system

Abstract

The system according to this embodiment aims to acquire information about the surrounding scenery and buildings in real time during travel and to suggest the optimal route. [Solution] The system according to the embodiment comprises a recognition unit, an explanation unit, a suggestion unit, and a translation unit. The recognition unit analyzes images taken by the user and recognizes the surrounding scenery and buildings. The explanation unit explains the history and cultural background based on the information recognized by the recognition unit. The suggestion unit proposes the optimal route based on the information obtained by the explanation unit, taking into account the user's current location and destination. The translation unit translates local signs and menus in real time based on the route proposed by the suggestion unit and reads them aloud.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to obtain information on surrounding scenery and buildings in real time during a trip and propose an optimal route.

[0005] The system according to the embodiment aims to obtain information on surrounding scenery and buildings in real time during a trip and propose an optimal route.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, an explanation unit, a suggestion unit, and a translation unit. The recognition unit analyzes images taken by the user and recognizes the surrounding scenery and buildings. The explanation unit explains the history and cultural background based on the information recognized by the recognition unit. The suggestion unit proposes the optimal route considering the user's current location and destination based on the information obtained by the explanation unit. The translation unit translates local signs and menus in real time and reads them aloud based on the route proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can acquire information about the surrounding scenery and buildings in real time during travel and suggest the optimal route. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel support system according to an embodiment of the present invention is an advanced travel support system that integrates and processes voice, images, and text using multimodal AI. This travel support system recognizes the surrounding scenery in real time and proposes the optimal route while explaining its history and cultural background. It also has a function to translate local signs and menus in real time and read them aloud. First, the user takes pictures of the surrounding scenery using a smartphone or tablet at their travel destination. The multimodal AI analyzes the captured images and recognizes buildings and landmarks. For example, if the user photographs a historical building, it will explain the history and cultural background of that building in real time. Next, the multimodal AI proposes the optimal route considering the user's current location and destination. For example, when the user is visiting tourist spots, it will propose a route that allows for efficient movement and provide detailed information about tourist spots. Furthermore, it also has a function to translate local signs and menus in real time and read them aloud. For example, if the user photographs a menu at a restaurant, the multimodal AI will translate the menu and read it aloud. This allows users to understand local information despite language barriers. This system proposes a personalized itinerary based on the user's interests and provides interactive learning that promotes a deep understanding of culture. For example, if a user is interested in a specific historical theme, the system will suggest tourist spots related to that theme and provide detailed information. Furthermore, the multimodal AI recognizes the user's real-time environment and provides optimal information. For instance, when a user is walking through a tourist area, it will instantly recognize the surrounding scenery and buildings and provide information relevant to that location. In this way, travel support systems utilizing multimodal AI provide users with a convenient and efficient travel experience, enabling free travel that transcends language barriers. Moreover, they promote deeper cultural understanding and international exchange, contributing to personal growth and improved well-being. Thus, travel support systems can enhance users' travel experiences and provide information that transcends language barriers.

[0029] The travel support system according to this embodiment comprises a recognition unit, an explanation unit, a suggestion unit, and a translation unit. The recognition unit analyzes images taken by the user and recognizes the surrounding scenery and buildings. The recognition unit analyzes images taken by the user and recognizes buildings and landmarks, for example, using an image analysis algorithm. The recognition unit can extract features in the image and identify buildings and landmarks, for example, using deep learning technology. The recognition unit can also analyze images taken by the user in real time and recognize the surrounding scenery and buildings, for example, using image recognition technology. The explanation unit explains the history and cultural background based on the information recognized by the recognition unit. The explanation unit refers to a pre-prepared database to explain the history and cultural background of buildings and landmarks recognized by the recognition unit, for example. The explanation unit can explain the history and cultural background based on the recognized information, for example, using natural language processing technology. The explanation unit can also explain the recognized information in speech, for example, using speech synthesis technology. The suggestion unit proposes the optimal route, taking into account the user's current location and destination, based on the information obtained by the explanation unit. The suggestion unit, for example, receives the user's current location and destination as input and uses an algorithm to calculate the optimal route. The suggestion unit can, for example, refer to traffic information and map data to suggest a route that allows the user to travel efficiently. The suggestion unit can also, for example, suggest a personalized route by considering the user's interests and past travel history. The translation unit translates local signs and menus in real time and reads them aloud based on the route suggested by the suggestion unit. The translation unit analyzes images of signs and menus taken by the user and extracts the text. The translation unit can, for example, use machine translation technology to translate the extracted text into the user's native language. The translation unit can also, for example, use speech synthesis technology to read the translated text aloud. As a result, the travel support system according to this embodiment can analyze images taken by the user, recognize the surrounding scenery and buildings, explain the history and cultural background, suggest the optimal route, and translate and read aloud local signs and menus in real time.

[0030] The recognition unit analyzes images taken by the user to recognize the surrounding scenery and buildings. For example, the recognition unit uses image analysis algorithms to analyze images taken by the user and recognize buildings and landmarks. Specifically, it can use deep learning technology to extract features from images and identify buildings and landmarks. Deep learning technology uses a convolutional neural network (CNN) to extract features from the pixel information of an image and recognize buildings and landmarks with high accuracy based on this. For example, if a user photographs a historical building, the recognition unit analyzes the building's shape, color, texture, and other features, and matches them with known buildings in a database to identify the building's name and detailed information. The recognition unit can also use image recognition technology to analyze images taken by the user in real time and recognize the surrounding scenery and buildings. In real-time analysis, the image is analyzed the moment the user points the camera, and recognition results are provided immediately. This allows users to obtain information about buildings and landmarks on the spot simply by taking pictures of the surrounding scenery with their smartphone or camera while traveling. Furthermore, the recognition unit can integrate and analyze multiple images to recognize scenery and buildings in a wide area at once. For example, by performing panoramic or continuous shooting, the camera can analyze a wide area of ​​scenery and simultaneously recognize multiple buildings and landmarks. This allows users to efficiently gather information while traveling.

