict future risk of bear appearance based on past appearance data. The prediction unit can also predict risk of appearance by taking environmental factors into account. The notification unit notifies residents of the risk of bear appearance predicted by the prediction unit. For example, the notification unit notifies residents via a smartphone app. The notification unit can also notify residents via a local disaster prevention radio. The notification unit can also notify residents via email. As a result, the coexistence environment system according to the embodiment can predict bear behavior and notify residents, thereby enabling them to avoid encounters with bears.

[0030] The behavior analysis unit collects biometric data such as bear body temperature and heart rate in real time and can predict behavior based on the biometric data. For example, to analyze bear behavior patterns, the behavior analysis unit uses AI to monitor the bear's body temperature and heart rate in real time and predict behavior based on that data. For example, it identifies the time of day when the bear is active. The behavior analysis unit also collects biometric data of the bear and analyzes it in combination with environmental data. For example, it predicts how the bear will behave when the temperature and humidity are high. The behavior analysis unit also collects biometric data of the bear and compares it with past data to predict behavior. For example, it predicts how the bear will behave during specific seasons and times of day. This allows the system to collect biometric data of the bear in real time and reflect it in behavior predictions, enabling more accurate predictions.

[0031] The behavior analysis unit can simulate future behavior by combining a bear's past behavioral history with environmental changes. For example, when analyzing a bear's behavioral patterns, the behavior analysis unit uses AI to store the bear's past behavioral history in a database and simulate future behavior by combining it with environmental changes. For example, it predicts the bear's movement route based on past data. The behavior analysis unit also combines and analyzes the bear's past behavioral history with weather data to simulate future behavior. For example, it predicts how a bear will behave when the weather changes. The behavior analysis unit also combines and analyzes the bear's past behavioral history with food supply conditions to simulate future behavior. For example, it predicts the possibility of a bear moving to an area with abundant food. This allows for more accurate predictions by combining the bear's past behavioral history with environmental changes to simulate future behavior.

[0032] The notification unit can collect location information of residents in real time and individually suggest the optimal evacuation route. For example, the notification unit adds a function to collect location information of residents in real time to a bear appearance risk notification system and individually suggest the optimal evacuation route. For example, it guides residents to the safest evacuation site based on their location. The notification unit also adds a function to collect location information of residents to the bear appearance risk notification system and analyzes the congestion status of evacuation routes in real time. For example, it suggests alternative routes to avoid congestion. The notification unit also adds a function to collect location information of residents to the bear appearance risk notification system and evaluates the safety of evacuation routes. For example, it prioritizes suggesting routes with a low risk of bear appearances. In this way, by collecting location information of residents in real time and individually suggesting the optimal evacuation route, safer evacuation is possible.

[0033] The notification unit can perform long-term risk predictions based on past appearance data and notify residents in advance. The notification unit, for example, adds a function to the bear appearance risk notification system that analyzes past appearance data and performs long-term risk predictions. For example, the notification unit predicts the risk of bear appearances during specific seasons or times of day. The notification unit also adds a function to the bear appearance risk notification system that performs risk predictions based on past appearance data and notifies residents in advance. For example, on days when the risk of bear appearances is high, a warning is issued in advance. The notification unit also adds a function to the bear appearance risk notification system that analyzes past appearance data and performs risk predictions. For example, the notification unit predicts periods when the risk of bear appearances will increase in specific areas and notifies residents. In this way, by performing long-term risk predictions based on past appearance data and notifying residents in advance, it becomes possible to take measures earlier.

[0034] The behavior analysis unit can collect environmental data on bear habitats in real time and automatically adjust protective measures based on the environmental data. For example, in the protection and management of bear habitats, the AI ​​collects environmental data such as temperature, humidity, and precipitation in real time and automatically adjusts protective measures in response to environmental changes. For example, when temperatures are high, measures are taken to suppress bear activity. In the protection and management of bear habitats, the AI ​​collects vegetation data in real time and automatically adjusts protective measures in response to environmental changes. For example, when food supplies are insufficient, vegetation management is carried out. In the protection and management of bear habitats, the AI ​​collects water source data in real time and automatically adjusts protective measures in response to environmental changes. For example, when water sources are depleted, measures are taken to increase the water supply. This allows for the proper protection of bear habitats by collecting environmental data on bear habitats in real time and automatically adjusting protective measures in response to environmental changes.

