system

Abstract

The system according to this embodiment aims to provide advice based on weather information and to provide rapid contact information in emergencies. [Solution] The system according to the embodiment comprises a collection unit, an advice unit, and a communication unit. The collection unit collects weather information. The advice unit provides advice based on the information collected by the collection unit. The communication unit provides communication information in the event of an emergency.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can provide advice based on weather information and provide rapid contact information in emergencies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The camping support system according to an embodiment of the present invention is a portable device for improving safety and convenience during camping. This camping support system utilizes AI installed in a portable device to provide necessary information in real time during camping. The system collects local weather information and immediately provides advice to optimize tarp setup and tent placement. Based on wind direction and rainfall, the system supports a safe and optimal camping experience. In emergencies, the system quickly provides contact information for medical facilities and rescue points and utilizes location information to support notification to the nearest rescue service. This improves the safety of outdoor activities and enhances the enjoyment and convenience of camping. The camping support system allows users to easily record photos and videos using its recording function and create an organized camping diary. For example, the system can collect information such as weather, wind direction, and rainfall to understand the conditions of the campsite. This ensures safety during camping. The system collects local weather information and immediately provides advice to optimize tarp setup and tent placement. For example, by optimizing the placement of tarps and tents based on wind direction and rainfall, it can provide a safe and comfortable camping experience. The camp support system provides rapid contact information to medical facilities and rescue points in emergencies and uses location information to support notification to the nearest rescue service. For example, in an emergency, the device can automatically transmit location information and notify the nearest rescue service, enabling a quick response. The camp support system also allows users to easily record photos and videos using its recording function and create an organized camp diary. For example, memories of camping can be recorded with photos and videos, and later organized into a camp diary to relive those memories. In this way, the AI ​​built into the portable device can improve safety and convenience during camping. This enhances the enjoyment and convenience of outdoor activities and allows users to organize and record their camping memories.This allows camp support systems to improve safety and convenience during camping trips.

[0029] The camp support system according to this embodiment comprises a data collection unit, an advice unit, and a communication unit. The data collection unit collects weather information. The data collection unit collects information such as temperature, humidity, wind speed, and precipitation. The data collection unit can collect weather information in real time using AI. For example, the data collection unit measures temperature using a temperature sensor and humidity using a humidity sensor. The data collection unit measures wind speed using an anemometer and precipitation using a rain gauge. The data collection unit inputs this information into the AI, which analyzes the weather information. The advice unit provides advice based on the information collected by the data collection unit. For example, the advice unit provides advice to optimize tarp installation and tent placement. The advice unit can analyze the collected information using AI and provide optimal advice. For example, the advice unit provides advice to optimize tarp and tent installation locations based on wind direction and precipitation. The advice unit uses AI to analyze the collected information and proposes the optimal installation location. The communication unit provides communication information in emergencies. For example, the communication unit provides communication information to medical facilities and rescue points. The liaison unit can use AI to quickly provide contact information in emergencies. For example, the liaison unit can use location information to support notification to the nearest rescue service. The liaison unit's AI analyzes location information and identifies the nearest rescue service. As a result, the camp support system according to this embodiment can collect weather information, provide advice, and provide emergency contact information.

[0030] The data collection unit collects weather information. For example, it collects information such as temperature, humidity, wind speed, and precipitation. Specifically, it measures temperature using a temperature sensor, humidity using a humidity sensor, wind speed using an anemometer, and precipitation using a rain gauge. These sensors are installed at the campsite and collect data in real time. The collected data is transmitted to a central database via wireless communication. The data collection unit can use AI to collect weather information in real time. The AI ​​analyzes the data from these sensors and predicts weather changes. For example, it detects sudden changes in temperature, increases in humidity, and increases in wind speed, and predicts weather changes based on this information. The data collection unit inputs this information into the AI, which analyzes the weather information. The AI ​​refers to historical data and weather models to analyze the current weather conditions. This allows the data collection unit to grasp the weather information at the campsite in real time and respond quickly. Furthermore, the data collection unit can store this data on a cloud server and share it with other systems and departments. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The advice unit provides advice based on the information collected by the data collection unit. For example, the advice unit provides advice on optimizing tarp placement and tent location. Specifically, it provides advice on optimizing tarp and tent placement based on wind direction and rainfall. The advice unit can use AI to analyze the collected information and provide optimal advice. The AI ​​analyzes wind direction and rainfall data and proposes the optimal placement location. For example, if the wind direction is strong, it will suggest setting up the tarp in a location that blocks the wind, and if there is heavy rainfall, it will suggest setting up the tent in a well-drained location. The advice unit uses AI to analyze the collected information and propose the optimal placement location. Furthermore, the advice unit can provide more specific advice based on past data and experience. For example, it can refer to past campsite data and propose the optimal placement location under specific conditions. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the advice unit to provide users with optimal advice and improve the safety and comfort of camping.

[0032] The liaison department provides contact information in emergencies. For example, it provides contact information for medical facilities and rescue points. Specifically, it uses location information to support notification to the nearest rescue service. The liaison department can use AI to provide contact information quickly in emergencies. The AI ​​analyzes location information and identifies the nearest rescue service. For example, based on the user's current location, it identifies the nearest medical facility or rescue point and provides contact information. The liaison department notifies the user of this information to support a rapid response. Furthermore, the liaison department can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. The liaison department uses AI to analyze location information and identify the nearest rescue service. This allows the liaison department to provide contact information quickly and reliably in emergencies, ensuring user safety. Furthermore, the liaison department can collect user feedback and continuously improve the accuracy and effectiveness of the contact information. This allows the liaison department to provide contact information quickly and reliably to users and support emergency response.

[0033] It is equipped with a recording unit that provides recording functionality. The recording unit provides recording functionality. For example, the recording unit provides recording functionality to record memories during camping. The recording unit can use AI to optimize the timing and content of recordings. For example, the recording unit can adjust the recording resolution and recording time. The recording unit is equipped with storage for saving recording data. The recording unit can also save recording data to the cloud. The recording unit also has functions for organizing recording data. The recording unit provides a tagging function to classify recording data and make it easier to search. The recording unit also has functions for editing recording data. The recording unit can trim recording data and apply filters. In this way, the recording unit can provide recording functionality to record and organize memories during camping.

