System

Abstract

An object of the system according to the embodiment is to make it easy to record a route during mountain climbing or hiking, specify a place where animals and plants are captured, and grasp a real-time position of a group member.SOLUTION: A system includes a route recording part, an image authentication part, and a position grasping part. The route recording unit records a route along which the user has walked, a walking time, a captured photo (and a capturing location thereof), a comment, and a recording memo as a log. The image authentication unit authenticates the image of the animal or plant recorded by the route recording unit. The location grasping unit grasps the real-time locations of the group members recorded by the route recording unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately record routes during mountain climbing or hiking, identify locations to photograph plants and animals, or track the real-time locations of group members, so there is room for improvement.

[0005] The system according to the embodiment aims to make it easy to record routes during mountain climbing or hiking, identify locations to photograph plants and animals, and grasp the real-time locations of group members. [Means for solving the problem]

[0006] The system according to the embodiment includes a route recording unit, an image authentication unit, and a location tracking unit. The route recording unit records the route walked by the user, walking time, photos taken (and their locations), comments, and recorded notes as a log. The image authentication unit authenticates images of plants and animals recorded by the route recording unit. The location tracking unit tracks the real-time locations of group members recorded by the route recording unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate recording routes during mountain climbing or hiking, identifying locations for photographing plants and animals, and tracking the real-time locations of group members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The mountain climbing / hiking log app according to the embodiment of the present invention is a system that allows a user to log the route they walked, the walking time, the photos they took (and the locations where they were taken), comments, and memos, and share these with other users. This allows the mountain climbing / hiking log app to allow a user to keep detailed records of their mountain climbing or hiking experiences and share them with other users.

[0029] A mountain climbing / hiking log app according to an embodiment includes a route recording unit, an image authentication unit, and a location tracking unit. The route recording unit records the user's walking route, walking time, photos taken (and their locations), comments, and record notes as a log. For example, the route recording unit records the user's walking route using GPS data. The route recording unit can also measure walking time and record start and end times. The route recording unit can also save photos taken in JPEG format and record the date and time of the photo and location information as metadata. The route recording unit can also save comments and record notes using text input or voice input. The image authentication unit authenticates images of plants and animals recorded by the route recording unit. For example, the image authentication unit can recognize the type of plant or animal using a machine learning algorithm. The image authentication unit can also analyze multiple images and extract characteristics of plants and animals to improve authentication accuracy. The image authentication unit can add authentication results to a log and share it with other users. The location tracking unit tracks the real-time locations of group members recorded by the route recording unit. For example, the location determination unit acquires the locations of group members in real time using GPS data. The location determination unit can display the acquired location information on the app to visualize the locations of group members. The location determination unit can also set the update frequency of the location information and update the location information in real time. This allows the mountain climbing / hiking log app according to the embodiment to keep detailed records of a user's mountain climbing or hiking, enable image recognition of animals and plants, and locate group members. For example, a user can share their mountain climbing or hiking records with other users, which can be useful for planning mountain climbing or considering mountains they want to climb. Furthermore, the image recognition function for animals and plants can be used to make it easier to find specific animals and plants, reducing the risk of encountering dangerous animals. Furthermore, the real-time location determination function for group members can be used to reduce the risk of getting separated during mountain climbing.

[0030] The route recording unit can automatically analyze the content of a photo and automatically add information about the weather, terrain, and types of flora and fauna. For example, the route recording unit uses generative AI to analyze the content of a photograph and automatically add weather information. For example, it estimates the day's weather from the color of the sky and the shape of the clouds in the photograph and records it in a log. The route recording unit also analyzes the terrain of a photograph and automatically adds information such as mountain height and slope angle. For example, it estimates the mountain's elevation and terrain characteristics from the shape of the mountain in the photograph. The route recording unit also automatically recognizes the types of flora and fauna photographed and adds that information to the log. For example, it identifies the type of flower or bird in the photograph and records its name and characteristics in the log. This automatically adds information related to the photograph, providing a detailed record.

[0031] The route recording unit can analyze the user's walking pattern, estimate the user's fatigue level and physical strength, and suggest appropriate rest points. The route recording unit, for example, analyzes the user's walking speed and stride length to estimate the user's fatigue level. For example, if the user's walking speed suddenly drops, it determines that fatigue is accumulating and suggests a break. The route recording unit also monitors the user's heart rate and breathing rate to estimate the user's physical strength. For example, if the heart rate rises above a certain level, it determines that the user is exhausted and suggests a break. The route recording unit also analyzes the user's walking distance and altitude changes to suggest appropriate rest points. For example, it suggests a break after walking a certain distance or after climbing a steep slope. In this way, the system supports safe mountain climbing by analyzing the user's fatigue level and physical strength and suggesting appropriate rest points.