[0035] The behavior analysis unit can monitor the food supply situation for bears and propose vegetation management based on the food supply situation. For example, in the protection and management of bear habitats, the behavior analysis unit uses AI to monitor the food supply situation for bears and propose vegetation management to optimize the food supply. For example, if there is a shortage of food for bears, the AI ​​increases the number of specific plants. In addition, in the protection and management of bear habitats, the AI ​​monitors the food supply situation for bears and proposes vegetation management to optimize the food supply. For example, if there is an excess of food for bears, the AI ​​adjusts the vegetation. In addition, in the protection and management of bear habitats, the AI ​​monitors the food supply situation for bears and proposes vegetation management to optimize the food supply. For example, if there is a shortage of food for bears in a particular season, seasonal vegetation management is performed. In this way, the AI ​​can monitor the food supply situation for bears and propose vegetation management to optimize the food supply, thereby properly protecting bear habitats.

[0036] The behavior analysis unit can apply the bear behavior pattern analysis to other wild animals to predict the behavior of animal species. For example, the behavior analysis unit applies the bear behavior pattern analysis technology to other wild animals, such as wolves and deer, to predict the behavior of each. This allows for comprehensive management of the behavior of multiple animal species. The behavior analysis unit also applies the bear behavior pattern analysis technology to birds and small mammals to predict the behavior of different animal species. For example, it predicts the migration patterns of migratory birds. The behavior analysis unit also applies the bear behavior pattern analysis technology to marine life to predict the behavior of different animal species. For example, it predicts the migration patterns of dolphins and whales. This allows for comprehensive management of the behavior of multiple animal species by applying the bear behavior pattern analysis technology to other wild animals.

[0037] The behavior analysis unit can apply bear behavior pattern analysis to animals in urban areas to promote coexistence with animals. For example, the behavior analysis unit applies bear behavior pattern analysis technology to stray cats and dogs in urban areas to predict the behavior of each. This promotes coexistence with animals in urban areas. The behavior analysis unit also applies bear behavior pattern analysis technology to birds and small mammals in urban areas to predict their behavior. For example, it predicts the behavior patterns of pigeons and squirrels. The behavior analysis unit also applies bear behavior pattern analysis technology to vermin in urban areas (e.g., rats and cockroaches) to predict their behavior. This promotes effective measures against vermin. By applying bear behavior pattern analysis technology to animals in urban areas, it is possible to promote coexistence with animals in urban areas.

[0038] The behavior analysis unit collects bear biometric data in real time and can predict behavior based on the biometric data. For example, in bear behavior monitoring technology, the behavior analysis unit uses AI to monitor bear body temperature and heart rate in real time and predict behavior based on that data. For example, it identifies the time of day when bears are active. The behavior analysis unit also collects bear biometric data and analyzes it in combination with environmental data. For example, it predicts how bears will behave when the temperature and humidity are high. The behavior analysis unit also collects bear biometric data and compares it with past data to predict behavior. For example, it predicts how bears will behave in specific seasons and times of day. This allows bear biometric data to be collected in real time and reflected in behavior predictions, enabling more accurate predictions.

[0039] The behavior analysis unit can simulate future behavior based on data on the bear's past behavior. For example, in bear behavior monitoring technology, the behavior analysis unit uses AI to store data on the bear's past behavior in a database and combine it with environmental changes to simulate future behavior. For example, it predicts the bear's movement route based on past data. The behavior analysis unit also combines and analyzes data on the bear's past behavior with weather data to simulate future behavior. For example, it predicts how the bear will behave when the weather changes. The behavior analysis unit also combines and analyzes data on the bear's past behavior with the food supply situation to simulate future behavior. For example, it predicts the possibility of the bear moving to an area with abundant food. This allows for more accurate predictions by simulating future behavior based on data on the bear's past behavior.

[0040] The behavior analysis unit can apply the bear behavior monitoring technology to other wild animals to predict the behavior of animal species. For example, the behavior analysis unit applies the bear behavior monitoring technology to monitoring the behavior of other wild animals, such as wolves and deer, and predicts the behavior of each. This allows for comprehensive management of the behavior of multiple animal species. The behavior analysis unit also applies the bear behavior monitoring technology to monitoring the behavior of birds and small mammals, and predicts the behavior of different animal species. For example, it predicts the migration patterns of migratory birds. The behavior analysis unit also applies the bear behavior monitoring technology to monitoring the behavior of marine life, and predicts the behavior of different animal species. For example, it predicts the migration patterns of dolphins and whales. This allows for comprehensive management of the behavior of multiple animal species by applying the bear behavior monitoring technology to other wild animals.

[0041] The behavior analysis unit can apply bear behavior monitoring technology to animals in urban areas to promote coexistence with animals. For example, the behavior analysis unit applies bear behavior monitoring technology to stray cats and dogs in urban areas to predict the behavior of each. This promotes coexistence with animals in urban areas. The behavior analysis unit also applies bear behavior monitoring technology to birds and small mammals in urban areas to predict their behavior. For example, it predicts the behavior patterns of pigeons and squirrels. The behavior analysis unit also applies bear behavior monitoring technology to vermin in urban areas (for example, rats and cockroaches) to predict their behavior. This promotes effective measures against vermin. By applying bear behavior monitoring technology to animals in urban areas, it is possible to promote coexistence with animals in urban areas.