[0031] The explanatory unit explains the history and cultural background based on the information recognized by the recognition unit. For example, to explain the history and cultural background of a building or landmark recognized by the recognition unit, the explanatory unit refers to a pre-prepared database. The database contains detailed information about each building or landmark, and based on this, it provides an appropriate explanation to the user. For example, if a building is a medieval castle, it can provide information such as the building's construction date, builder, historical events, and architectural style. The explanatory unit can use natural language processing technology to explain the history and cultural background based on the recognized information. Natural language processing technology is used to analyze text data and provide information to the user in an easy-to-understand format. For example, based on the information of the recognized building, it generates a concise and easy-to-understand explanation and presents it to the user. The explanatory unit can also use speech synthesis technology to explain the recognized information audibly. Speech synthesis technology converts text data into speech and provides information to the user audibly. This improves convenience during travel, as the user can obtain information by voice without looking at the screen. Furthermore, the explanatory unit can provide information in multiple languages ​​according to the user's language settings. For example, by providing explanations tailored to the user's native language, such as English, Japanese, or French, information can be delivered across language barriers. This allows the explanation section to provide comprehensive, multilingual information to users, enriching their travel experience.

[0032] The suggestion unit proposes the optimal route based on the information obtained by the explanation unit, taking into account the user's current location and destination. For example, the suggestion unit uses an algorithm that takes the user's current location and destination as input and calculates the optimal route. Specifically, it uses GPS data to identify the user's current location and calculates the shortest or most efficient route to the destination. The suggestion unit can also refer to traffic information and map data to propose a route that allows the user to travel efficiently. For example, it can obtain real-time traffic information and propose a route that avoids obstacles such as congestion and road construction. It can also consider public transport timetables and operating status to propose the optimal mode of transportation using trains and buses. Furthermore, the suggestion unit can propose a personalized route considering the user's interests and past travel history. For example, it can propose a sightseeing route tailored to the user's preferences based on data on places the user has visited in the past and tourist spots they have been interested in. This allows the user to create a travel plan that suits their interests. In addition, the suggestion unit can update the route in real time while the user is traveling. For example, it can recalculate the optimal route in response to changes in traffic conditions and the user's actions, providing the latest information. This ensures that the user is always traveling on the optimal route, reducing stress during travel. The proposal department can provide efficient and personalized route suggestions to improve the user's travel experience.

[0033] The translation unit translates local signs and menus in real time and reads them aloud, based on the route proposed by the suggestion unit. For example, the translation unit analyzes images of signs and menus taken by the user and extracts the text. Specifically, it uses optical character recognition (OCR) technology to detect characters in the image and convert them into text data. Next, it uses machine translation technology to translate the extracted text into the user's native language. The machine translation technology uses a translation model based on a neural network to achieve highly accurate translations. For example, it can analyze a restaurant menu photographed by the user and translate the dish names and descriptions into the user's native language. The translation unit can also read the translated text aloud using speech synthesis technology. Speech synthesis technology converts text into speech with natural pronunciation, providing information to the user aurally. This improves convenience during travel, as the user can check the translation results by voice without looking at the screen. Furthermore, the translation unit supports multiple languages ​​and can perform translations according to the user's language settings. For example, it supports various languages ​​such as English, Japanese, French, and Chinese, and can provide appropriate translations tailored to the country or region the user is visiting. This allows the translation department to make it easier for users to understand local information and facilitate smooth communication during their travels. The translation department can support the user's travel experience and improve the enjoyment and convenience of travel by providing information that transcends language barriers.

[0034] The recognition unit can analyze images taken by the user and recognize buildings and landmarks. For example, the recognition unit can scan images taken by the user and save them as image data. Then, the recognition unit analyzes the image data using deep learning technology to recognize buildings and landmarks. The recognition unit can also analyze images taken by the user using a smartphone camera in real time and recognize buildings and landmarks. For example, the recognition unit can input images taken by the camera into a deep learning model to identify buildings and landmarks. The recognition unit can also upload images taken by the user to the cloud and analyze them using a deep learning model on the cloud. This allows the recognition unit to analyze images taken by the user and recognize buildings and landmarks. Some or all of the above processing in the recognition unit may be performed using, for example, generative AI, or not using generative AI. For example, the recognition unit can input images taken by the user into a generative AI and have the generative AI perform the recognition of buildings and landmarks.

[0035] The explanation unit can explain the history and cultural background of buildings and landmarks recognized by the recognition unit. For example, the explanation unit can retrieve information about buildings and landmarks recognized by the recognition unit from a database and provide it to the user. For example, the explanation unit can use natural language processing technology to explain the history and cultural background based on the recognized information. The explanation unit can also use speech synthesis technology to explain the recognized information aloud. For example, the explanation unit can display information about buildings and landmarks recognized as text and provide it to the user. The explanation unit can also read the recognized information aloud. For example, the explanation unit can use speech synthesis technology to explain the recognized information aloud. This allows the explanation unit to explain the history and cultural background of buildings and landmarks recognized by the recognition unit. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the explanation unit can input the recognized information into a generative AI and have the generative AI perform an explanation of the history and cultural background.

[0036] The suggestion unit can propose the optimal route considering the user's current location and destination. For example, the suggestion unit uses an algorithm that takes the user's current location and destination as input and calculates the optimal route. For example, the suggestion unit can refer to traffic information and map data to propose a route that allows the user to travel efficiently. Furthermore, the suggestion unit can propose a personalized route considering the user's interests and past travel history. For example, the suggestion unit takes the user's current location and destination as input and calculates the optimal route. Furthermore, the suggestion unit can propose a personalized route considering the user's interests and past travel history. For example, the suggestion unit provides information such as tourist spots and restaurants based on the user's interests. This allows the suggestion unit to propose the optimal route considering the user's current location and destination. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's current location and destination into a generative AI and have the generative AI propose the optimal route.

[0037] The translation unit can translate local signs and menus in real time and read them aloud. For example, the translation unit can analyze images of signs and menus taken by the user and extract the text. The translation unit can translate the extracted text into the user's native language using machine translation technology. The translation unit can also read the translated text aloud using speech synthesis technology. For example, the translation unit can analyze images of menus taken by the user and extract the text. The translation unit can also translate the extracted text using machine translation technology and read it aloud. For example, the translation unit can analyze images of signs taken by the user and extract the text. The translation unit can also translate the extracted text using machine translation technology and read it aloud. This allows for real-time translation and reading of local signs and menus. Some or all of the above processing in the translation unit may be performed using, for example, generative AI, or without generative AI. For example, the translation unit can input images of signs or menus taken by the user into a generation AI, which can then extract and translate the text.

[0038] The suggestion unit can propose a personalized itinerary based on the user's interests. For example, the suggestion unit can propose a personalized route considering the user's interests and past activity history. For example, if the user is interested in a particular historical theme, the suggestion unit can propose tourist spots related to that theme. The suggestion unit can also provide information such as tourist spots and restaurants based on the user's interests. For example, the suggestion unit can provide information such as tourist spots and restaurants based on the user's interests. The suggestion unit can also provide information such as tourist spots and restaurants based on the user's interests. This makes it possible to propose a personalized itinerary based on the user's interests. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's interests and past activity history into a generative AI and have the generative AI propose a personalized route.