[0032] The route recording unit can display the average time and difficulty of other users walking the same route based on the log data and provide it as reference information. The route recording unit, for example, analyzes the log data and displays the average time it took other users to walk the same route. For example, it calculates the average time required for a specific route based on past data and provides it as reference information. The route recording unit also evaluates the difficulty of other users walking the same route based on the log data. For example, it analyzes walking speed and altitude changes and displays the route difficulty in categories such as "easy," "normal," and "difficult." The route recording unit also displays the popularity of a specific route based on the log data of other users. For example, it tallys the number of users who walked the same route and displays popular routes in a ranking format. In this way, by displaying the average time and difficulty based on the data of other users, reference information for mountain climbing plans is provided.

[0033] The route recording unit displays the log data on a 3D map and provides visual information, thereby recreating a realistic experience. For example, the route recording unit displays the log data on a 3D map to provide three-dimensional visual information. For example, the route recorded by the user may be reproduced on the 3D map, visually displaying the terrain and elevation changes. The route recording unit also places photographs taken on the 3D map to provide three-dimensional visual information. For example, the location where a photograph was taken may be marked on the 3D map so that the photograph can be displayed by clicking on it. The route recording unit also animates the user's walking route on the 3D map to re-create a realistic experience. For example, the route recorded by the user may be played back as an animation, visually recreating the walking state. In this way, by displaying the log data on the 3D map, three-dimensional visual information may be provided, recreating a realistic experience.

[0034] The image recognition unit can analyze images of plants and animals and automatically group similar plants and animals. The image recognition unit implements a function that uses, for example, generative AI to analyze images of plants and animals and automatically group similar plants and animals. For example, it groups and displays the same types of flowers and birds. The image recognition unit also analyzes images of plants and animals and automatically groups plants and animals with similar characteristics. For example, it displays plants and animals with similar colors or shapes together. The image recognition unit also implements a function that uses generative AI to analyze images of plants and animals and group them based on academic classification. For example, it classifies and displays plants and animals by family or genus. This makes it easier to organize and search information by analyzing images of plants and animals and grouping similar plants and animals.

[0035] The image authentication unit allows the generation AI to automatically provide related academic information and conservation information for images of plants and animals taken by the user. For example, the image authentication unit allows the generation AI to automatically provide related academic information for images of plants and animals taken by the user. For example, it displays the scientific name and ecological information of the plants and animals. The image authentication unit also uses the generation AI to provide conservation information for images of plants and animals taken by the user. For example, it displays information about endangered species and protected plants and animals. The image authentication unit also allows the generation AI to automatically provide related academic papers and research results for images of plants and animals taken by the user. For example, it displays the latest research information on plants and animals. In this way, the user's knowledge is deepened by automatically providing academic information and conservation information related to images of plants and animals.

[0036] The location understanding unit can analyze the location information of group members and suggest optimal meeting points and rest points. The location understanding unit, for example, uses a generation AI to analyze the location information of group members and suggest the optimal meeting point. For example, it automatically calculates and suggests a midpoint that is easy for everyone to meet up. The location understanding unit also suggests rest points based on the location information of group members. For example, it selects and suggests appropriate rest locations taking into account the locations of everyone. The location understanding unit also uses a generation AI to analyze the location information of group members in real time and dynamically suggest optimal meeting points and rest points. For example, it updates the suggestions according to the movement status of members. In this way, by analyzing the location information of group members and suggesting optimal meeting points and rest points, the safety and efficiency of the entire group is improved.

[0037] The location grasping unit can analyze the physical strength and pace of each group member based on the location information of the group members and provide appropriate advice. For example, the location grasping unit can analyze the physical strength and pace of each group member based on the location information of the group members and provide appropriate advice. For example, it can suggest to a member who is lagging behind that they should pick up the pace. The location grasping unit can also use a generation AI to analyze the location information of group members and evaluate the level of physical exhaustion. For example, it can suggest to a member who is accumulating fatigue that they should take a break. The location grasping unit can also analyze the pace of each group member based on the location information of the group members and provide advice to adjust the overall pace. For example, it can suggest a pace that allows everyone to proceed at a comfortable pace. In this way, by analyzing the physical strength and pace of group members and providing appropriate advice, the safety and efficiency of the entire group can be improved.

[0038] The location understanding unit can share the location information of group members with other groups, promoting cooperation and interaction between multiple groups. For example, the location understanding unit can share the location information of group members with other groups to promote cooperation and interaction between multiple groups. For example, a function for working together with other nearby groups can be implemented. The location understanding unit can also use generative AI to analyze the location information of group members and suggest points for cooperation with other groups. For example, it can suggest points where groups walking the same route can meet up. The location understanding unit can also share the location information of group members with other groups, providing a platform for promoting interaction between multiple groups. For example, a function can be implemented that allows messages to be exchanged between groups. This can promote cooperation and interaction between multiple groups by sharing the location information of group members with other groups.