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

[0043] The behavioral analysis unit not only analyzes the behavioral patterns of the bears, but can also monitor their health. For example, the behavioral analysis unit periodically measures the bears' weight and body fat percentage to assess their health. The behavioral analysis unit can also collect blood samples from the bears to detect their nutritional status and signs of illness. Furthermore, the behavioral analysis unit can monitor the bears' exercise levels and rest times, and evaluate their stress levels and fatigue. This allows for comprehensive management of the bears' health and, if necessary, take appropriate protective measures.

[0044] The behavior analysis unit not only collects bear biometric data in real time, but can also perform 3D mapping of bear movement routes. For example, the behavior analysis unit uses GPS data to visualize bear movement routes in 3D and analyze detailed movement patterns that take into account terrain and obstacles. The behavior analysis unit can also use drones to monitor bear movements from the air and perform 3D mapping in real time. Furthermore, the behavior analysis unit can analyze movement patterns over a wide area using satellite data to understand the dynamics of bear habitats as a whole. This allows for a detailed understanding of bear movement routes and more accurate behavior predictions.

[0045] The behavior analysis unit not only combines the bear's past behavioral history with environmental changes, but can also analyze external factors that affect bear behavior. For example, the behavior analysis unit monitors human activities that affect bear behavior (e.g., deforestation and tourism activities) and evaluates their impact. The behavior analysis unit can also analyze weather conditions that affect bear behavior (e.g., temperature and precipitation) and predict their impact. Furthermore, the behavior analysis unit can monitor food supply conditions that affect bear behavior (e.g., fruit harvest volume and fish reproduction status) and evaluate their impact. This allows for a comprehensive analysis of external factors that affect bear behavior, enabling more accurate behavior predictions.

[0046] The notification unit not only collects residents' location information in real time, but also monitors their health status to ensure safety during evacuation. For example, the notification unit monitors residents' heart rate and blood pressure and suggests evacuation routes based on their health status. The notification unit can also evaluate residents' physical strength and athletic ability and suggest evacuation routes that are not too strenuous. Furthermore, the notification unit can monitor residents' health status in real time and request medical assistance in an emergency. This makes it possible to suggest evacuation routes based on residents' health status and ensure safety during evacuation.

[0047] The notification unit can not only make long-term risk predictions based on past appearance data, but also formulate local disaster prevention plans based on the risk predictions. For example, the notification unit can identify areas and time periods with a high risk of bear appearances and formulate local disaster prevention plans based on that information. The notification unit can also identify seasons and weather conditions with a high risk of bear appearances and conduct disaster prevention drills based on that information. Furthermore, the notification unit can install protective fences and alarm devices in advance in areas with a high risk of bear appearances. This allows local disaster prevention plans to be formulated based on long-term risk predictions and reduces the risk of encountering a bear.

[0048] The behavior analysis unit not only collects environmental data of bear habitats in real time, but also proposes habitat improvement measures based on the environmental data. For example, the behavior analysis unit analyzes vegetation data of bear habitats and proposes vegetation management to optimize food supply. The behavior analysis unit can also analyze water source data and propose water source management to optimize water supply. Furthermore, the behavior analysis unit can analyze meteorological data and propose habitat improvement measures to respond to climate change. In this way, habitat improvement measures can be proposed based on environmental data of bear habitats, and the bear habitat can be appropriately protected.

[0049] The behavioral analysis unit not only monitors the bears' food supply situation, but can also evaluate the bears' nutritional status based on the food supply situation. For example, the behavioral analysis unit can analyze the bears' diet and evaluate their nutritional balance. The behavioral analysis unit can also monitor the bears' weight and body fat percentage to evaluate their nutritional status. Furthermore, the behavioral analysis unit can collect the bears' blood samples and detect their nutritional status and signs of illness. This allows the system to evaluate the bears' nutritional status based on the food supply situation and take appropriate protective measures if necessary.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The behavior analysis unit analyzes the behavioral patterns of bears. For example, the behavior analysis unit collects data on bear movements, and the AI ​​analyzes that data. The behavior analysis unit can also collect environmental data on bear habitats, and the AI ​​can analyze that data. Furthermore, the behavior analysis unit can analyze the feeding behavior of bears, and the AI ​​can identify behavioral patterns based on that data. Step 2: The prediction unit predicts the risk of bear appearances based on the bear behavior patterns analyzed by the behavior analysis unit. For example, the prediction unit uses AI to identify areas and time periods with a high risk of bear appearances. The prediction unit can also use AI to predict the risk of future appearances based on past appearance data. Furthermore, the prediction unit can also use AI to predict the risk of appearances taking environmental factors into account. Step 3: The notification unit notifies residents of the bear appearance risk predicted by the prediction unit. For example, the notification unit notifies residents via a smartphone app. The notification unit can also notify residents via local disaster prevention radio. Furthermore, the notification unit can also notify residents via email.