[0034] It includes an organization section for organizing photos and videos. The organization section organizes photos and videos. For example, the organization section provides functions for organizing photos and videos taken during camping. The organization section can use AI to optimize the organization of photos and videos. For example, the organization section provides a tagging function to classify photos and videos and make them easier to search. The organization section also has functions for editing photos and videos. The organization section can crop photos and videos and apply filters. The organization section can also save photos and videos to the cloud. The organization section also has functions for sharing photos and videos. The organization section can post photos and videos to social media or send them by email. In this way, the organization section can efficiently organize and manage photos and videos taken during camping.

[0035] It includes a creation section for creating camp diaries. The creation section creates camp diaries. For example, the creation section creates camp diaries to organize and record memories from camping trips. The creation section can optimize the creation of camp diaries using AI. The creation section provides functions to insert photos and videos into camp diaries, for example. The creation section also has a function to add text to camp diaries. The creation section can also save camp diaries to the cloud. The creation section also has a function to share camp diaries. The creation section can post camp diaries to social media or send them by email. In this way, the creation section can create camp diaries to organize and record memories from camping trips.

[0036] The data collection unit can collect weather information, wind direction, and precipitation data. For example, the unit collects information such as temperature, humidity, wind speed, and precipitation. The data collection unit can collect weather information in real time using AI. For example, the unit measures temperature using a temperature sensor and humidity using a humidity sensor. The unit measures wind speed using an anemometer and precipitation using a rain gauge. The unit inputs this information into the AI, which analyzes the weather information. As a result, the data collection unit can improve camping safety by collecting information such as weather, wind direction, and precipitation.

[0037] The advice unit can provide advice to optimize tarp setup and tent placement based on collected information. For example, the advice unit can provide advice to optimize tarp setup and tent placement. The advice unit can use AI to analyze collected information and provide optimal advice. For example, the advice unit can provide advice to optimize tarp and tent placement based on wind direction and rainfall. The advice unit uses AI to analyze collected information and propose the optimal placement location. This allows the advice unit to provide a safe and comfortable camping experience by offering advice to optimize tarp setup and tent placement.

[0038] The liaison unit can provide contact information to medical facilities and rescue points in emergencies and use location information to support reporting to the nearest rescue service. For example, the liaison unit can provide contact information to medical facilities and rescue points. The liaison unit can use AI to provide contact information quickly in emergencies. For example, the liaison unit can use location information to support reporting to the nearest rescue service. The liaison unit's AI analyzes location information to identify the nearest rescue service. This enables the liaison unit to respond quickly in emergencies and improves safety during camping.

[0039] The data collection unit can improve the accuracy of weather information collection by referring to past weather data. For example, the data collection unit can improve the accuracy of collection by analyzing weather patterns in a specific area based on past weather data. The data collection unit can improve the accuracy of collection by predicting weather changes in a specific time period by referring to past weather data. The data collection unit can improve the accuracy of collection by analyzing the frequency of extreme weather events using past weather data. In this way, the data collection unit can improve the accuracy of weather information collection by referring to past weather data.

[0040] The data collection unit can select the types of information to collect based on the topographical information of the campsite when collecting weather information. For example, if the campsite is in a mountainous area, the data collection unit will prioritize collecting wind speed and precipitation information. If the campsite is in a coastal area, the data collection unit can prioritize collecting tidal information and wind direction information. If the campsite is on flat land, the data collection unit can prioritize collecting temperature and humidity information. In this way, the data collection unit can collect more appropriate weather information by taking into account the topographical information of the campsite.

[0041] The data collection unit can adjust the level of detail of the information it collects based on the user's camping experience. For example, if the user is a novice camper, the unit can collect detailed weather information and provide specific advice. If the user is an experienced camper, the unit can collect only the minimum necessary weather information and provide concise advice. If the user is a seasoned camper, the unit can collect only the specific weather information the user needs. This allows the data collection unit to provide more appropriate information by adjusting the level of detail according to the user's camping experience.

[0042] The data collection unit can collect relevant weather information by analyzing users' social media activity when gathering weather information. For example, the data collection unit can collect weather information for a specific area based on campsite information shared by users on social media. The data collection unit can collect the latest weather information from camping-related accounts that users follow on social media. The data collection unit can collect relevant weather information by analyzing local weather conditions from photos and videos posted by users on social media. In this way, the data collection unit can collect relevant weather information by analyzing users' social media activity.

[0043] The advisory unit can adjust the level of detail of its advice based on the reliability of the collected weather information when providing advice. For example, the advisory unit can provide detailed advice based on reliable weather information. The advisory unit can provide general advice based on unreliable weather information. If the reliability of the weather information is unknown, the advisory unit can provide advice by presenting multiple scenarios. This allows the advisory unit to provide more appropriate advice by adjusting the level of detail of its advice based on the reliability of the weather information.

[0044] The advice unit can customize the content of its advice based on the topographical information of the campsite when providing advice. For example, if the campsite is in a mountainous area, the advice unit can advise on the placement of tarps and tents based on wind speed and precipitation. If the campsite is on the coast, the advice unit can advise on the placement of tarps and tents based on tidal information and wind direction. If the campsite is on flat land, the advice unit can advise on the placement of tarps and tents based on temperature and humidity. In this way, the advice unit can provide more appropriate advice by taking into account the topographical information of the campsite.

[0045] The advice section can adjust the level of detail in its advice based on the user's camping experience. For example, for a beginner camper, it can provide detailed advice and specific steps. For an experienced camper, it can provide minimal advice and concise steps. For a seasoned camper, it can provide only the specific advice the user needs. This allows the advice section to provide more appropriate advice by adjusting the level of detail according to the user's camping experience.

[0046] The advice team can analyze a user's social media activity when providing advice and offer relevant advice. For example, the advice team can provide specific advice based on information about campsites shared by the user on social media. The advice team can provide the latest advice from camping-related accounts that the user follows on social media. The advice team can analyze local conditions from photos and videos posted by the user on social media and offer relevant advice. In this way, the advice team can provide relevant advice by analyzing the user's social media activity.