[0039] The recognition unit can recognize the user's real-time environment and provide optimal information. For example, the recognition unit recognizes the user's real-time environment using sensors. For example, the recognition unit can recognize the environment around the user in real time using cameras and microphones. The recognition unit can also recognize the environment in real time using the user's location information. For example, the recognition unit can use GPS data to determine the user's current location and recognize the environment around it. The recognition unit can also recognize the user's real-time environment and provide optimal information. For example, the recognition unit recognizes the environment around the user in real time and provides information related to that location. This allows the recognition unit to recognize the user's real-time environment and provide optimal information. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's real-time environment data into a generative AI and have the generative AI provide optimal information.

[0040] The recognition unit can improve recognition accuracy by referring to the user's past shooting history during recognition. For example, the recognition unit can analyze images previously taken by the user and prioritize the recognition of similar buildings and landmarks. For example, the recognition unit can identify categories of interest from the user's past shooting history and prioritize the recognition of objects belonging to those categories. The recognition unit can also prioritize the recognition of relevant locations based on information about places the user has visited in the past. For example, the recognition unit can analyze images previously taken by the user and prioritize the recognition of similar buildings and landmarks. For example, the recognition unit can identify categories of interest from the user's past shooting history and prioritize the recognition of objects belonging to those categories. This allows for improved recognition accuracy by referring to the user's past shooting history. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's past shooting history data into a generative AI and have the generative AI perform the improvement of recognition accuracy.

[0041] The recognition unit can optimize its recognition algorithm according to the weather and time of day during recognition. For example, in sunny weather, the recognition unit will prioritize recognizing outdoor tourist attractions. For example, at night, the recognition unit can prioritize recognizing illuminated buildings and landmarks. Furthermore, in rainy weather, the recognition unit can prioritize recognizing indoor tourist attractions and museums. For example, in sunny weather, the recognition unit will prioritize recognizing outdoor tourist attractions. Furthermore, at night, the recognition unit can prioritize recognizing illuminated buildings and landmarks. By optimizing the recognition algorithm according to the weather and time of day, recognition accuracy can be improved. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input weather and time-of-day data into a generative AI and have the generative AI perform the optimization of the recognition algorithm.

[0042] The recognition unit can prioritize the selection of recognition targets by considering the user's geographical location information during recognition. For example, if the user is in a tourist area, the recognition unit can prioritize the recognition of major tourist spots. For example, if the user is in a shopping area, the recognition unit can prioritize the recognition of popular stores and shopping malls. Also, if the user is in a nature park, the recognition unit can prioritize the recognition of scenic spots and hiking trails. For example, if the user is in a tourist area, the recognition unit can prioritize the recognition of major tourist spots. Also, if the user is in a shopping area, the recognition unit can prioritize the recognition of popular stores and shopping malls. By prioritizing the selection of recognition targets by considering the user's geographical location information, the recognition unit can prioritize the provision of information that is important to the user. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recognition unit can input the user's geographical location information into a generative AI and have the generative AI perform the priority selection of recognition targets.

[0043] The recognition unit can analyze the user's social media activity and recognize relevant information during recognition. For example, the recognition unit can recognize relevant tourist spots based on photos shared by the user on social media. For example, the recognition unit can analyze the content of posts from accounts the user follows on social media and recognize relevant places. The recognition unit can also recognize relevant places based on information about places the user has checked into on social media. For example, the recognition unit can recognize relevant tourist spots based on photos shared by the user on social media. For example, the recognition unit can analyze the content of posts from accounts the user follows on social media and recognize relevant places. In this way, relevant information can be recognized by analyzing the user's social media activity. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's social media activity data into a generative AI and have the generative AI perform the recognition of relevant information.

[0044] The explanation unit can adjust the level of detail in its explanation based on the importance of the recognized object. For example, it can provide detailed historical and cultural background information for major tourist attractions. For example, it can provide a concise explanation for general buildings. The explanation unit can also provide detailed information related to specific themes depending on the user's interests. For example, it can provide detailed historical and cultural background information for major tourist attractions. For example, it can provide a concise explanation for general buildings. This allows the explanation unit to provide detailed information that is important to the user by adjusting the level of detail in its explanation based on the importance of the recognized object. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the explanation unit can input importance data of the recognized object into a generative AI and have the generative AI perform the adjustment of the level of detail in the explanation.

[0045] The explanatory unit can apply different explanatory algorithms depending on the category of the subject during the explanation process. For example, for historical buildings, the explanatory unit can provide explanations focusing on historical background and architectural style. For natural landscapes, the explanatory unit can provide explanations regarding geological features and ecosystems. Furthermore, for modern architecture, the explanatory unit can provide explanations regarding the designer and the intent behind the building. For example, for historical buildings, the explanatory unit can provide explanations focusing on historical background and architectural style. Furthermore, for natural landscapes, the explanatory unit can provide explanations regarding geological features and ecosystems. This allows for more appropriate explanations by applying different explanatory algorithms depending on the category of the subject. Some or all of the above processing in the explanatory unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the explanatory unit can input the target category data into a generative AI and have the generative AI execute the application of the explanatory algorithm.

[0046] The explanation unit can determine the priority of explanations based on the historical background of the subject. For example, the explanation unit will prioritize explaining places related to important historical events. For example, the explanation unit can prioritize explaining places related to specific historical themes according to the user's interests. The explanation unit can also provide detailed explanations for places with rich historical backgrounds. For example, the explanation unit will prioritize explaining places related to important historical events. For example, the explanation unit can prioritize explaining places related to specific historical themes according to the user's interests. By determining the priority of explanations based on the historical background of the subject, information important to the user can be explained preferentially. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the explanation unit can input the historical background data of the subject into a generative AI and have the generative AI perform the determination of the explanation priority.

[0047] The explanatory unit can improve the accuracy of its explanations by referring to relevant literature during the explanation process. For example, the explanatory unit can provide accurate information by referring to academic papers and books related to the content being explained. For example, the explanatory unit can provide detailed information by referring to historical documents related to the content being explained. Furthermore, the explanatory unit can provide up-to-date information by referring to the latest research findings related to the content being explained. For example, the explanatory unit can provide accurate information by referring to academic papers and books related to the content being explained. Furthermore, the explanatory unit can provide detailed information by referring to historical documents related to the content being explained. In this way, the accuracy of the explanation can be improved by referring to relevant literature. Some or all of the above processing in the explanatory unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanatory unit can input the relevant literature data into a generative AI and have the generative AI perform the improvement of the accuracy of the explanation.