[0052] (Example 2) The Coexistence Environment System according to an embodiment of the present invention is a system that reduces bear-related damage to zero and creates an environment where bears and humans can coexist harmoniously. As a result, the Coexistence Environment System predicts bear behavior and notifies residents, allowing them to avoid encounters with bears.

[0053] The coexistence environment system according to the embodiment includes a behavior analysis unit, a prediction unit, and a notification unit. The behavior analysis unit analyzes bear behavior patterns. For example, the behavior analysis unit collects bear movement data and an AI analyzes the data. The behavior analysis unit can also collect environmental data on bear habitats and an AI analyzes the data. The behavior analysis unit can analyze bear feeding behavior and identify behavior patterns based on the data. The prediction unit predicts the risk of bear appearance based on the bear behavior patterns analyzed by the behavior analysis unit. For example, the prediction unit identifies areas and time periods with a high risk of bear appearance. The prediction unit can also predict future risk of bear appearance based on past appearance data. The prediction unit can also predict risk of appearance by taking environmental factors into account. The notification unit notifies residents of the risk of bear appearance predicted by the prediction unit. For example, the notification unit notifies residents via a smartphone app. The notification unit can also notify residents via a local disaster prevention radio. The notification unit can also notify residents via email. As a result, the coexistence environment system according to the embodiment can predict bear behavior and notify residents, thereby enabling them to avoid encounters with bears.

[0054] The behavior analysis unit can be equipped with an emotion estimation function that estimates the emotional state of a bear and predicts its behavior based on that emotional state. For example, when analyzing a bear's behavioral patterns, the behavior analysis unit uses AI to analyze the bear's facial expressions and movements to estimate its emotional state. For example, if a bear is excited, it may move faster, and behavior predictions are based on that information. The behavior analysis unit also analyzes the bear's cries and body movements to estimate its emotional state. For example, when a bear is alert, it often makes a specific cry, and behavior predictions are based on that information. The behavior analysis unit also collects biometric data such as the bear's body temperature and heart rate to estimate its emotional state. For example, if a bear is stressed, its heart rate increases, and behavior predictions are based on that information. This allows for more accurate predictions by estimating the bear's emotional state and predicting its behavior based on that emotion.

[0055] The behavior analysis unit collects biometric data such as bear body temperature and heart rate in real time and can predict behavior based on the biometric data. For example, to analyze bear behavior patterns, the behavior analysis unit uses AI to monitor the bear's body temperature and heart rate in real time and predict behavior based on that data. For example, it identifies the time of day when the bear is active. The behavior analysis unit also collects biometric data of the bear and analyzes it in combination with environmental data. For example, it predicts how the bear will behave when the temperature and humidity are high. The behavior analysis unit also collects biometric data of the bear and compares it with past data to predict behavior. For example, it predicts how the bear will behave during specific seasons and times of day. This allows the system to collect biometric data of the bear in real time and reflect it in behavior predictions, enabling more accurate predictions.

[0056] The behavior analysis unit can simulate future behavior by combining a bear's past behavioral history with environmental changes. For example, when analyzing a bear's behavioral patterns, the behavior analysis unit uses AI to store the bear's past behavioral history in a database and simulate future behavior by combining it with environmental changes. For example, it predicts the bear's movement route based on past data. The behavior analysis unit also combines and analyzes the bear's past behavioral history with weather data to simulate future behavior. For example, it predicts how a bear will behave when the weather changes. The behavior analysis unit also combines and analyzes the bear's past behavioral history with food supply conditions to simulate future behavior. For example, it predicts the possibility of a bear moving to an area with abundant food. This allows for more accurate predictions by combining the bear's past behavioral history with environmental changes to simulate future behavior.

[0057] The notification unit can estimate the emotional state of residents and customize the notification content according to their emotional state. For example, the notification unit adds an emotion estimation function to the bear appearance risk notification system and customizes the notification content according to the emotional state of residents. For example, if a resident is feeling anxious, the notification unit provides notification content that gives a sense of security. The notification unit also adds an emotion estimation function to the bear appearance risk notification system and monitors the emotional state of residents in real time. For example, if a resident is in a state of panic, the notification unit provides advice to help them stay calm. The notification unit also adds an emotion estimation function to the bear appearance risk notification system and adjusts the timing of notifications based on the emotional state of residents. For example, notifications are sent during times when residents are relaxed. This allows for more effective notifications by customizing the notification content according to the emotional state of residents.