[0047] The advice team can analyze a user's social media activity when providing advice and offer relevant advice. For example, the advice team can provide specific advice based on information about campsites shared by the user on social media. The advice team can provide the latest advice from camping-related accounts that the user follows on social media. The advice team can analyze local conditions from photos and videos posted by the user on social media and offer relevant advice. In this way, the advice team can provide relevant advice by analyzing the user's social media activity.

[0048] The liaison department can improve the accuracy of emergency communications by referring to past emergency case data. For example, the liaison department can select the optimal communication method based on past emergency case data. The liaison department can select contacts capable of a quick response by referring to past emergency case data. The liaison department can optimize emergency communication procedures using past emergency case data. As a result, the accuracy of emergency communications is improved by the liaison department referring to past emergency case data.

[0049] The liaison department can select contacts based on the topographical information of the campsite in the event of an emergency. For example, if the campsite is in a mountainous area, the liaison department will contact the nearest mountain rescue team. If the campsite is on the coast, the liaison department can contact the nearest Japan Coast Guard. If the campsite is on flat land, the liaison department can contact the nearest police or fire department. This allows the liaison department to select more appropriate contacts by considering the topographical information of the campsite.

[0050] The liaison department can adjust the content of emergency communications based on the user's camping experience. For example, for a novice camper, the liaison department can provide detailed information and specific steps. For an experienced camper, the liaison department can provide only the essential information and concise steps. For a seasoned camper, the liaison department can provide only the specific information the user needs. This allows the liaison department to provide more appropriate information by adjusting the content of communications according to the user's camping experience.

[0051] The liaison department can analyze a user's social media activity and provide relevant contact information in the event of an emergency. For example, the liaison department can provide specific contact information based on campsite information shared by the user on social media. The liaison department can provide the latest contact information from camp-related accounts that the user follows on social media. The liaison department can analyze local conditions from photos and videos posted by the user on social media and provide relevant contact information. In this way, the liaison department can provide relevant contact information by analyzing a user's social media activity.

[0052] The recording unit can improve recording accuracy by referring to past recording data during recording. For example, the recording unit can prioritize recording specific scenes or moments based on past recording data. The recording unit can reflect the user's preferred recording style by referring to past recording data. The recording unit can optimize the timing and angle of recording using past recording data. As a result, the recording unit improves recording accuracy by referring to past recording data.

[0053] The recording unit can adjust the recording content based on the user's camping experience. For example, for a beginner camper, it will record basic camping procedures and scenery. For an experienced camper, it can record specific activities or events. For a seasoned camper, it can record specific scenes or moments that the user prefers. This allows the recording unit to adjust the recording content according to the user's camping experience, resulting in more appropriate recordings.

[0054] The organization unit can improve the accuracy of organization by referring to past organization data during the organization process. For example, the organization unit can prioritize organizing specific scenes or moments based on past organization data. The organization unit can reflect the user's preferred organization style by referring to past organization data. The organization unit can optimize the organization method and order using past organization data. As a result, the organization unit improves the accuracy of organization by referring to past organization data.

[0055] The organization function can adjust its content based on the user's camping experience. For example, for a beginner camper, it will organize content focusing on basic camping procedures and scenery. For an experienced camper, it can organize content focusing on specific activities and events. For a seasoned camper, it can organize content focusing on specific scenes and moments that the user likes. This allows the organization function to adjust its content according to the user's camping experience, resulting in more appropriate organization.

[0056] The creation unit can improve the accuracy of diary creation by referring to past diary data. For example, the creation unit can prioritize recording specific scenes or moments based on past diary data. The creation unit can reflect the user's preferred diary style by referring to past diary data. The creation unit can optimize the method and order of diary creation using past diary data. As a result, the accuracy of diary creation is improved by referring to past diary data.

[0057] The creation function can adjust the content of the diary based on the user's camping experience. For example, for a beginner camper, the creation function will focus on recording basic camping procedures and scenery. For an experienced camper, the creation function can focus on recording specific activities and events. For a seasoned camper, the creation function can focus on recording specific scenes and moments that the user likes. In this way, the creation function can adjust the content according to the user's camping experience, enabling the creation of a more appropriate diary.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The camping support system can also include a health management unit that monitors the user's health status. This unit can, for example, measure vital signs such as heart rate, body temperature, and blood pressure in real time, and issue alerts if abnormalities are detected. Based on the user's health status, the health management unit can also provide advice on appropriate rest times and hydration. This helps reduce health risks during camping and supports a safe and comfortable camping experience.

[0060] The camping support system can also include a meal management unit to manage the user's meals. For example, the meal management unit can suggest suitable meal menus during the camping trip based on the user's food preferences and allergy information. It can also recommend meals containing necessary nutrients based on the user's activity level and health condition. This makes meal management during camping easier, allowing users to enjoy healthy and balanced meals.

[0061] The camping support system can also be equipped with a navigation unit to assist user movement. For example, the navigation unit provides map information about the area around the campsite, guiding users to their destination safely. The navigation unit can also reflect real-time weather and terrain information to suggest the optimal route. This allows users to move around the campsite without getting lost, ensuring a safe and efficient camping experience.

[0062] The camping support system can also include a sleep management unit to manage the user's sleep. For example, the sleep management unit can monitor the user's sleep patterns and provide an optimal sleep environment. To improve the user's sleep quality, the sleep management unit can set alarms at appropriate times and play relaxing music. This allows users to ensure high-quality sleep even while camping, preparing them for the next day's activities.

[0063] The camping support system can also include a luggage management unit to manage the user's belongings. For example, the luggage management unit can create a list of the user's belongings and manage necessary items in a checklist format. The luggage management unit can also set reminders to prevent users from forgetting items and suggest optimal packing methods by weighing the luggage. This makes luggage management easier for users, ensuring a smooth camping preparation process.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects weather information. The data collection unit collects information such as temperature, humidity, wind speed, and precipitation. The data collection unit can collect weather information in real time using AI. For example, the data collection unit measures temperature using a temperature sensor and humidity using a humidity sensor. The data collection unit measures wind speed using an anemometer and precipitation using a rain gauge. The data collection unit inputs this information into the AI, which then analyzes the weather information. Step 2: The advice unit provides advice based on the information collected by the data collection unit. For example, the advice unit provides advice to optimize tarp setup and tent placement. The advice unit can use AI to analyze the collected information and provide optimal advice. For example, the advice unit provides advice to optimize tarp and tent setup locations based on wind direction and rainfall. The advice unit uses AI to analyze the collected information and propose the optimal setup location. Step 3: The liaison unit provides contact information in emergencies. The liaison unit provides contact information, for example, to medical facilities and rescue points. The liaison unit can use AI to quickly provide contact information in emergencies. For example, the liaison unit can use location information to support notification to the nearest rescue service. The liaison unit's AI analyzes location information and identifies the nearest rescue service.