[0048] The suggestion unit can propose the optimal route by referring to the user's past travel history when making a suggestion. For example, the suggestion unit can propose the optimal route based on routes the user has used in the past. For example, the suggestion unit can propose a route that avoids congestion based on the user's past travel history. The suggestion unit can also analyze the user's past travel history and propose the most efficient route. For example, the suggestion unit can propose the optimal route based on routes the user has used in the past. For example, the suggestion unit can propose a route that avoids congestion based on the user's past travel history. In this way, the optimal route can be proposed by referring to the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past travel history data into a generative AI and have the generative AI execute the optimal route proposal.

[0049] The suggestion unit can adjust the route when making a suggestion, taking into account the user's current physical condition and fatigue level. For example, if the user is tired, the suggestion unit will suggest the shortest route. For example, if the user is seeking healthy exercise, the suggestion unit can suggest a slightly longer route. Also, if the user is feeling unwell, the suggestion unit can suggest a route that includes rest stops. For example, if the user is tired, the suggestion unit will suggest the shortest route. Also, if the user is seeking healthy exercise, the suggestion unit can suggest a slightly longer route. In this way, by taking into account the user's current physical condition and fatigue level, the optimal route can be suggested. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's physical condition and fatigue level data into a generative AI and have the generative AI perform the route adjustment.

[0050] The suggestion unit can propose the optimal route by considering the user's geographical location information. For example, if the user is in a tourist area, the suggestion unit can propose a route that visits major tourist spots. For example, if the user is in a shopping area, the suggestion unit can propose a route that visits popular stores. Also, if the user is in a nature park, the suggestion unit can propose a scenic hiking trail. For example, if the suggestion unit is in a tourist area, the suggestion unit can propose a route that visits major tourist spots. Also, if the user is in a shopping area, the suggestion unit can propose a route that visits popular stores. In this way, the optimal route can be proposed by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal route.

[0051] The suggestion unit can analyze the user's social media activity and propose relevant routes when making suggestions. For example, the suggestion unit can propose a route visiting relevant tourist spots based on photos the user has shared on social media. For example, the suggestion unit can analyze the content of posts from accounts the user follows on social media and propose a route visiting relevant places. The suggestion unit can also propose relevant routes based on information about places the user has checked into on social media. For example, the suggestion unit can propose a route visiting relevant tourist spots based on photos the user has shared on social media. For example, the suggestion unit can analyze the content of posts from accounts the user follows on social media and propose a route visiting relevant places. In this way, relevant routes can be proposed by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI and have the generative AI propose relevant routes.

[0052] The translation unit can improve translation accuracy by referring to the user's past translation history during translation. For example, the translation unit can prioritize translating similar phrases based on phrases the user has translated in the past. For example, the translation unit can improve translation accuracy for specific language pairs based on the user's past translation history. The translation unit can also provide more natural translations by considering the context in which the user has translated in the past. For example, the translation unit can prioritize translating similar phrases based on phrases the user has translated in the past. The translation unit can also improve translation accuracy for specific language pairs based on the user's past translation history. This allows for improved translation accuracy by referring to the user's past translation history. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input the user's past translation history data into a generative AI and have the generative AI perform the translation accuracy improvement.

[0053] The translation unit can optimize its translation algorithm by considering local dialects and slang during translation. For example, the translation unit can provide more natural translations by considering local dialects. For example, the translation unit can provide appropriate translations by considering local slang. Furthermore, the translation unit can provide contextually appropriate translations by considering the local cultural background. For example, the translation unit can provide more natural translations by considering local dialects. Furthermore, the translation unit can provide appropriate translations by considering local slang. This makes more natural translations possible by considering local dialects and slang. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input local dialect and slang data into a generative AI and have the generative AI perform the optimization of the translation algorithm.

[0054] The translation unit can prioritize the selection of translation targets by considering the user's geographical location during translation. For example, if the user is in a tourist area, the translation unit will prioritize translating signs and menus related to tourist attractions. For example, if the user is in a shopping area, the translation unit can prioritize translating store signs and product descriptions. Also, if the user is in a restaurant, the translation unit can prioritize translating menus. For example, if the user is in a tourist area, the translation unit will prioritize translating signs and menus related to tourist attractions. Also, if the user is in a shopping area, the translation unit can prioritize translating store signs and product descriptions. This allows for the prioritization of translation targets by considering the user's geographical location. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the translation unit can input the user's geographical location information into a generative AI and have the generative AI perform the priority selection of translation targets.

[0055] The translation unit can analyze the user's social media activity and translate relevant information during the translation process. For example, the translation unit can translate relevant signs and menus based on photos shared by the user on social media. For example, the translation unit can analyze the content of posts from accounts the user follows on social media and translate relevant information. The translation unit can also translate relevant signs and menus based on information about places the user has checked into on social media. For example, the translation unit can translate relevant signs and menus based on photos shared by the user on social media. For example, the translation unit can analyze the content of posts from accounts the user follows on social media and translate relevant information. This allows the translation of relevant information by analyzing the user's social media activity. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input the user's social media activity data into a generative AI and have the generative AI perform the translation of relevant information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The recognition unit can adjust the priority of recognition targets by referring to the user's past travel history. For example, it can prioritize recognizing places of similar interest based on information about tourist spots the user has visited in the past. It can also analyze photos of places the user has visited in the past and prioritize recognizing similar landscapes and buildings. Furthermore, it can refer to reviews and ratings of places the user has visited in the past and prioritize recognizing places with high ratings. This makes it possible to provide more personalized information by utilizing the user's past travel history.

[0058] The suggestion function can monitor the user's current activity in real time and adjust its suggestions accordingly. For example, if the user is walking, the suggestion function can suggest sightseeing spots accessible on foot. If the user is driving, the suggestion function can suggest routes and sightseeing spots suitable for driving. Furthermore, if the user is taking a break, the suggestion function can suggest nearby cafes and rest spots. This enables appropriate suggestions tailored to the user's current activity, thereby improving the user's travel experience.

[0059] The recognition unit can adjust the priority of recognition targets by referring to the user's current weather information. For example, on sunny days, it can prioritize the recognition of outdoor tourist attractions. On rainy days, it can prioritize the recognition of indoor tourist attractions and museums. Furthermore, on snowy days, it can prioritize the recognition of ski resorts and winter activities. This enables appropriate recognition according to the user's current weather information, improving the user's travel experience.