[0058] The notification unit can collect location information of residents in real time and individually suggest the optimal evacuation route. For example, the notification unit adds a function to collect location information of residents in real time to a bear appearance risk notification system and individually suggest the optimal evacuation route. For example, it guides residents to the safest evacuation site based on their location. The notification unit also adds a function to collect location information of residents to the bear appearance risk notification system and analyzes the congestion status of evacuation routes in real time. For example, it suggests alternative routes to avoid congestion. The notification unit also adds a function to collect location information of residents to the bear appearance risk notification system and evaluates the safety of evacuation routes. For example, it prioritizes suggesting routes with a low risk of bear appearances. In this way, by collecting location information of residents in real time and individually suggesting the optimal evacuation route, safer evacuation is possible.

[0059] The notification unit can perform long-term risk predictions based on past appearance data and notify residents in advance. The notification unit, for example, adds a function to the bear appearance risk notification system that analyzes past appearance data and performs long-term risk predictions. For example, the notification unit predicts the risk of bear appearances during specific seasons or times of day. The notification unit also adds a function to the bear appearance risk notification system that performs risk predictions based on past appearance data and notifies residents in advance. For example, on days when the risk of bear appearances is high, a warning is issued in advance. The notification unit also adds a function to the bear appearance risk notification system that analyzes past appearance data and performs risk predictions. For example, the notification unit predicts periods when the risk of bear appearances will increase in specific areas and notifies residents. In this way, by performing long-term risk predictions based on past appearance data and notifying residents in advance, it becomes possible to take measures earlier.

[0060] The behavior analysis unit can collect environmental data on bear habitats in real time and automatically adjust protective measures based on the environmental data. For example, in the protection and management of bear habitats, the AI ​​collects environmental data such as temperature, humidity, and precipitation in real time and automatically adjusts protective measures in response to environmental changes. For example, when temperatures are high, measures are taken to suppress bear activity. In the protection and management of bear habitats, the AI ​​collects vegetation data in real time and automatically adjusts protective measures in response to environmental changes. For example, when food supplies are insufficient, vegetation management is carried out. In the protection and management of bear habitats, the AI ​​collects water source data in real time and automatically adjusts protective measures in response to environmental changes. For example, when water sources are depleted, measures are taken to increase the water supply. This allows for the proper protection of bear habitats by collecting environmental data on bear habitats in real time and automatically adjusting protective measures in response to environmental changes.

[0061] The behavior analysis unit can monitor the food supply situation for bears and propose vegetation management based on the food supply situation. For example, in the protection and management of bear habitats, the behavior analysis unit uses AI to monitor the food supply situation for bears and propose vegetation management to optimize the food supply. For example, if there is a shortage of food for bears, the AI ​​increases the number of specific plants. In addition, in the protection and management of bear habitats, the AI ​​monitors the food supply situation for bears and proposes vegetation management to optimize the food supply. For example, if there is an excess of food for bears, the AI ​​adjusts the vegetation. In addition, in the protection and management of bear habitats, the AI ​​monitors the food supply situation for bears and proposes vegetation management to optimize the food supply. For example, if there is a shortage of food for bears in a particular season, seasonal vegetation management is performed. In this way, the AI ​​can monitor the food supply situation for bears and propose vegetation management to optimize the food supply, thereby properly protecting bear habitats.

[0062] The behavior analysis unit can estimate the emotional state of a bear and propose protection measures based on that emotional state. For example, in the protection and management of bear habitats, the behavior analysis unit uses AI to analyze the bear's facial expressions and movements to estimate its emotional state. For example, if a bear is feeling stressed, protection measures to reduce the stress are proposed. The behavior analysis unit also analyzes the bear's cries and body movements to estimate its emotional state. For example, if a bear is alert, protection measures to reduce the alertness are proposed. The behavior analysis unit also collects biometric data such as the bear's body temperature and heart rate to estimate its emotional state. For example, if a bear is excited, protection measures to reduce the excitement are proposed. In this way, by estimating the emotional state of a bear and proposing protection measures based on that emotion, bear habitats can be appropriately protected.