[0066] (Example of form 2) The camping support system according to an embodiment of the present invention is a portable device for improving safety and convenience during camping. This camping support system utilizes AI installed in a portable device to provide necessary information in real time during camping. The system collects local weather information and immediately provides advice to optimize tarp setup and tent placement. Based on wind direction and rainfall, the system supports a safe and optimal camping experience. In emergencies, the system quickly provides contact information for medical facilities and rescue points and utilizes location information to support notification to the nearest rescue service. This improves the safety of outdoor activities and enhances the enjoyment and convenience of camping. The camping support system allows users to easily record photos and videos using its recording function and create an organized camping diary. For example, the system can collect information such as weather, wind direction, and rainfall to understand the conditions of the campsite. This ensures safety during camping. The system collects local weather information and immediately provides advice to optimize tarp setup and tent placement. For example, by optimizing the placement of tarps and tents based on wind direction and rainfall, it can provide a safe and comfortable camping experience. The camp support system provides rapid contact information to medical facilities and rescue points in emergencies and uses location information to support notification to the nearest rescue service. For example, in an emergency, the device can automatically transmit location information and notify the nearest rescue service, enabling a quick response. The camp support system also allows users to easily record photos and videos using its recording function and create an organized camp diary. For example, memories of camping can be recorded with photos and videos, and later organized into a camp diary to relive those memories. In this way, the AI ​​built into the portable device can improve safety and convenience during camping. This enhances the enjoyment and convenience of outdoor activities and allows users to organize and record their camping memories.This allows camp support systems to improve safety and convenience during camping trips.

[0067] The camp support system according to this embodiment comprises a data collection unit, an advice unit, and a communication unit. The data collection unit collects weather information. The data collection unit collects information such as temperature, humidity, wind speed, and precipitation. The data collection unit can collect weather information in real time using AI. For example, the data collection unit measures temperature using a temperature sensor and humidity using a humidity sensor. The data collection unit measures wind speed using an anemometer and precipitation using a rain gauge. The data collection unit inputs this information into the AI, which analyzes the weather information. The advice unit provides advice based on the information collected by the data collection unit. For example, the advice unit provides advice to optimize tarp installation and tent placement. The advice unit can analyze the collected information using AI and provide optimal advice. For example, the advice unit provides advice to optimize tarp and tent installation locations based on wind direction and precipitation. The advice unit uses AI to analyze the collected information and proposes the optimal installation location. The communication unit provides communication information in emergencies. For example, the communication unit provides communication information to medical facilities and rescue points. The liaison unit can use AI to quickly provide contact information in emergencies. For example, the liaison unit can use location information to support notification to the nearest rescue service. The liaison unit's AI analyzes location information and identifies the nearest rescue service. As a result, the camp support system according to this embodiment can collect weather information, provide advice, and provide emergency contact information.

[0068] The data collection unit collects weather information. For example, it collects information such as temperature, humidity, wind speed, and precipitation. Specifically, it measures temperature using a temperature sensor, humidity using a humidity sensor, wind speed using an anemometer, and precipitation using a rain gauge. These sensors are installed at the campsite and collect data in real time. The collected data is transmitted to a central database via wireless communication. The data collection unit can use AI to collect weather information in real time. The AI ​​analyzes the data from these sensors and predicts weather changes. For example, it detects sudden changes in temperature, increases in humidity, and increases in wind speed, and predicts weather changes based on this information. The data collection unit inputs this information into the AI, which analyzes the weather information. The AI ​​refers to historical data and weather models to analyze the current weather conditions. This allows the data collection unit to grasp the weather information at the campsite in real time and respond quickly. Furthermore, the data collection unit can store this data on a cloud server and share it with other systems and departments. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The advice unit provides advice based on the information collected by the data collection unit. For example, the advice unit provides advice on optimizing tarp placement and tent location. Specifically, it provides advice on optimizing tarp and tent placement based on wind direction and rainfall. The advice unit can use AI to analyze the collected information and provide optimal advice. The AI ​​analyzes wind direction and rainfall data and proposes the optimal placement location. For example, if the wind direction is strong, it will suggest setting up the tarp in a location that blocks the wind, and if there is heavy rainfall, it will suggest setting up the tent in a well-drained location. The advice unit uses AI to analyze the collected information and propose the optimal placement location. Furthermore, the advice unit can provide more specific advice based on past data and experience. For example, it can refer to past campsite data and propose the optimal placement location under specific conditions. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the advice unit to provide users with optimal advice and improve the safety and comfort of camping.

[0070] The liaison department provides contact information in emergencies. For example, it provides contact information for medical facilities and rescue points. Specifically, it uses location information to support notification to the nearest rescue service. The liaison department can use AI to provide contact information quickly in emergencies. The AI ​​analyzes location information and identifies the nearest rescue service. For example, based on the user's current location, it identifies the nearest medical facility or rescue point and provides contact information. The liaison department notifies the user of this information to support a rapid response. Furthermore, the liaison department can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. The liaison department uses AI to analyze location information and identify the nearest rescue service. This allows the liaison department to provide contact information quickly and reliably in emergencies, ensuring user safety. Furthermore, the liaison department can collect user feedback and continuously improve the accuracy and effectiveness of the contact information. This allows the liaison department to provide contact information quickly and reliably to users and support emergency response.

[0071] It is equipped with a recording unit that provides recording functionality. The recording unit provides recording functionality. For example, the recording unit provides recording functionality to record memories during camping. The recording unit can use AI to optimize the timing and content of recordings. For example, the recording unit can adjust the recording resolution and recording time. The recording unit is equipped with storage for saving recording data. The recording unit can also save recording data to the cloud. The recording unit also has functions for organizing recording data. The recording unit provides a tagging function to classify recording data and make it easier to search. The recording unit also has functions for editing recording data. The recording unit can trim recording data and apply filters. In this way, the recording unit can provide recording functionality to record and organize memories during camping.