[0060] The recognition unit can adjust the priority of recognition targets by referring to the user's current location information. For example, if the user is in a tourist area, it can prioritize recognizing major tourist spots. If the user is in a shopping area, it can prioritize recognizing popular stores and shopping malls. Furthermore, if the user is in a nature park, it can prioritize recognizing scenic spots and hiking trails. This enables appropriate recognition based on the user's current location information, improving the user's travel experience.

[0061] The suggestion function can monitor the user's current health status and adjust its suggestions accordingly. For example, if the user is tired, the suggestion function can suggest a rest. If the user is seeking healthy exercise, the suggestion function can suggest walking or hiking trails. Furthermore, if the user is feeling unwell, the suggestion function can suggest nearby medical facilities or pharmacies. This enables appropriate suggestions tailored to the user's current health condition, thereby improving the user's travel experience.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The recognition unit analyzes the image captured by the user to recognize the surrounding scenery and buildings. For example, it uses image analysis algorithms and deep learning technology to extract features from the image and identify buildings and landmarks. It can also analyze the image in real time to recognize the surrounding scenery and buildings. Step 2: The explanation unit explains the history and cultural background based on the information recognized by the recognition unit. For example, it refers to a pre-prepared database and uses natural language processing and speech synthesis technologies to explain the history and cultural background based on the recognized information. Step 3: The suggestion unit proposes the optimal route based on the information obtained by the explanation unit, taking into account the user's current location and destination. For example, it may use an algorithm that calculates a route that allows the user to travel efficiently by referring to traffic information and map data. It can also propose a personalized route by considering the user's interests and past travel history. Step 4: The translation unit translates local signs and menus in real time and reads them aloud, based on the route proposed by the proposal unit. For example, it analyzes images of signs and menus taken by the user, extracts the text, and translates it into the user's native language using machine translation technology. It can also read the translated text aloud using speech synthesis technology.

[0064] (Example of form 2) The travel support system according to an embodiment of the present invention is an advanced travel support system that integrates and processes voice, images, and text using multimodal AI. This travel support system recognizes the surrounding scenery in real time and proposes the optimal route while explaining its history and cultural background. It also has a function to translate local signs and menus in real time and read them aloud. First, the user takes pictures of the surrounding scenery using a smartphone or tablet at their travel destination. The multimodal AI analyzes the captured images and recognizes buildings and landmarks. For example, if the user photographs a historical building, it will explain the history and cultural background of that building in real time. Next, the multimodal AI proposes the optimal route considering the user's current location and destination. For example, when the user is visiting tourist spots, it will propose a route that allows for efficient movement and provide detailed information about tourist spots. Furthermore, it also has a function to translate local signs and menus in real time and read them aloud. For example, if the user photographs a menu at a restaurant, the multimodal AI will translate the menu and read it aloud. This allows users to understand local information despite language barriers. This system proposes a personalized itinerary based on the user's interests and provides interactive learning that promotes a deep understanding of culture. For example, if a user is interested in a specific historical theme, the system will suggest tourist spots related to that theme and provide detailed information. Furthermore, the multimodal AI recognizes the user's real-time environment and provides optimal information. For instance, when a user is walking through a tourist area, it will instantly recognize the surrounding scenery and buildings and provide information relevant to that location. In this way, travel support systems utilizing multimodal AI provide users with a convenient and efficient travel experience, enabling free travel that transcends language barriers. Moreover, they promote deeper cultural understanding and international exchange, contributing to personal growth and improved well-being. Thus, travel support systems can enhance users' travel experiences and provide information that transcends language barriers.

[0065] The travel support system according to this embodiment comprises a recognition unit, an explanation unit, a suggestion unit, and a translation unit. The recognition unit analyzes images taken by the user and recognizes the surrounding scenery and buildings. The recognition unit analyzes images taken by the user and recognizes buildings and landmarks, for example, using an image analysis algorithm. The recognition unit can extract features in the image and identify buildings and landmarks, for example, using deep learning technology. The recognition unit can also analyze images taken by the user in real time and recognize the surrounding scenery and buildings, for example, using image recognition technology. The explanation unit explains the history and cultural background based on the information recognized by the recognition unit. The explanation unit refers to a pre-prepared database to explain the history and cultural background of buildings and landmarks recognized by the recognition unit, for example. The explanation unit can explain the history and cultural background based on the recognized information, for example, using natural language processing technology. The explanation unit can also explain the recognized information in speech, for example, using speech synthesis technology. The suggestion unit proposes the optimal route, taking into account the user's current location and destination, based on the information obtained by the explanation unit. The suggestion unit, for example, receives the user's current location and destination as input and uses an algorithm to calculate the optimal route. The suggestion unit can, for example, refer to traffic information and map data to suggest a route that allows the user to travel efficiently. The suggestion unit can also, for example, suggest a personalized route by considering the user's interests and past travel history. The translation unit translates local signs and menus in real time and reads them aloud based on the route suggested by the suggestion unit. The translation unit analyzes images of signs and menus taken by the user and extracts the text. The translation unit can, for example, use machine translation technology to translate the extracted text into the user's native language. The translation unit can also, for example, use speech synthesis technology to read the translated text aloud. As a result, the travel support system according to this embodiment can analyze images taken by the user, recognize the surrounding scenery and buildings, explain the history and cultural background, suggest the optimal route, and translate and read aloud local signs and menus in real time.

[0066] The recognition unit analyzes images taken by the user to recognize the surrounding scenery and buildings. For example, the recognition unit uses image analysis algorithms to analyze images taken by the user and recognize buildings and landmarks. Specifically, it can use deep learning technology to extract features from images and identify buildings and landmarks. Deep learning technology uses a convolutional neural network (CNN) to extract features from the pixel information of an image and recognize buildings and landmarks with high accuracy based on this. For example, if a user photographs a historical building, the recognition unit analyzes the building's shape, color, texture, and other features, and matches them with known buildings in a database to identify the building's name and detailed information. The recognition unit can also use image recognition technology to analyze images taken by the user in real time and recognize the surrounding scenery and buildings. In real-time analysis, the image is analyzed the moment the user points the camera, and recognition results are provided immediately. This allows users to obtain information about buildings and landmarks on the spot simply by taking pictures of the surrounding scenery with their smartphone or camera while traveling. Furthermore, the recognition unit can integrate and analyze multiple images to recognize scenery and buildings in a wide area at once. For example, by performing panoramic or continuous shooting, the camera can analyze a wide area of ​​scenery and simultaneously recognize multiple buildings and landmarks. This allows users to efficiently gather information while traveling.