[0063] The behavior analysis unit can apply the bear behavior pattern analysis to other wild animals to predict the behavior of animal species. For example, the behavior analysis unit applies the bear behavior pattern analysis technology to other wild animals, such as wolves and deer, to predict the behavior of each. This allows for comprehensive management of the behavior of multiple animal species. The behavior analysis unit also applies the bear behavior pattern analysis technology to birds and small mammals to predict the behavior of different animal species. For example, it predicts the migration patterns of migratory birds. The behavior analysis unit also applies the bear behavior pattern analysis technology to marine life to predict the behavior of different animal species. For example, it predicts the migration patterns of dolphins and whales. This allows for comprehensive management of the behavior of multiple animal species by applying the bear behavior pattern analysis technology to other wild animals.

[0064] The behavior analysis unit can apply bear behavior pattern analysis to animals in urban areas to promote coexistence with animals. For example, the behavior analysis unit applies bear behavior pattern analysis technology to stray cats and dogs in urban areas to predict the behavior of each. This promotes coexistence with animals in urban areas. The behavior analysis unit also applies bear behavior pattern analysis technology to birds and small mammals in urban areas to predict their behavior. For example, it predicts the behavior patterns of pigeons and squirrels. The behavior analysis unit also applies bear behavior pattern analysis technology to vermin in urban areas (e.g., rats and cockroaches) to predict their behavior. This promotes effective measures against vermin. By applying bear behavior pattern analysis technology to animals in urban areas, it is possible to promote coexistence with animals in urban areas.

[0065] The behavior analysis unit adds an emotion estimation function to the bear's behavior pattern analysis, and can predict behavior based on the bear's emotional state. For example, the behavior analysis unit adds an emotion estimation function to the bear's behavior pattern analysis, and predicts behavior based on the bear's emotional state. For example, if the bear is feeling stressed, it is likely to behave in a specific way. The behavior analysis unit also adds an emotion estimation function to the bear's behavior pattern analysis, and monitors the bear's emotional state in real time. For example, if the bear is excited, it is likely to move faster. The behavior analysis unit also adds an emotion estimation function to the bear's behavior pattern analysis, and simulates future behavior based on the bear's emotional state. For example, if the bear is wary, it is likely to stay in a specific area. In this way, by adding the emotion estimation function to the bear's behavior pattern analysis, it becomes possible to predict behavior based on the bear's emotional state.

[0066] The behavior analysis unit monitors the behavior of bears, estimates their emotional state using an emotion estimation function, and can predict their behavior based on their emotional state. For example, in bear behavior monitoring technology, the behavior analysis unit uses AI to analyze the bear's facial expressions and movements to estimate its emotional state. For example, if a bear is excited, it is likely to move faster, and behavior predictions are based on this information. In addition, in bear behavior monitoring technology, the AI ​​analyzes the bear's cries and body movements to estimate its emotional state. For example, if a bear is alert, it often makes a specific cry, and behavior predictions are based on this information. In addition, in bear behavior monitoring technology, the AI ​​collects biometric data such as the bear's body temperature and heart rate to estimate its emotional state. For example, if a bear is stressed, its heart rate increases, and behavior predictions are based on this information. This makes it possible to monitor the bear's behavior and predict its behavior based on its emotional state using the emotion estimation function.

[0067] The behavior analysis unit collects bear biometric data in real time and can predict behavior based on the biometric data. For example, in bear behavior monitoring technology, the behavior analysis unit uses AI to monitor bear body temperature and heart rate in real time and predict behavior based on that data. For example, it identifies the time of day when bears are active. The behavior analysis unit also collects bear biometric data and analyzes it in combination with environmental data. For example, it predicts how bears will behave when the temperature and humidity are high. The behavior analysis unit also collects bear biometric data and compares it with past data to predict behavior. For example, it predicts how bears will behave in specific seasons and times of day. This allows bear biometric data to be collected in real time and reflected in behavior predictions, enabling more accurate predictions.

[0068] The behavior analysis unit can simulate future behavior based on data on the bear's past behavior. For example, in bear behavior monitoring technology, the behavior analysis unit uses AI to store data on the bear's past behavior in a database and combine it with environmental changes to simulate future behavior. For example, it predicts the bear's movement route based on past data. The behavior analysis unit also combines and analyzes data on the bear's past behavior with weather data to simulate future behavior. For example, it predicts how the bear will behave when the weather changes. The behavior analysis unit also combines and analyzes data on the bear's past behavior with the food supply situation to simulate future behavior. For example, it predicts the possibility of the bear moving to an area with abundant food. This allows for more accurate predictions by simulating future behavior based on data on the bear's past behavior.