[0072] It includes an organization section for organizing photos and videos. The organization section organizes photos and videos. For example, the organization section provides functions for organizing photos and videos taken during camping. The organization section can use AI to optimize the organization of photos and videos. For example, the organization section provides a tagging function to classify photos and videos and make them easier to search. The organization section also has functions for editing photos and videos. The organization section can crop photos and videos and apply filters. The organization section can also save photos and videos to the cloud. The organization section also has functions for sharing photos and videos. The organization section can post photos and videos to social media or send them by email. In this way, the organization section can efficiently organize and manage photos and videos taken during camping.

[0073] It includes a creation section for creating camp diaries. The creation section creates camp diaries. For example, the creation section creates camp diaries to organize and record memories from camping trips. The creation section can optimize the creation of camp diaries using AI. The creation section provides functions to insert photos and videos into camp diaries, for example. The creation section also has a function to add text to camp diaries. The creation section can also save camp diaries to the cloud. The creation section also has a function to share camp diaries. The creation section can post camp diaries to social media or send them by email. In this way, the creation section can create camp diaries to organize and record memories from camping trips.

[0074] The data collection unit can collect weather information, wind direction, and precipitation data. For example, the unit collects information such as temperature, humidity, wind speed, and precipitation. The data collection unit can collect weather information in real time using AI. For example, the unit measures temperature using a temperature sensor and humidity using a humidity sensor. The unit measures wind speed using an anemometer and precipitation using a rain gauge. The unit inputs this information into the AI, which analyzes the weather information. As a result, the data collection unit can improve camping safety by collecting information such as weather, wind direction, and precipitation.

[0075] The advice unit can provide advice to optimize tarp setup and tent placement based on collected information. For example, the advice unit can provide advice to optimize tarp setup and tent placement. The advice unit can use AI to analyze collected information and provide optimal advice. For example, the advice unit can provide advice to optimize tarp and tent placement based on wind direction and rainfall. The advice unit uses AI to analyze collected information and propose the optimal placement location. This allows the advice unit to provide a safe and comfortable camping experience by offering advice to optimize tarp setup and tent placement.

[0076] The liaison unit can provide contact information to medical facilities and rescue points in emergencies and use location information to support reporting to the nearest rescue service. For example, the liaison unit can provide contact information to medical facilities and rescue points. The liaison unit can use AI to provide contact information quickly in emergencies. For example, the liaison unit can use location information to support reporting to the nearest rescue service. The liaison unit's AI analyzes location information to identify the nearest rescue service. This enables the liaison unit to respond quickly in emergencies and improves safety during camping.

[0077] The data collection unit can estimate the user's emotions and adjust the frequency of weather information collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can increase the frequency of weather information collection and update it in real time. If the user is relaxed, the data collection unit can decrease the frequency of weather information collection and update it only when necessary. If the user is in a hurry, the data collection unit can prioritize collecting only important weather information. In this way, the data collection unit can provide more appropriate information by adjusting the frequency of weather information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can improve the accuracy of weather information collection by referring to past weather data. For example, the data collection unit can improve the accuracy of collection by analyzing weather patterns in a specific area based on past weather data. The data collection unit can improve the accuracy of collection by predicting weather changes in a specific time period by referring to past weather data. The data collection unit can improve the accuracy of collection by analyzing the frequency of extreme weather events using past weather data. In this way, the data collection unit can improve the accuracy of weather information collection by referring to past weather data.

[0079] The data collection unit can select the types of information to collect based on the topographical information of the campsite when collecting weather information. For example, if the campsite is in a mountainous area, the data collection unit will prioritize collecting wind speed and precipitation information. If the campsite is in a coastal area, the data collection unit can prioritize collecting tidal information and wind direction information. If the campsite is on flat land, the data collection unit can prioritize collecting temperature and humidity information. In this way, the data collection unit can collect more appropriate weather information by taking into account the topographical information of the campsite.

[0080] The data collection unit can estimate the user's emotions and determine the priority of weather information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting safety-related information such as wind speed and precipitation. If the user is relaxed, the data collection unit can prioritize collecting comfort-related information such as temperature and humidity. If the user is in a hurry, the data collection unit can prioritize collecting only essential weather information. This allows the data collection unit to provide more appropriate information by prioritizing weather information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can adjust the level of detail of the information it collects based on the user's camping experience. For example, if the user is a novice camper, the unit can collect detailed weather information and provide specific advice. If the user is an experienced camper, the unit can collect only the minimum necessary weather information and provide concise advice. If the user is a seasoned camper, the unit can collect only the specific weather information the user needs. This allows the data collection unit to provide more appropriate information by adjusting the level of detail according to the user's camping experience.

[0082] The data collection unit can collect relevant weather information by analyzing users' social media activity when gathering weather information. For example, the data collection unit can collect weather information for a specific area based on campsite information shared by users on social media. The data collection unit can collect the latest weather information from camping-related accounts that users follow on social media. The data collection unit can collect relevant weather information by analyzing local weather conditions from photos and videos posted by users on social media. In this way, the data collection unit can collect relevant weather information by analyzing users' social media activity.

[0083] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is feeling anxious, the advice unit can provide advice in a reassuring way. If the user is relaxed, the advice unit can provide advice in a friendly way. If the user is in a hurry, the advice unit can provide advice in a concise and quick way. In this way, the advice unit can provide more appropriate advice by adjusting the way it expresses advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The advisory unit can adjust the level of detail of its advice based on the reliability of the collected weather information when providing advice. For example, the advisory unit can provide detailed advice based on reliable weather information. The advisory unit can provide general advice based on unreliable weather information. If the reliability of the weather information is unknown, the advisory unit can provide advice by presenting multiple scenarios. This allows the advisory unit to provide more appropriate advice by adjusting the level of detail of its advice based on the reliability of the weather information.