[0039] The location understanding unit can enhance real-time photo sharing and message sending functions based on the location information of group members. The location understanding unit, for example, enhances the real-time photo sharing function based on the location information of group members. For example, it implements a function that automatically shares photos taken at a specific location with everyone in the group. The location understanding unit also enhances the real-time message sending function based on the location information of group members. For example, it implements a function that sends messages based on the current location. The location understanding unit also uses a generation AI to analyze the location information of group members and enhances the real-time photo sharing and message sending functions. For example, it implements a function that automatically sends photos and messages based on the location information. This enhances the real-time photo sharing and message sending functions based on the location information of group members, thereby facilitating communication.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The route recording unit can analyze the user's walking pattern, estimate their fatigue level and physical strength, and suggest appropriate rest points. For example, it can analyze the user's walking speed and stride length to estimate their fatigue level. If their walking speed suddenly drops, it determines that fatigue is accumulating and suggests a break. The route recording unit also monitors the user's heart rate and breathing rate to estimate their physical strength. If their heart rate rises above a certain level, it determines that their physical strength is depleted and suggests a break. The route recording unit also analyzes the user's walking distance and altitude changes to suggest appropriate rest points. It suggests a break after walking a certain distance or after overcoming a steep uphill slope. In this way, the system supports safe mountain climbing by analyzing the user's fatigue level and physical strength and suggesting appropriate rest points.

[0042] The route recording unit can automatically analyze the contents of a photo and automatically add information about the weather, terrain, and types of flora and fauna. For example, it uses generative AI to analyze the contents of a photograph and automatically add weather information. It estimates the day's weather from the color of the sky and the shape of the clouds in the photograph and records it in the log. The route recording unit also analyzes the terrain in the photograph and automatically adds information such as mountain height and slope angle. It estimates the mountain's elevation and terrain characteristics from the shape of the mountain in the photograph. The route recording unit also automatically recognizes the types of flora and fauna photographed and adds that information to the log. It identifies the types of flowers and birds in the photograph and records their names and characteristics in the log. This automatically adds information related to the photo, providing a detailed record.

[0043] The route recording unit can display the average time and difficulty when other users have walked the same route based on the log data and provide it as reference information. For example, it analyzes the log data and displays the average time when other users have walked the same route. It calculates the average time required for a specific route based on past data and provides it as reference information. It also evaluates the difficulty when other users have walked the same route based on the log data. It analyzes walking speed and altitude changes and displays the route difficulty in categories such as "easy," "normal," and "difficult." It also displays the popularity of a specific route based on the log data of other users. It tallys the number of users who have walked the same route and displays popular routes in a ranking format. In this way, by displaying the average time and difficulty based on the data of other users, it provides reference information for mountain climbing plans.

[0044] The route recording unit displays the log data on a 3D map and provides visual information, thereby recreating a realistic experience. For example, the log data can be displayed on a 3D map to provide three-dimensional visual information. The route walked by the user can be reproduced on the 3D map, visually displaying the terrain and elevation changes. Photographs taken can also be placed on the 3D map to provide three-dimensional visual information. The location where a photograph was taken can be marked on the 3D map so that the photograph can be displayed by clicking on it. Animated versions of the user's walking route can also be displayed on the 3D map to recreate a realistic experience. Animated versions of the route walked by the user can be played back to visually recreate the walking experience. In this way, displaying the log data on a 3D map provides three-dimensional visual information and recreates a realistic experience.

[0045] The image recognition unit can analyze images of plants and animals and automatically group similar plants and animals. For example, generative AI can be used to implement a function that analyzes images of plants and animals and automatically groups similar plants and animals. Flowers and birds of the same species are grouped and displayed. Images of plants and animals are also analyzed to automatically group plants and animals with similar characteristics. Plants and animals with similar colors and shapes are displayed together. Generative AI can also be used to implement a function that analyzes images of plants and animals and group them based on academic classification. Plants and animals are categorized and displayed by family and genus. This makes it easier to organize and search information by analyzing images of plants and animals and grouping similar plants and animals.

[0046] The image authentication unit allows the generation AI to automatically provide relevant academic information and conservation information for images of plants and animals taken by the user. For example, the generation AI automatically provides relevant academic information for images of plants and animals taken by the user. It displays the scientific names and ecological information of plants and animals. The generation AI also provides conservation information for images of plants and animals taken by the user. It displays information on endangered species and protected plants and animals. The generation AI also automatically provides relevant academic papers and research results for images of plants and animals taken by the user. It displays the latest research information on plants and animals. This deepens the user's knowledge by automatically providing academic information and conservation information related to images of plants and animals.