[0067] The explanatory unit explains the history and cultural background based on the information recognized by the recognition unit. For example, to explain the history and cultural background of a building or landmark recognized by the recognition unit, the explanatory unit refers to a pre-prepared database. The database contains detailed information about each building or landmark, and based on this, it provides an appropriate explanation to the user. For example, if a building is a medieval castle, it can provide information such as the building's construction date, builder, historical events, and architectural style. The explanatory unit can use natural language processing technology to explain the history and cultural background based on the recognized information. Natural language processing technology is used to analyze text data and provide information to the user in an easy-to-understand format. For example, based on the information of the recognized building, it generates a concise and easy-to-understand explanation and presents it to the user. The explanatory unit can also use speech synthesis technology to explain the recognized information audibly. Speech synthesis technology converts text data into speech and provides information to the user audibly. This improves convenience during travel, as the user can obtain information by voice without looking at the screen. Furthermore, the explanatory unit can provide information in multiple languages ​​according to the user's language settings. For example, by providing explanations tailored to the user's native language, such as English, Japanese, or French, information can be delivered across language barriers. This allows the explanation section to provide comprehensive, multilingual information to users, enriching their travel experience.

[0068] The suggestion unit proposes the optimal route based on the information obtained by the explanation unit, taking into account the user's current location and destination. For example, the suggestion unit uses an algorithm that takes the user's current location and destination as input and calculates the optimal route. Specifically, it uses GPS data to identify the user's current location and calculates the shortest or most efficient route to the destination. The suggestion unit can also refer to traffic information and map data to propose a route that allows the user to travel efficiently. For example, it can obtain real-time traffic information and propose a route that avoids obstacles such as congestion and road construction. It can also consider public transport timetables and operating status to propose the optimal mode of transportation using trains and buses. Furthermore, the suggestion unit can propose a personalized route considering the user's interests and past travel history. For example, it can propose a sightseeing route tailored to the user's preferences based on data on places the user has visited in the past and tourist spots they have been interested in. This allows the user to create a travel plan that suits their interests. In addition, the suggestion unit can update the route in real time while the user is traveling. For example, it can recalculate the optimal route in response to changes in traffic conditions and the user's actions, providing the latest information. This ensures that the user is always traveling on the optimal route, reducing stress during travel. The proposal department can provide efficient and personalized route suggestions to improve the user's travel experience.

[0069] The translation unit translates local signs and menus in real time and reads them aloud, based on the route proposed by the suggestion unit. For example, the translation unit analyzes images of signs and menus taken by the user and extracts the text. Specifically, it uses optical character recognition (OCR) technology to detect characters in the image and convert them into text data. Next, it uses machine translation technology to translate the extracted text into the user's native language. The machine translation technology uses a translation model based on a neural network to achieve highly accurate translations. For example, it can analyze a restaurant menu photographed by the user and translate the dish names and descriptions into the user's native language. The translation unit can also read the translated text aloud using speech synthesis technology. Speech synthesis technology converts text into speech with natural pronunciation, providing information to the user aurally. This improves convenience during travel, as the user can check the translation results by voice without looking at the screen. Furthermore, the translation unit supports multiple languages ​​and can perform translations according to the user's language settings. For example, it supports various languages ​​such as English, Japanese, French, and Chinese, and can provide appropriate translations tailored to the country or region the user is visiting. This allows the translation department to make it easier for users to understand local information and facilitate smooth communication during their travels. The translation department can support the user's travel experience and improve the enjoyment and convenience of travel by providing information that transcends language barriers.

[0070] The recognition unit can analyze images taken by the user and recognize buildings and landmarks. For example, the recognition unit can scan images taken by the user and save them as image data. Then, the recognition unit analyzes the image data using deep learning technology to recognize buildings and landmarks. The recognition unit can also analyze images taken by the user using a smartphone camera in real time and recognize buildings and landmarks. For example, the recognition unit can input images taken by the camera into a deep learning model to identify buildings and landmarks. The recognition unit can also upload images taken by the user to the cloud and analyze them using a deep learning model on the cloud. This allows the recognition unit to analyze images taken by the user and recognize buildings and landmarks. Some or all of the above processing in the recognition unit may be performed using, for example, generative AI, or not using generative AI. For example, the recognition unit can input images taken by the user into a generative AI and have the generative AI perform the recognition of buildings and landmarks.

[0071] The explanation unit can explain the history and cultural background of buildings and landmarks recognized by the recognition unit. For example, the explanation unit can retrieve information about buildings and landmarks recognized by the recognition unit from a database and provide it to the user. For example, the explanation unit can use natural language processing technology to explain the history and cultural background based on the recognized information. The explanation unit can also use speech synthesis technology to explain the recognized information aloud. For example, the explanation unit can display information about buildings and landmarks recognized as text and provide it to the user. The explanation unit can also read the recognized information aloud. For example, the explanation unit can use speech synthesis technology to explain the recognized information aloud. This allows the explanation unit to explain the history and cultural background of buildings and landmarks recognized by the recognition unit. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the explanation unit can input the recognized information into a generative AI and have the generative AI perform an explanation of the history and cultural background.

[0072] The suggestion unit can propose the optimal route considering the user's current location and destination. For example, the suggestion unit uses an algorithm that takes the user's current location and destination as input and calculates the optimal route. For example, the suggestion unit can refer to traffic information and map data to propose a route that allows the user to travel efficiently. Furthermore, the suggestion unit can propose a personalized route considering the user's interests and past travel history. For example, the suggestion unit takes the user's current location and destination as input and calculates the optimal route. Furthermore, the suggestion unit can propose a personalized route considering the user's interests and past travel history. For example, the suggestion unit provides information such as tourist spots and restaurants based on the user's interests. This allows the suggestion unit to propose the optimal route considering the user's current location and destination. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's current location and destination into a generative AI and have the generative AI propose the optimal route.

[0073] The translation unit can translate local signs and menus in real time and read them aloud. For example, the translation unit can analyze images of signs and menus taken by the user and extract the text. The translation unit can translate the extracted text into the user's native language using machine translation technology. The translation unit can also read the translated text aloud using speech synthesis technology. For example, the translation unit can analyze images of menus taken by the user and extract the text. The translation unit can also translate the extracted text using machine translation technology and read it aloud. For example, the translation unit can analyze images of signs taken by the user and extract the text. The translation unit can also translate the extracted text using machine translation technology and read it aloud. This allows for real-time translation and reading of local signs and menus. Some or all of the above processing in the translation unit may be performed using, for example, generative AI, or without generative AI. For example, the translation unit can input images of signs or menus taken by the user into a generation AI, which can then extract and translate the text.