[0085] The advice unit can customize the content of its advice based on the topographical information of the campsite when providing advice. For example, if the campsite is in a mountainous area, the advice unit can advise on the placement of tarps and tents based on wind speed and precipitation. If the campsite is on the coast, the advice unit can advise on the placement of tarps and tents based on tidal information and wind direction. If the campsite is on flat land, the advice unit can advise on the placement of tarps and tents based on temperature and humidity. In this way, the advice unit can provide more appropriate advice by taking into account the topographical information of the campsite.

[0086] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is feeling anxious, the advice unit will prioritize safety-related advice. If the user is relaxed, the advice unit will prioritize comfort-related advice. If the user is in a hurry, the advice unit will prioritize only important advice. In this way, the advice unit can provide more appropriate advice by prioritizing advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The advice section can adjust the level of detail in its advice based on the user's camping experience. For example, for a beginner camper, it can provide detailed advice and specific steps. For an experienced camper, it can provide minimal advice and concise steps. For a seasoned camper, it can provide only the specific advice the user needs. This allows the advice section to provide more appropriate advice by adjusting the level of detail according to the user's camping experience.

[0088] The advice team can analyze a user's social media activity when providing advice and offer relevant advice. For example, the advice team can provide specific advice based on information about campsites shared by the user on social media. The advice team can provide the latest advice from camping-related accounts that the user follows on social media. The advice team can analyze local conditions from photos and videos posted by the user on social media and offer relevant advice. In this way, the advice team can provide relevant advice by analyzing the user's social media activity.

[0089] The advice team can analyze a user's social media activity when providing advice and offer relevant advice. For example, the advice team can provide specific advice based on information about campsites shared by the user on social media. The advice team can provide the latest advice from camping-related accounts that the user follows on social media. The advice team can analyze local conditions from photos and videos posted by the user on social media and offer relevant advice. In this way, the advice team can provide relevant advice by analyzing the user's social media activity.

[0090] The communication unit can estimate the user's emotions and adjust the method of emergency contact based on the estimated emotions. For example, if the user is feeling anxious, the communication unit can make an emergency contact in a quick and concise manner. If the user is relaxed, the communication unit can make an emergency contact in a manner that includes detailed information. If the user is in a hurry, the communication unit can make an emergency contact in the fastest possible manner. This allows the communication unit to respond more quickly and appropriately by adjusting the method of emergency contact according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The liaison department can improve the accuracy of emergency communications by referring to past emergency case data. For example, the liaison department can select the optimal communication method based on past emergency case data. The liaison department can select contacts capable of a quick response by referring to past emergency case data. The liaison department can optimize emergency communication procedures using past emergency case data. As a result, the accuracy of emergency communications is improved by the liaison department referring to past emergency case data.

[0092] The liaison department can select contacts based on the topographical information of the campsite in the event of an emergency. For example, if the campsite is in a mountainous area, the liaison department will contact the nearest mountain rescue team. If the campsite is on the coast, the liaison department can contact the nearest Japan Coast Guard. If the campsite is on flat land, the liaison department can contact the nearest police or fire department. This allows the liaison department to select more appropriate contacts by considering the topographical information of the campsite.

[0093] The communication department can estimate the user's emotions and prioritize emergency contacts based on those emotions. For example, if the user is feeling anxious, the communication department will prioritize selecting the most important contacts. If the user is relaxed, the communication department can select contacts that can provide detailed information. If the user is in a hurry, the communication department can prioritize contacts that can respond quickly. This allows the communication department to respond more quickly and appropriately by prioritizing emergency contacts according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The liaison department can adjust the content of emergency communications based on the user's camping experience. For example, for a novice camper, the liaison department can provide detailed information and specific steps. For an experienced camper, the liaison department can provide only the essential information and concise steps. For a seasoned camper, the liaison department can provide only the specific information the user needs. This allows the liaison department to provide more appropriate information by adjusting the content of communications according to the user's camping experience.

[0095] The liaison department can analyze a user's social media activity and provide relevant contact information in the event of an emergency. For example, the liaison department can provide specific contact information based on campsite information shared by the user on social media. The liaison department can provide the latest contact information from camp-related accounts that the user follows on social media. The liaison department can analyze local conditions from photos and videos posted by the user on social media and provide relevant contact information. In this way, the liaison department can provide relevant contact information by analyzing a user's social media activity.

[0096] The recording unit can estimate the user's emotions and adjust the recording timing based on the estimated emotions. For example, if the user is enjoying themselves, the recording unit will record particularly emotional moments. If the user is relaxed, the recording unit can record natural scenery or quiet moments. If the user is excited, the recording unit can prioritize recording active scenes. This allows the recording unit to make more appropriate recordings by adjusting the recording timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The recording unit can improve recording accuracy by referring to past recording data during recording. For example, the recording unit can prioritize recording specific scenes or moments based on past recording data. The recording unit can reflect the user's preferred recording style by referring to past recording data. The recording unit can optimize the timing and angle of recording using past recording data. As a result, the recording unit improves recording accuracy by referring to past recording data.

[0098] The recording unit can estimate the user's emotions and determine recording priorities based on those estimated emotions. For example, if the user is enjoying themselves, the recording unit will prioritize recording moments when their emotions are heightened. If the user is relaxed, the recording unit can prioritize recording natural scenery or quiet moments. If the user is excited, the recording unit can prioritize recording active scenes. This allows the recording unit to make more appropriate recordings by prioritizing recordings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The recording unit can adjust the recording content based on the user's camping experience. For example, for a beginner camper, it will record basic camping procedures and scenery. For an experienced camper, it can record specific activities or events. For a seasoned camper, it can record specific scenes or moments that the user prefers. This allows the recording unit to adjust the recording content according to the user's camping experience, resulting in more appropriate recordings.

[0100] The editing unit can estimate the user's emotions and adjust the editing method based on the estimated emotions. For example, if the user is enjoying themselves, the editing unit will highlight moments when their emotions were heightened. If the user is relaxed, the editing unit can focus on natural scenery and quiet moments. If the user is excited, the editing unit can focus on active scenes. This allows the editing unit to provide more appropriate editing by adjusting the editing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The organization unit can improve the accuracy of organization by referring to past organization data during the organization process. For example, the organization unit can prioritize organizing specific scenes or moments based on past organization data. The organization unit can reflect the user's preferred organization style by referring to past organization data. The organization unit can optimize the organization method and order using past organization data. As a result, the organization unit improves the accuracy of organization by referring to past organization data.