[0047] The location understanding unit can analyze the location information of group members and suggest optimal meeting points and rest points. For example, using generation AI, the location information of group members is analyzed and the optimal meeting point is suggested. A midpoint that is easy for everyone to meet up is automatically calculated and suggested. Rest points are also suggested based on the location information of group members. Appropriate rest locations are selected and suggested taking into account the locations of everyone. Furthermore, generation AI is used to analyze the location information of group members in real time and dynamically suggest optimal meeting points and rest points. Suggestions are updated according to the movement status of members. In this way, by analyzing the location information of group members and suggesting optimal meeting points and rest points, the safety and efficiency of the entire group is improved.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The route recording unit records the user's walking route, walking time, photos taken (and their locations), comments, and record notes as a log. For example, the route recording unit uses GPS data to record the user's walking route. The route recording unit can also measure walking time and record the start and end times. The route recording unit can also save the photos taken in JPEG format and record the date and time of the photo and location information as metadata. Furthermore, the route recording unit can save comments and record notes using text input or voice input. Step 2: The image authentication unit authenticates the images of plants and animals recorded by the route recording unit. For example, the image authentication unit may use a machine learning algorithm to recognize the type of plant or animal. The image authentication unit may also analyze multiple images and extract characteristics of plants and animals to improve authentication accuracy. The image authentication unit may also add the authentication results to a log and share them with other users. Step 3: The location determination unit determines the real-time locations of the group members recorded by the route recording unit. For example, the location determination unit obtains the locations of the group members in real time using GPS data. The location determination unit can also display the obtained location information on the app to visualize the locations of the group members. The location determination unit can also set the update frequency of the location information and update the location information in real time.

[0050] (Example 2) The mountain climbing / hiking log app according to the embodiment of the present invention is a system that allows a user to log the route they walked, the walking time, the photos they took (and the locations where they were taken), comments, and memos, and share these with other users. This allows the mountain climbing / hiking log app to allow a user to keep detailed records of their mountain climbing or hiking experiences and share them with other users.

[0051] A mountain climbing / hiking log app according to an embodiment includes a route recording unit, an image authentication unit, and a location tracking unit. The route recording unit records the user's walking route, walking time, photos taken (and their locations), comments, and record notes as a log. For example, the route recording unit records the user's walking route using GPS data. The route recording unit can also measure walking time and record start and end times. The route recording unit can also save photos taken in JPEG format and record the date and time of the photo and location information as metadata. The route recording unit can also save comments and record notes using text input or voice input. The image authentication unit authenticates images of plants and animals recorded by the route recording unit. For example, the image authentication unit can recognize the type of plant or animal using a machine learning algorithm. The image authentication unit can also analyze multiple images and extract characteristics of plants and animals to improve authentication accuracy. The image authentication unit can add authentication results to a log and share it with other users. The location tracking unit tracks the real-time locations of group members recorded by the route recording unit. For example, the location determination unit acquires the locations of group members in real time using GPS data. The location determination unit can display the acquired location information on the app to visualize the locations of group members. The location determination unit can also set the update frequency of the location information and update the location information in real time. This allows the mountain climbing / hiking log app according to the embodiment to keep detailed records of a user's mountain climbing or hiking, enable image recognition of animals and plants, and locate group members. For example, a user can share their mountain climbing or hiking records with other users, which can be useful for planning mountain climbing or considering mountains they want to climb. Furthermore, the image recognition function for animals and plants can be used to make it easier to find specific animals and plants, reducing the risk of encountering dangerous animals. Furthermore, the real-time location determination function for group members can be used to reduce the risk of getting separated during mountain climbing.

[0052] The route recording unit can automatically analyze the content of a photo and automatically add information about the weather, terrain, and types of flora and fauna. For example, the route recording unit uses generative AI to analyze the content of a photograph and automatically add weather information. For example, it estimates the day's weather from the color of the sky and the shape of the clouds in the photograph and records it in a log. The route recording unit also analyzes the terrain of a photograph and automatically adds information such as mountain height and slope angle. For example, it estimates the mountain's elevation and terrain characteristics from the shape of the mountain in the photograph. The route recording unit also automatically recognizes the types of flora and fauna photographed and adds that information to the log. For example, it identifies the type of flower or bird in the photograph and records its name and characteristics in the log. This automatically adds information related to the photograph, providing a detailed record.

[0053] The route recording unit can analyze the user's walking pattern, estimate the user's fatigue level and physical strength, and suggest appropriate rest points. The route recording unit, for example, analyzes the user's walking speed and stride length to estimate the user's fatigue level. For example, if the user's walking speed suddenly drops, it determines that fatigue is accumulating and suggests a break. The route recording unit also monitors the user's heart rate and breathing rate to estimate the user's physical strength. For example, if the heart rate rises above a certain level, it determines that the user is exhausted and suggests a break. The route recording unit also analyzes the user's walking distance and altitude changes to suggest appropriate rest points. For example, it suggests a break after walking a certain distance or after climbing a steep slope. In this way, the system supports safe mountain climbing by analyzing the user's fatigue level and physical strength and suggesting appropriate rest points.