[0074] The suggestion unit can propose a personalized itinerary based on the user's interests. For example, the suggestion unit can propose a personalized route considering the user's interests and past activity history. For example, if the user is interested in a particular historical theme, the suggestion unit can propose tourist spots related to that theme. The suggestion unit can also provide information such as tourist spots and restaurants based on the user's interests. For example, the suggestion unit can provide information such as tourist spots and restaurants based on the user's interests. The suggestion unit can also provide information such as tourist spots and restaurants based on the user's interests. This makes it possible to propose a personalized itinerary based on the user's interests. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's interests and past activity history into a generative AI and have the generative AI propose a personalized route.

[0075] The recognition unit can recognize the user's real-time environment and provide optimal information. For example, the recognition unit recognizes the user's real-time environment using sensors. For example, the recognition unit can recognize the environment around the user in real time using cameras and microphones. The recognition unit can also recognize the environment in real time using the user's location information. For example, the recognition unit can use GPS data to determine the user's current location and recognize the environment around it. The recognition unit can also recognize the user's real-time environment and provide optimal information. For example, the recognition unit recognizes the environment around the user in real time and provides information related to that location. This allows the recognition unit to recognize the user's real-time environment and provide optimal information. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's real-time environment data into a generative AI and have the generative AI provide optimal information.

[0076] The recognition unit can estimate the user's emotions and adjust the priority of objects to be recognized based on the estimated emotions. For example, the recognition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the recognition unit can record the user's voice and estimate emotions using voice analysis technology. The recognition unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the recognition unit can calculate an emotion score based on changes in the user's facial expressions and adjust the priority of objects to be recognized. The recognition unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the priority of objects to be recognized. This allows for recognition tailored to the user's situation by adjusting the priority of objects to be recognized based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI adjust the priority of the objects to be recognized.

[0077] The recognition unit can improve recognition accuracy by referring to the user's past shooting history during recognition. For example, the recognition unit can analyze images previously taken by the user and prioritize the recognition of similar buildings and landmarks. For example, the recognition unit can identify categories of interest from the user's past shooting history and prioritize the recognition of objects belonging to those categories. The recognition unit can also prioritize the recognition of relevant locations based on information about places the user has visited in the past. For example, the recognition unit can analyze images previously taken by the user and prioritize the recognition of similar buildings and landmarks. For example, the recognition unit can identify categories of interest from the user's past shooting history and prioritize the recognition of objects belonging to those categories. This allows for improved recognition accuracy by referring to the user's past shooting history. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's past shooting history data into a generative AI and have the generative AI perform the improvement of recognition accuracy.

[0078] The recognition unit can optimize its recognition algorithm according to the weather and time of day during recognition. For example, in sunny weather, the recognition unit will prioritize recognizing outdoor tourist attractions. For example, at night, the recognition unit can prioritize recognizing illuminated buildings and landmarks. Furthermore, in rainy weather, the recognition unit can prioritize recognizing indoor tourist attractions and museums. For example, in sunny weather, the recognition unit will prioritize recognizing outdoor tourist attractions. Furthermore, at night, the recognition unit can prioritize recognizing illuminated buildings and landmarks. By optimizing the recognition algorithm according to the weather and time of day, recognition accuracy can be improved. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input weather and time-of-day data into a generative AI and have the generative AI perform the optimization of the recognition algorithm.

[0079] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated emotions. For example, the recognition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the recognition unit can record the user's voice and estimate emotions using voice analysis technology. The recognition unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the recognition unit can calculate an emotion score based on changes in the user's facial expressions and adjust the display method of the recognition results. The recognition unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the display method of the recognition results. By adjusting the display method of the recognition results based on the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the recognition results.

[0080] The recognition unit can prioritize the selection of recognition targets by considering the user's geographical location information during recognition. For example, if the user is in a tourist area, the recognition unit can prioritize the recognition of major tourist spots. For example, if the user is in a shopping area, the recognition unit can prioritize the recognition of popular stores and shopping malls. Also, if the user is in a nature park, the recognition unit can prioritize the recognition of scenic spots and hiking trails. For example, if the user is in a tourist area, the recognition unit can prioritize the recognition of major tourist spots. Also, if the user is in a shopping area, the recognition unit can prioritize the recognition of popular stores and shopping malls. By prioritizing the selection of recognition targets by considering the user's geographical location information, the recognition unit can prioritize the provision of information that is important to the user. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recognition unit can input the user's geographical location information into a generative AI and have the generative AI perform the priority selection of recognition targets.

[0081] The recognition unit can analyze the user's social media activity and recognize relevant information during recognition. For example, the recognition unit can recognize relevant tourist spots based on photos shared by the user on social media. For example, the recognition unit can analyze the content of posts from accounts the user follows on social media and recognize relevant places. The recognition unit can also recognize relevant places based on information about places the user has checked into on social media. For example, the recognition unit can recognize relevant tourist spots based on photos shared by the user on social media. For example, the recognition unit can analyze the content of posts from accounts the user follows on social media and recognize relevant places. In this way, relevant information can be recognized by analyzing the user's social media activity. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's social media activity data into a generative AI and have the generative AI perform the recognition of relevant information.

[0082] The explanation unit can estimate the user's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, the explanation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the explanation unit can record the user's voice and estimate their emotions using voice analysis technology. The explanation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the explanation unit can calculate an emotion score based on changes in the user's facial expressions and adjust the way the explanation is presented. The explanation unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the way the explanation is presented. This allows for explanations that are easier for the user to understand by adjusting the way the explanation is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the explanation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the explanation unit can input user emotion data into a generative AI and have the generative AI adjust the way the explanation is expressed.

[0083] The explanation unit can adjust the level of detail in its explanation based on the importance of the recognized object. For example, it can provide detailed historical and cultural background information for major tourist attractions. For example, it can provide a concise explanation for general buildings. The explanation unit can also provide detailed information related to specific themes depending on the user's interests. For example, it can provide detailed historical and cultural background information for major tourist attractions. For example, it can provide a concise explanation for general buildings. This allows the explanation unit to provide detailed information that is important to the user by adjusting the level of detail in its explanation based on the importance of the recognized object. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the explanation unit can input importance data of the recognized object into a generative AI and have the generative AI perform the adjustment of the level of detail in the explanation.