[0102] The sorting unit can estimate the user's emotions and determine sorting priorities based on the estimated emotions. For example, if the user is enjoying themselves, the sorting unit will prioritize sorting moments when their emotions are heightened. If the user is relaxed, the sorting unit can prioritize sorting natural scenery and quiet moments. If the user is excited, the sorting unit can prioritize sorting active scenes. This allows the sorting unit to sort more appropriately by determining sorting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The organization function can adjust its content based on the user's camping experience. For example, for a beginner camper, it will organize content focusing on basic camping procedures and scenery. For an experienced camper, it can organize content focusing on specific activities and events. For a seasoned camper, it can organize content focusing on specific scenes and moments that the user likes. This allows the organization function to adjust its content according to the user's camping experience, resulting in more appropriate organization.

[0104] The creation unit can estimate the user's emotions and adjust the diary creation method based on the estimated emotions. For example, if the user is having fun, the creation unit will highlight moments of heightened emotion in the diary. If the user is relaxed, the creation unit can focus on recording natural scenery and quiet moments in the diary. If the user is excited, the creation unit can focus on recording active scenes in the diary. In this way, the creation unit can create a more appropriate diary by adjusting the diary creation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The creation unit can improve the accuracy of diary creation by referring to past diary data. For example, the creation unit can prioritize recording specific scenes or moments based on past diary data. The creation unit can reflect the user's preferred diary style by referring to past diary data. The creation unit can optimize the method and order of diary creation using past diary data. As a result, the accuracy of diary creation is improved by referring to past diary data.

[0106] The creation unit can estimate the user's emotions and determine the priority of diary creation based on the estimated emotions. For example, if the user is having fun, the creation unit will prioritize recording moments when emotions are heightened. If the user is relaxed, the creation unit can prioritize recording natural scenery or quiet moments. If the user is excited, the creation unit can prioritize recording active scenes. In this way, the creation unit can create more appropriate diaries by determining the priority of diary creation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The creation function can adjust the content of the diary based on the user's camping experience. For example, for a beginner camper, the creation function will focus on recording basic camping procedures and scenery. For an experienced camper, the creation function can focus on recording specific activities and events. For a seasoned camper, the creation function can focus on recording specific scenes and moments that the user likes. In this way, the creation function can adjust the content according to the user's camping experience, enabling the creation of a more appropriate diary.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The camping support system can also include a health management unit that monitors the user's health status. This unit can, for example, measure vital signs such as heart rate, body temperature, and blood pressure in real time, and issue alerts if abnormalities are detected. Based on the user's health status, the health management unit can also provide advice on appropriate rest times and hydration. This helps reduce health risks during camping and supports a safe and comfortable camping experience.

[0110] The camping support system can also include a music provider that estimates the user's emotions and selects music based on those emotions. For example, the music provider could provide calming music when the user is relaxed and upbeat music when the user is excited. The music provider could also adjust the volume and playlist according to the user's emotions. This would provide a music experience tailored to the user's emotions, further enhancing the enjoyment of camping.

[0111] The camping support system can also include a meal management unit to manage the user's meals. For example, the meal management unit can suggest suitable meal menus during the camping trip based on the user's food preferences and allergy information. It can also recommend meals containing necessary nutrients based on the user's activity level and health condition. This makes meal management during camping easier, allowing users to enjoy healthy and balanced meals.

[0112] The camping support system can also include a lighting management unit that estimates the user's emotions and adjusts the lighting based on those emotions. For example, the lighting management unit provides warm-colored lighting when the user is relaxed and cool-colored lighting when the user is concentrating. The lighting management unit can also adjust the brightness and color temperature of the lighting according to the user's emotions. This provides a comfortable lighting environment tailored to the user's emotions, further enhancing the camping atmosphere.

[0113] The camping support system can also be equipped with a navigation unit to assist user movement. For example, the navigation unit provides map information about the area around the campsite, guiding users to their destination safely. The navigation unit can also reflect real-time weather and terrain information to suggest the optimal route. This allows users to move around the campsite without getting lost, ensuring a safe and efficient camping experience.

[0114] The camping support system can also include a communication unit that estimates the user's emotions and supports communication based on those estimated emotions. For example, the communication unit could provide encouraging messages if the user is feeling anxious, or empathetic messages if the user is enjoying themselves. The communication unit could also send messages at the appropriate time according to the user's emotions. This would support communication that is sensitive to the user's feelings and further enhance the enjoyment of camping.

[0115] The camping support system can also include a sleep management unit to manage the user's sleep. For example, the sleep management unit can monitor the user's sleep patterns and provide an optimal sleep environment. To improve the user's sleep quality, the sleep management unit can set alarms at appropriate times and play relaxing music. This allows users to ensure high-quality sleep even while camping, preparing them for the next day's activities.

[0116] The camping support system can also include an entertainment unit that estimates the user's emotions and provides entertainment based on those emotions. For example, the entertainment unit might provide a quiet movie if the user is relaxed, or an action movie if the user is excited. The entertainment unit can also select appropriate entertainment content according to the user's emotions. This allows for an entertainment experience tailored to the user's emotions, further enhancing the enjoyment of camping.

[0117] The camping support system can also include a luggage management unit to manage the user's belongings. For example, the luggage management unit can create a list of the user's belongings and manage necessary items in a checklist format. The luggage management unit can also set reminders to prevent users from forgetting items and suggest optimal packing methods by weighing the luggage. This makes luggage management easier for users, ensuring a smooth camping preparation process.