[0054] The route recording unit can use the emotion estimation function to analyze emotions from the user's comments and recorded notes and implement a highlighting function that emphasizes positive experiences. For example, the route recording unit analyzes emotions from the user's comments and recorded notes and implements a highlighting function that emphasizes positive experiences. For example, positive expressions such as "It was fun" and "Beautiful scenery" are extracted and displayed as highlights. The route recording unit also uses the emotion estimation function to detect negative emotions from the user's comments and recorded notes and display them in a less conspicuous manner. For example, negative expressions such as "I was tired" and "It was dangerous" are suppressed. The route recording unit also analyzes the user's emotions and adds visual effects to emphasize positive experiences. For example, bright colors and icons are displayed for positive comments. This enhances the enjoyment of mountain climbing by emphasizing the user's positive experiences.

[0055] The route recording unit can display the average time and difficulty of other users walking the same route based on the log data and provide it as reference information. The route recording unit, for example, analyzes the log data and displays the average time it took other users to walk the same route. For example, it calculates the average time required for a specific route based on past data and provides it as reference information. The route recording unit also evaluates the difficulty of other users walking the same route based on the log data. For example, it analyzes walking speed and altitude changes and displays the route difficulty in categories such as "easy," "normal," and "difficult." The route recording unit also displays the popularity of a specific route based on the log data of other users. For example, it tallys the number of users who walked the same route and displays popular routes in a ranking format. In this way, by displaying the average time and difficulty based on the data of other users, reference information for mountain climbing plans is provided.

[0056] The route recording unit displays the log data on a 3D map and provides visual information, thereby recreating a realistic experience. For example, the route recording unit displays the log data on a 3D map to provide three-dimensional visual information. For example, the route recorded by the user may be reproduced on the 3D map, visually displaying the terrain and elevation changes. The route recording unit also places photographs taken on the 3D map to provide three-dimensional visual information. For example, the location where a photograph was taken may be marked on the 3D map so that the photograph can be displayed by clicking on it. The route recording unit also animates the user's walking route on the 3D map to re-create a realistic experience. For example, the route recorded by the user may be played back as an animation, visually recreating the walking state. In this way, by displaying the log data on the 3D map, three-dimensional visual information may be provided, recreating a realistic experience.

[0057] The route recording unit can use the emotion estimation function to display the emotions felt by the user at a specific location on a map, allowing other users to predict the experience they will have at that location. For example, the route recording unit can use the emotion estimation function to analyze the emotions felt by the user at a specific location and display them on a map. For example, emotions such as "fun" or "fear" can be displayed as icons on the map. The route recording unit also implements a function to predict the experience at a specific location based on the user's emotion data. For example, it can display emotional trends at a specific location based on past data, allowing other users to predict the experience they will have at that location. The route recording unit can also use the emotion estimation function to highlight the emotions felt by the user at a specific location, allowing other users to refer to their experiences at that location. For example, it can prominently display locations with a high proportion of positive emotions. In this way, by displaying the user's emotions on a map, it can predict the experience other users will have at that location.

[0058] The image recognition unit can analyze images of plants and animals and automatically group similar plants and animals. The image recognition unit implements a function that uses, for example, generative AI to analyze images of plants and animals and automatically group similar plants and animals. For example, it groups and displays the same types of flowers and birds. The image recognition unit also analyzes images of plants and animals and automatically groups plants and animals with similar characteristics. For example, it displays plants and animals with similar colors or shapes together. The image recognition unit also implements a function that uses generative AI to analyze images of plants and animals and group them based on academic classification. For example, it classifies and displays plants and animals by family or genus. This makes it easier to organize and search information by analyzing images of plants and animals and grouping similar plants and animals.

[0059] The image authentication unit allows the generation AI to automatically provide related academic information and conservation information for images of plants and animals taken by the user. For example, the image authentication unit allows the generation AI to automatically provide related academic information for images of plants and animals taken by the user. For example, it displays the scientific name and ecological information of the plants and animals. The image authentication unit also uses the generation AI to provide conservation information for images of plants and animals taken by the user. For example, it displays information about endangered species and protected plants and animals. The image authentication unit also allows the generation AI to automatically provide related academic papers and research results for images of plants and animals taken by the user. For example, it displays the latest research information on plants and animals. In this way, the user's knowledge is deepened by automatically providing academic information and conservation information related to images of plants and animals.