[0069] The behavior analysis unit can apply the bear behavior monitoring technology to other wild animals to predict the behavior of animal species. For example, the behavior analysis unit applies the bear behavior monitoring technology to monitoring the behavior of other wild animals, such as wolves and deer, and predicts the behavior of each. This allows for comprehensive management of the behavior of multiple animal species. The behavior analysis unit also applies the bear behavior monitoring technology to monitoring the behavior of birds and small mammals, and predicts the behavior of different animal species. For example, it predicts the migration patterns of migratory birds. The behavior analysis unit also applies the bear behavior monitoring technology to monitoring the behavior of marine life, and predicts the behavior of different animal species. For example, it predicts the migration patterns of dolphins and whales. This allows for comprehensive management of the behavior of multiple animal species by applying the bear behavior monitoring technology to other wild animals.

[0070] The behavior analysis unit can apply bear behavior monitoring technology to animals in urban areas to promote coexistence with animals. For example, the behavior analysis unit applies bear behavior monitoring technology to stray cats and dogs in urban areas to predict the behavior of each. This promotes coexistence with animals in urban areas. The behavior analysis unit also applies bear behavior monitoring technology to birds and small mammals in urban areas to predict their behavior. For example, it predicts the behavior patterns of pigeons and squirrels. The behavior analysis unit also applies bear behavior monitoring technology to vermin in urban areas (for example, rats and cockroaches) to predict their behavior. This promotes effective measures against vermin. By applying bear behavior monitoring technology to animals in urban areas, it is possible to promote coexistence with animals in urban areas.

[0071] The behavior analysis unit adds an emotion estimation function to the bear behavior monitoring technology, enabling it to predict behavior based on the emotional state. For example, the behavior analysis unit adds an emotion estimation function to the bear behavior monitoring technology and predicts behavior based on the emotional state of the bear. For example, if a bear is feeling stressed, it is likely to behave in a specific way. The behavior analysis unit also adds an emotion estimation function to the bear behavior monitoring technology and monitors the emotional state of the bear in real time. For example, if a bear is excited, it is likely to move faster. The behavior analysis unit also adds an emotion estimation function to the bear behavior monitoring technology and simulates future behavior based on the emotional state of the bear. For example, if a bear is alert, it is likely to stay in a specific area. Thus, by adding the emotion estimation function to the bear behavior monitoring technology, it becomes possible to predict behavior based on the emotional state of the bear.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The behavioral analysis unit not only analyzes the behavioral patterns of the bears, but can also monitor their health. For example, the behavioral analysis unit periodically measures the bears' weight and body fat percentage to assess their health. The behavioral analysis unit can also collect blood samples from the bears to detect their nutritional status and signs of illness. Furthermore, the behavioral analysis unit can monitor the bears' exercise levels and rest times, and evaluate their stress levels and fatigue. This allows for comprehensive management of the bears' health and, if necessary, take appropriate protective measures.

[0074] The behavioral analysis unit can not only estimate the emotional state of bears, but also analyze their social behavior. For example, the behavioral analysis unit can analyze communication patterns between bears to evaluate their social connections. The behavioral analysis unit can also monitor changes in bear group structure and leadership to understand social dynamics. Furthermore, the behavioral analysis unit can analyze bear breeding and parenting behavior to evaluate reproductive success and cub survival rates. This allows for a comprehensive understanding of bear social behavior and the design of more effective conservation measures.

[0075] The behavior analysis unit not only collects bear biometric data in real time, but can also perform 3D mapping of bear movement routes. For example, the behavior analysis unit uses GPS data to visualize bear movement routes in 3D and analyze detailed movement patterns that take into account terrain and obstacles. The behavior analysis unit can also use drones to monitor bear movements from the air and perform 3D mapping in real time. Furthermore, the behavior analysis unit can analyze movement patterns over a wide area using satellite data to understand the dynamics of bear habitats as a whole. This allows for a detailed understanding of bear movement routes and more accurate behavior predictions.

[0076] The behavior analysis unit not only combines the bear's past behavioral history with environmental changes, but can also analyze external factors that affect bear behavior. For example, the behavior analysis unit monitors human activities that affect bear behavior (e.g., deforestation and tourism activities) and evaluates their impact. The behavior analysis unit can also analyze weather conditions that affect bear behavior (e.g., temperature and precipitation) and predict their impact. Furthermore, the behavior analysis unit can monitor food supply conditions that affect bear behavior (e.g., fruit harvest volume and fish reproduction status) and evaluate their impact. This allows for a comprehensive analysis of external factors that affect bear behavior, enabling more accurate behavior predictions.

[0077] The notification unit can not only estimate the emotional state of residents, but also analyze their behavioral patterns to determine the optimal notification timing. For example, the notification unit can analyze the resident's smartphone usage history to identify the time periods when they are most likely to receive notifications. The notification unit can also analyze the resident's location information and send notifications when they are in a specific location. Furthermore, the notification unit can analyze the resident's past notification history, evaluate the effectiveness of notifications, and select the optimal notification method. This allows the optimal notification timing to be determined based on the resident's behavioral patterns, enabling more effective notifications.