[0118] The camping support system can also include an activity suggestion unit that estimates the user's emotions and proposes activities based on those emotions. For example, the activity suggestion unit might suggest walking or reading if the user is relaxed, or hiking or kayaking if the user is excited. The activity suggestion unit can also select an appropriate activity according to the user's emotions. This allows for an activity experience tailored to the user's emotions, further enhancing the enjoyment of camping.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects weather information. The data collection unit collects information such as temperature, humidity, wind speed, and precipitation. The data collection unit can collect weather information in real time using AI. For example, the data collection unit measures temperature using a temperature sensor and humidity using a humidity sensor. The data collection unit measures wind speed using an anemometer and precipitation using a rain gauge. The data collection unit inputs this information into the AI, which then analyzes the weather information. Step 2: The advice unit provides advice based on the information collected by the data collection unit. For example, the advice unit provides advice to optimize tarp setup and tent placement. The advice unit can use AI to analyze the collected information and provide optimal advice. For example, the advice unit provides advice to optimize tarp and tent setup locations based on wind direction and rainfall. The advice unit uses AI to analyze the collected information and propose the optimal setup location. Step 3: The liaison unit provides contact information in emergencies. The liaison unit provides contact information, for example, to medical facilities and rescue points. The liaison unit can use AI to quickly provide contact information in emergencies. For example, the liaison unit can use location information to support notification to the nearest rescue service. The liaison unit's AI analyzes location information and identifies the nearest rescue service.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the collection unit, advice unit, communication unit, recording unit, organization unit, and creation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects weather information using the sensors of the smart device 14 and analyzes it using the identification processing unit 290 of the data processing unit 12. The advice unit provides optimal advice based on the collected information using the control unit 46A of the smart device 14. The communication unit provides communication information via the communication I / F 44 of the smart device 14 in case of emergency. The recording unit records using the camera 42 of the smart device 14 and saves it to the storage 32 of the data processing unit 12. The organization unit classifies the recorded data and provides a tagging function to make it easier to search. The creation unit provides a function to create a camp diary and save it to the cloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, advice unit, communication unit, recording unit, organization unit, and creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects weather information using the sensors of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The advice unit provides optimal advice based on the collected information using the control unit 46A of the smart glasses 214. The communication unit provides communication information via the communication I / F 44 of the smart glasses 214 in case of emergency. The recording unit records using the camera 42 of the smart glasses 214 and saves it to the storage 32 of the data processing unit 12. The organization unit classifies the recorded data and provides a tagging function to make it easier to search. The creation unit provides a function to create a camp diary and save it to the cloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, advice unit, communication unit, recording unit, organization unit, and creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects weather information using the sensors of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The advice unit provides optimal advice based on the collected information using the control unit 46A of the headset terminal 314. The communication unit provides communication information via the communication I / F 44 of the headset terminal 314 in emergencies. The recording unit records using the camera 42 of the headset terminal 314 and saves it to the storage 32 of the data processing unit 12. The organization unit classifies the recorded data and provides a tagging function to make it easier to search. The creation unit provides a function to create a camp diary and save it to the cloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, advice unit, communication unit, recording unit, organization unit, and creation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects weather information using the robot 414's sensors and analyzes it using the identification unit 290 of the data processing unit 12. The advice unit provides optimal advice based on the collected information using the control unit 46A of the robot 414. The communication unit provides communication information via the robot 414's communication I / F 44 in emergencies. The recording unit records using the robot 414's camera 42 and saves it to the storage 32 of the data processing unit 12. The organization unit classifies the recorded data and provides a tagging function to make it easier to search. The creation unit provides a function to create a camp diary and save it to the cloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A collection unit that collects weather information, An advice unit provides advice based on the information collected by the aforementioned collection unit, A liaison department that provides contact information in emergencies, and A system characterized by the following features. (Note 2) It is equipped with a recording unit that provides recording functionality. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has an organizing section for photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a section for creating a camping diary. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We collect weather information, wind direction, and precipitation data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, We provide advice on optimizing tarp setup and tent placement based on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned liaison department, In emergencies, it provides contact information for medical facilities and rescue points, and uses location information to support notifications to the nearest rescue service. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather information collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting weather information, we improve the accuracy of the collection by referring to past weather data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting weather information, select the type of information to collect based on the topographical information of the campsite. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of weather information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting weather information, adjust the level of detail of the information collected based on the user's camping experience. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting weather information, we analyze users' social media activity and collect relevant weather information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, adjust the level of detail based on the reliability of the collected weather information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, customize the advice based on the terrain information of the campsite. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, adjust the level of detail based on the user's camping experience. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned liaison department, The system estimates the user's emotions and adjusts the method of emergency contact based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned liaison department, When making emergency contacts, we improve the accuracy of communication by referring to past emergency case data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned liaison department, In case of emergency contact, select contact persons based on the topographical information of the campsite. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned liaison department, It estimates the user's emotions and prioritizes emergency contacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned liaison department, During emergency contact, the content of the message will be adjusted based on the user's camping experience. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned liaison department, In case of emergency contact, the system analyzes the user's social media activity and provides relevant contact information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recording unit is It estimates the user's emotions and adjusts the recording timing based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned recording unit is During recording, past recording data is referenced to improve recording accuracy. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned recording unit is It estimates the user's emotions and determines the priority of recordings based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned recording unit is During recording, the recording content is adjusted based on the user's camping experience. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned editing unit, It estimates the user's emotions and adjusts the sorting method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned editing unit, When organizing data, refer to past organizing data to improve the accuracy of the organization process. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned editing unit, It estimates the user's emotions and determines the priority of sorting based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned editing unit, During organization, adjust the organization based on the user's camping experience. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned creation unit, It estimates the user's emotions and adjusts the diary creation method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned creation unit, When creating a diary, refer to past diary data to improve the accuracy of the creation process. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned creation unit, It estimates the user's emotions and determines the priority of diary creation based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned creation unit, When creating a diary, adjust the content based on the user's camping experience. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects weather information, An advice unit provides advice based on the information collected by the aforementioned collection unit, A liaison department that provides contact information in emergencies, and A system characterized by the following features.

2. It is equipped with a recording unit that provides recording functionality. The system according to feature 1.

3. It has an organizing section for photos and videos. The system according to feature 1.

4. It includes a section for creating a camping diary. The system according to feature 1.

5. The aforementioned collection unit is We collect weather information, wind direction, and precipitation data. The system according to feature 1.

6. The aforementioned advice section, We provide advice on optimizing tarp setup and tent placement based on the collected information. The system according to feature 1.

7. The aforementioned liaison department, In emergencies, it provides contact information for medical facilities and rescue points, and uses location information to support notifications to the nearest rescue service. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather information collection based on those emotions. The system according to feature 1.

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