[0060] The image authentication unit can use the emotion estimation function to analyze the emotion a user felt toward a specific animal or plant and automatically generate an introductory text for the animal or plant based on that emotion. For example, the image authentication unit can use the emotion estimation function to analyze the emotion a user felt toward a specific animal or plant and automatically generate an introductory text for the animal or plant based on that emotion. For example, an introductory text reflecting emotions such as "beautiful" or "rare" can be generated. The image authentication unit can also analyze the emotion a user felt toward a specific animal or plant based on the user's emotion data and automatically generate an introductory text based on that emotion. For example, an introductory text emphasizing positive emotions can be generated. The image authentication unit can also use the emotion estimation function to analyze the emotion a user felt toward a specific animal or plant and introduce the characteristics of the animal or plant based on that emotion. For example, an introductory text reflecting emotions such as "moving scenery" or "soothing flowers" can be generated. This makes it possible to provide more emotional information by automatically generating introductory texts for animals and plants based on the user's emotions.

[0061] The location understanding unit can analyze the location information of group members and suggest optimal meeting points and rest points. The location understanding unit, for example, uses a generation AI to analyze the location information of group members and suggest the optimal meeting point. For example, it automatically calculates and suggests a midpoint that is easy for everyone to meet up. The location understanding unit also suggests rest points based on the location information of group members. For example, it selects and suggests appropriate rest locations taking into account the locations of everyone. The location understanding unit also uses a generation AI to analyze the location information of group members in real time and dynamically suggest optimal meeting points and rest points. For example, it updates the suggestions according to the movement status of members. In this way, by analyzing the location information of group members and suggesting optimal meeting points and rest points, the safety and efficiency of the entire group is improved.

[0062] The location grasping unit can analyze the physical strength and pace of each group member based on the location information of the group members and provide appropriate advice. For example, the location grasping unit can analyze the physical strength and pace of each group member based on the location information of the group members and provide appropriate advice. For example, it can suggest to a member who is lagging behind that they should pick up the pace. The location grasping unit can also use a generation AI to analyze the location information of group members and evaluate the level of physical exhaustion. For example, it can suggest to a member who is accumulating fatigue that they should take a break. The location grasping unit can also analyze the pace of each group member based on the location information of the group members and provide advice to adjust the overall pace. For example, it can suggest a pace that allows everyone to proceed at a comfortable pace. In this way, by analyzing the physical strength and pace of group members and providing appropriate advice, the safety and efficiency of the entire group can be improved.

[0063] The location grasping unit can use the emotion estimation function to analyze the emotional states of group members and make suggestions to reduce stress and anxiety. For example, the location grasping unit uses the emotion estimation function to analyze the emotional states of group members and make suggestions to reduce stress and anxiety. For example, it displays an encouraging message to a member who is feeling anxious. The location grasping unit also makes specific suggestions to reduce stress and anxiety based on the emotion data of group members. For example, it suggests a place to rest where they can relax. The location grasping unit also uses the emotion estimation function to analyze the emotional states of group members in real time and suggest actions to reduce stress and anxiety. For example, it suggests activities for a change of mood. In this way, by analyzing the emotional states of group members and making suggestions to reduce stress and anxiety, the safety and comfort of the entire group is improved.

[0064] The location understanding unit can share the location information of group members with other groups, promoting cooperation and interaction between multiple groups. For example, the location understanding unit can share the location information of group members with other groups to promote cooperation and interaction between multiple groups. For example, a function for working together with other nearby groups can be implemented. The location understanding unit can also use generative AI to analyze the location information of group members and suggest points for cooperation with other groups. For example, it can suggest points where groups walking the same route can meet up. The location understanding unit can also share the location information of group members with other groups, providing a platform for promoting interaction between multiple groups. For example, a function can be implemented that allows messages to be exchanged between groups. This can promote cooperation and interaction between multiple groups by sharing the location information of group members with other groups.

[0065] The location understanding unit can enhance real-time photo sharing and message sending functions based on the location information of group members. The location understanding unit, for example, enhances the real-time photo sharing function based on the location information of group members. For example, it implements a function that automatically shares photos taken at a specific location with everyone in the group. The location understanding unit also enhances the real-time message sending function based on the location information of group members. For example, it implements a function that sends messages based on the current location. The location understanding unit also uses a generation AI to analyze the location information of group members and enhances the real-time photo sharing and message sending functions. For example, it implements a function that automatically sends photos and messages based on the location information. This enhances the real-time photo sharing and message sending functions based on the location information of group members, thereby facilitating communication.

[0066] The location grasping unit can use the emotion estimation function to suggest activities to increase the motivation of the entire group based on the emotional states of the group members. For example, the location grasping unit uses the emotion estimation function to analyze the emotional states of the group members and suggest activities to increase the motivation of the entire group. For example, it suggests games or challenges that everyone can enjoy. The location grasping unit also suggests specific activities to increase motivation based on the emotional data of the group members. For example, it suggests rest areas where people can refresh themselves or spots with beautiful views. The location grasping unit also uses the emotion estimation function to analyze the emotional states of the group members in real time and dynamically suggest activities to increase motivation. For example, it suggests activities that match the mood of the members. In this way, by suggesting activities to increase motivation based on the emotional states of the group members, morale of the entire group is improved.