[0078] The notification unit not only collects residents' location information in real time, but also monitors their health status to ensure safety during evacuation. For example, the notification unit monitors residents' heart rate and blood pressure and suggests evacuation routes based on their health status. The notification unit can also evaluate residents' physical strength and athletic ability and suggest evacuation routes that are not too strenuous. Furthermore, the notification unit can monitor residents' health status in real time and request medical assistance in an emergency. This makes it possible to suggest evacuation routes based on residents' health status and ensure safety during evacuation.

[0079] The notification unit can not only make long-term risk predictions based on past appearance data, but also formulate local disaster prevention plans based on the risk predictions. For example, the notification unit can identify areas and time periods with a high risk of bear appearances and formulate local disaster prevention plans based on that information. The notification unit can also identify seasons and weather conditions with a high risk of bear appearances and conduct disaster prevention drills based on that information. Furthermore, the notification unit can install protective fences and alarm devices in advance in areas with a high risk of bear appearances. This allows local disaster prevention plans to be formulated based on long-term risk predictions and reduces the risk of encountering a bear.

[0080] The behavior analysis unit not only collects environmental data of bear habitats in real time, but also proposes habitat improvement measures based on the environmental data. For example, the behavior analysis unit analyzes vegetation data of bear habitats and proposes vegetation management to optimize food supply. The behavior analysis unit can also analyze water source data and propose water source management to optimize water supply. Furthermore, the behavior analysis unit can analyze meteorological data and propose habitat improvement measures to respond to climate change. In this way, habitat improvement measures can be proposed based on environmental data of bear habitats, and the bear habitat can be appropriately protected.

[0081] The behavioral analysis unit not only monitors the bears' food supply situation, but can also evaluate the bears' nutritional status based on the food supply situation. For example, the behavioral analysis unit can analyze the bears' diet and evaluate their nutritional balance. The behavioral analysis unit can also monitor the bears' weight and body fat percentage to evaluate their nutritional status. Furthermore, the behavioral analysis unit can collect the bears' blood samples and detect their nutritional status and signs of illness. This allows the system to evaluate the bears' nutritional status based on the food supply situation and take appropriate protective measures if necessary.

[0082] The behavior analysis unit can not only estimate the emotional state of the bear, but also evaluate the stress level of the bear and suggest stress reduction measures. For example, the behavior analysis unit can monitor the heart rate and body temperature of the bear to evaluate the stress level. The behavior analysis unit can also analyze the behavioral patterns of the bear and identify the cause of stress. Furthermore, the behavior analysis unit can evaluate the stress level of the bear and suggest environmental improvement measures to reduce stress. In this way, by evaluating the stress level based on the emotional state of the bear and suggesting stress reduction measures, the health of the bear can be appropriately managed.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The behavior analysis unit analyzes the behavioral patterns of bears. For example, the behavior analysis unit collects data on bear movements, and the AI ​​analyzes that data. The behavior analysis unit can also collect environmental data on bear habitats, and the AI ​​can analyze that data. Furthermore, the behavior analysis unit can analyze the feeding behavior of bears, and the AI ​​can identify behavioral patterns based on that data. Step 2: The prediction unit predicts the risk of bear appearances based on the bear behavior patterns analyzed by the behavior analysis unit. For example, the prediction unit uses AI to identify areas and time periods with a high risk of bear appearances. The prediction unit can also use AI to predict the risk of future appearances based on past appearance data. Furthermore, the prediction unit can also use AI to predict the risk of appearances taking environmental factors into account. Step 3: The notification unit notifies residents of the bear appearance risk predicted by the prediction unit. For example, the notification unit notifies residents via a smartphone app. The notification unit can also notify residents via local disaster prevention radio. Furthermore, the notification unit can also notify residents via email.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A behavior analysis unit that analyzes bear behavior patterns; a prediction unit that predicts a risk of a bear appearing based on the bear behavior pattern analyzed by the behavior analysis unit; a notification unit that notifies residents of the risk of bear appearance predicted by the prediction unit. A system characterized by:

2. The behavior analysis unit Emotion estimation function that estimates the emotional state of the bear and predicts its behavior based on that emotional state 2. The system of claim 1.

3. The behavior analysis unit Collecting bears' biological data, such as body temperature and heart rate, in real time and predicting their behavior based on that data 2. The system of claim 1.

4. The behavior analysis unit Combining the bear's past behavior history with environmental changes to simulate future behavior 2. The system of claim 1.

5. The notification unit Estimate the emotional state of residents and customize notification content according to said emotional state.

2. The system of claim 1.

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