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

[0068] The route recording unit can analyze the user's walking pattern, estimate their fatigue level and physical strength, and suggest appropriate rest points. For example, it can analyze the user's walking speed and stride length to estimate their fatigue level. If their walking speed suddenly drops, it determines that fatigue is accumulating and suggests a break. The route recording unit also monitors the user's heart rate and breathing rate to estimate their physical strength. If their heart rate rises above a certain level, it determines that their physical strength is depleted and suggests a break. The route recording unit also analyzes the user's walking distance and altitude changes to suggest appropriate rest points. It suggests a break after walking a certain distance or after overcoming a steep uphill slope. In this way, the system supports safe mountain climbing by analyzing the user's fatigue level and physical strength and suggesting appropriate rest points.

[0069] The route recording unit can automatically analyze the contents of a photo and automatically add information about the weather, terrain, and types of flora and fauna. For example, it uses generative AI to analyze the contents of a photograph and automatically add weather information. It estimates the day's weather from the color of the sky and the shape of the clouds in the photograph and records it in the log. The route recording unit also analyzes the terrain in the photograph and automatically adds information such as mountain height and slope angle. It estimates the mountain's elevation and terrain characteristics from the shape of the mountain in the photograph. The route recording unit also automatically recognizes the types of flora and fauna photographed and adds that information to the log. It identifies the types of flowers and birds in the photograph and records their names and characteristics in the log. This automatically adds information related to the photo, providing a detailed record.

[0070] The route recording unit can use the emotion estimation function to analyze emotions from the user's comments and recorded notes and implement a highlighting function that emphasizes positive experiences. For example, it can analyze emotions from the user's comments and recorded notes and implement a highlighting function that emphasizes positive experiences. Positive expressions such as "It was fun" and "Beautiful scenery" can be extracted and displayed as highlights. The emotion estimation function can also be used to detect negative emotions from the user's comments and recorded notes and display them in a less conspicuous manner. Negative expressions such as "It was tired" and "It was dangerous" can be suppressed. The system can also analyze the user's emotions and add visual effects to emphasize positive experiences. Bright colors and icons can be displayed for positive comments. This enhances the enjoyment of mountain climbing by emphasizing the user's positive experiences.

[0071] The route recording unit can display the average time and difficulty when other users have walked the same route based on the log data and provide it as reference information. For example, it analyzes the log data and displays the average time when other users have walked the same route. It calculates the average time required for a specific route based on past data and provides it as reference information. It also evaluates the difficulty when other users have walked the same route based on the log data. It analyzes walking speed and altitude changes and displays the route difficulty in categories such as "easy," "normal," and "difficult." It also displays the popularity of a specific route based on the log data of other users. It tallys the number of users who have walked the same route and displays popular routes in a ranking format. In this way, by displaying the average time and difficulty based on the data of other users, it provides reference information for mountain climbing plans.

[0072] The route recording unit displays the log data on a 3D map and provides visual information, thereby recreating a realistic experience. For example, the log data can be displayed on a 3D map to provide three-dimensional visual information. The route walked by the user can be reproduced on the 3D map, visually displaying the terrain and elevation changes. Photographs taken can also be placed on the 3D map to provide three-dimensional visual information. The location where a photograph was taken can be marked on the 3D map so that the photograph can be displayed by clicking on it. Animated versions of the user's walking route can also be displayed on the 3D map to recreate a realistic experience. Animated versions of the route walked by the user can be played back to visually recreate the walking experience. In this way, displaying the log data on a 3D map provides three-dimensional visual information and recreates a realistic experience.

[0073] The route recording unit can use the emotion estimation function to display the emotions felt by the user at a specific location on a map, allowing other users to predict the experience they will have at that location. For example, the emotion estimation function can be used to analyze the emotions felt by the user at a specific location and display them on a map. Emotions such as "fun" and "scary" can be displayed as icons on the map. A function for predicting the experience at a specific location based on the user's emotion data can also be implemented. Emotion trends at a specific location can be displayed based on past data, allowing other users to predict the experience they will have at that location. The emotion estimation function can also be used to highlight the emotions felt by the user at a specific location, allowing other users to refer to their experiences at that location. Places with a high proportion of positive emotions can be displayed prominently. In this way, the user's emotions can be displayed on a map, allowing other users to predict the experience they will have at that location.

[0074] The image recognition unit can analyze images of plants and animals and automatically group similar plants and animals. For example, generative AI can be used to implement a function that analyzes images of plants and animals and automatically groups similar plants and animals. Flowers and birds of the same species are grouped and displayed. Images of plants and animals are also analyzed to automatically group plants and animals with similar characteristics. Plants and animals with similar colors and shapes are displayed together. Generative AI can also be used to implement a function that analyzes images of plants and animals and group them based on academic classification. Plants and animals are categorized and displayed by family and genus. This makes it easier to organize and search information by analyzing images of plants and animals and grouping similar plants and animals.

[0075] The image authentication unit allows the generation AI to automatically provide relevant academic information and conservation information for images of plants and animals taken by the user. For example, the generation AI automatically provides relevant academic information for images of plants and animals taken by the user. It displays the scientific names and ecological information of plants and animals. The generation AI also provides conservation information for images of plants and animals taken by the user. It displays information on endangered species and protected plants and animals. The generation AI also automatically provides relevant academic papers and research results for images of plants and animals taken by the user. It displays the latest research information on plants and animals. This deepens the user's knowledge by automatically providing academic information and conservation information related to images of plants and animals.

[0076] The image authentication unit can use the emotion estimation function to analyze the emotion a user feels toward a specific animal or plant, and automatically generate an introductory text for the animal or plant based on that emotion. For example, the emotion estimation function can be used to analyze the emotion a user feels toward a specific animal or plant, and automatically generate an introductory text for the animal or plant based on that emotion. An introductory text reflecting emotions such as "beautiful" or "rare" can be generated. The emotion toward a specific animal or plant can also be analyzed based on the user's emotion data, and an introductory text based on that emotion can be generated. An introductory text that emphasizes positive emotions can also be generated. The emotion estimation function can also be used to analyze the emotion a user feels toward a specific animal or plant, and introduce the characteristics of the animal or plant based on that emotion. An introductory text reflecting emotions such as "moving scenery" or "soothing flowers" can be generated. This makes it possible to provide more emotional information by automatically generating introductory texts for animals and plants based on the user's emotions.

[0077] The location understanding unit can analyze the location information of group members and suggest optimal meeting points and rest points. For example, using generation AI, the location information of group members is analyzed and the optimal meeting point is suggested. A midpoint that is easy for everyone to meet up is automatically calculated and suggested. Rest points are also suggested based on the location information of group members. Appropriate rest locations are selected and suggested taking into account the locations of everyone. Furthermore, generation AI is used to analyze the location information of group members in real time and dynamically suggest optimal meeting points and rest points. Suggestions are updated according to the movement status of members. In this way, by analyzing the location information of group members and suggesting optimal meeting points and rest points, the safety and efficiency of the entire group is improved.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The route recording unit records the user's walking route, walking time, photos taken (and their locations), comments, and record notes as a log. For example, the route recording unit uses GPS data to record the user's walking route. The route recording unit can also measure walking time and record the start and end times. The route recording unit can also save the photos taken in JPEG format and record the date and time of the photo and location information as metadata. Furthermore, the route recording unit can save comments and record notes using text input or voice input. Step 2: The image authentication unit authenticates the images of plants and animals recorded by the route recording unit. For example, the image authentication unit may use a machine learning algorithm to recognize the type of plant or animal. The image authentication unit may also analyze multiple images and extract characteristics of plants and animals to improve authentication accuracy. The image authentication unit may also add the authentication results to a log and share them with other users. Step 3: The location determination unit determines the real-time locations of the group members recorded by the route recording unit. For example, the location determination unit obtains the locations of the group members in real time using GPS data. The location determination unit can also display the obtained location information on the app to visualize the locations of the group members. The location determination unit can also set the update frequency of the location information and update the location information in real time.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] 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.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A route recording section logs the user's walking route, walking time, photos taken (and their locations), comments, and memos. an image authentication unit that authenticates the images of the plants and animals recorded by the route recording unit; a location recognition unit that recognizes the real-time locations of the group members recorded by the route recording unit. A system characterized by:

2. The route recording unit Automatically analyzes the content of the photo and automatically adds information about the weather, terrain, and types of flora and fauna.

2. The system of claim 1.

3. The route recording unit Analyze the user's walking pattern, estimate fatigue level and physical strength, and suggest rest points 2. The system of claim 1.

4. The route recording unit Analyzing emotions from the user's comments and recorded notes and implementing a highlighting function that emphasizes positive experiences 2. The system of claim 1.

5. The route recording unit Based on the log data, the average time and difficulty of other users walking the same route are displayed and provided as reference information.

2. The system of claim 1.

6. The route recording unit Log data is displayed on the 3D map, providing visual information to recreate a realistic experience.

2. The system of claim 1.

7. The route recording unit Displaying the emotions felt by the user at a particular location on a map, allowing other users to predict their experiences at that location 2. The system of claim 1.

8. The image authentication unit Analyzing the images of the plants and animals and automatically grouping similar plants and animals together 2. The system of claim 1.

Images