[0084] The explanatory unit can apply different explanatory algorithms depending on the category of the subject during the explanation process. For example, for historical buildings, the explanatory unit can provide explanations focusing on historical background and architectural style. For natural landscapes, the explanatory unit can provide explanations regarding geological features and ecosystems. Furthermore, for modern architecture, the explanatory unit can provide explanations regarding the designer and the intent behind the building. For example, for historical buildings, the explanatory unit can provide explanations focusing on historical background and architectural style. Furthermore, for natural landscapes, the explanatory unit can provide explanations regarding geological features and ecosystems. This allows for more appropriate explanations by applying different explanatory algorithms depending on the category of the subject. Some or all of the above processing in the explanatory unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the explanatory unit can input the target category data into a generative AI and have the generative AI execute the application of the explanatory algorithm.

[0085] The explanation unit can estimate the user's emotions and adjust the length of the explanation based on the estimated emotions. For example, the explanation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the explanation unit can record the user's voice and estimate the emotions using voice analysis technology. The explanation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, the explanation unit can calculate an emotion score based on changes in the user's facial expressions and adjust the length of the explanation. The explanation unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the length of the explanation. This makes it possible to provide an explanation of an appropriate length for the user by adjusting the length of the explanation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the explanation unit may be performed using a generative AI, for example, or without a generative AI. For example, the explanation unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the explanation.

[0086] The explanation unit can determine the priority of explanations based on the historical background of the subject. For example, the explanation unit will prioritize explaining places related to important historical events. For example, the explanation unit can prioritize explaining places related to specific historical themes according to the user's interests. The explanation unit can also provide detailed explanations for places with rich historical backgrounds. For example, the explanation unit will prioritize explaining places related to important historical events. For example, the explanation unit can prioritize explaining places related to specific historical themes according to the user's interests. By determining the priority of explanations based on the historical background of the subject, information important to the user can be explained preferentially. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the explanation unit can input the historical background data of the subject into a generative AI and have the generative AI perform the determination of the explanation priority.

[0087] The explanatory unit can improve the accuracy of its explanations by referring to relevant literature during the explanation process. For example, the explanatory unit can provide accurate information by referring to academic papers and books related to the content being explained. For example, the explanatory unit can provide detailed information by referring to historical documents related to the content being explained. Furthermore, the explanatory unit can provide up-to-date information by referring to the latest research findings related to the content being explained. For example, the explanatory unit can provide accurate information by referring to academic papers and books related to the content being explained. Furthermore, the explanatory unit can provide detailed information by referring to historical documents related to the content being explained. In this way, the accuracy of the explanation can be improved by referring to relevant literature. Some or all of the above processing in the explanatory unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanatory unit can input the relevant literature data into a generative AI and have the generative AI perform the improvement of the accuracy of the explanation.

[0088] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the proposal unit can record the user's voice and estimate the emotions using voice analysis technology. The proposal unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in the user's facial expressions and adjust the way the proposal is presented. The proposal unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the way the proposal is presented. By adjusting the way the proposal is presented based on the user's emotions, it becomes possible to make proposals that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal section may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal section can input user emotion data into a generative AI and have the generative AI adjust the way the proposal is expressed.

[0089] The suggestion unit can propose the optimal route by referring to the user's past travel history when making a suggestion. For example, the suggestion unit can propose the optimal route based on routes the user has used in the past. For example, the suggestion unit can propose a route that avoids congestion based on the user's past travel history. The suggestion unit can also analyze the user's past travel history and propose the most efficient route. For example, the suggestion unit can propose the optimal route based on routes the user has used in the past. For example, the suggestion unit can propose a route that avoids congestion based on the user's past travel history. In this way, the optimal route can be proposed by referring to the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past travel history data into a generative AI and have the generative AI execute the optimal route proposal.

[0090] The suggestion unit can adjust the route when making a suggestion, taking into account the user's current physical condition and fatigue level. For example, if the user is tired, the suggestion unit will suggest the shortest route. For example, if the user is seeking healthy exercise, the suggestion unit can suggest a slightly longer route. Also, if the user is feeling unwell, the suggestion unit can suggest a route that includes rest stops. For example, if the user is tired, the suggestion unit will suggest the shortest route. Also, if the user is seeking healthy exercise, the suggestion unit can suggest a slightly longer route. In this way, by taking into account the user's current physical condition and fatigue level, the optimal route can be suggested. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's physical condition and fatigue level data into a generative AI and have the generative AI perform the route adjustment.

[0091] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can record the user's voice and estimate their emotions using voice analysis technology. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expressions and determine the priority of suggestions. The suggestion unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and determine the priority of suggestions. This allows for optimal suggestions for the user by determining the priority of suggestions based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI determine the priority of proposals.

[0092] The suggestion unit can propose the optimal route by considering the user's geographical location information. For example, if the user is in a tourist area, the suggestion unit can propose a route that visits major tourist spots. For example, if the user is in a shopping area, the suggestion unit can propose a route that visits popular stores. Also, if the user is in a nature park, the suggestion unit can propose a scenic hiking trail. For example, if the suggestion unit is in a tourist area, the suggestion unit can propose a route that visits major tourist spots. Also, if the user is in a shopping area, the suggestion unit can propose a route that visits popular stores. In this way, the optimal route can be proposed by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal route.

[0093] The suggestion unit can analyze the user's social media activity and propose relevant routes when making suggestions. For example, the suggestion unit can propose a route visiting relevant tourist spots based on photos the user has shared on social media. For example, the suggestion unit can analyze the content of posts from accounts the user follows on social media and propose a route visiting relevant places. The suggestion unit can also propose relevant routes based on information about places the user has checked into on social media. For example, the suggestion unit can propose a route visiting relevant tourist spots based on photos the user has shared on social media. For example, the suggestion unit can analyze the content of posts from accounts the user follows on social media and propose a route visiting relevant places. In this way, relevant routes can be proposed by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI and have the generative AI propose relevant routes.

[0094] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, the translation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the translation unit can record the user's voice and estimate their emotions using voice analysis technology. The translation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the translation unit can calculate an emotion score based on changes in the user's facial expressions and adjust the translation's expression. The translation unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the translation's expression. This allows for translations that are easier for the user to understand by adjusting the translation's expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the translation unit can input user sentiment data into a generative AI and have the generative AI adjust the expression of the translation.

[0095] The translation unit can improve translation accuracy by referring to the user's past translation history during translation. For example, the translation unit can prioritize translating similar phrases based on phrases the user has translated in the past. For example, the translation unit can improve translation accuracy for specific language pairs based on the user's past translation history. The translation unit can also provide more natural translations by considering the context in which the user has translated in the past. For example, the translation unit can prioritize translating similar phrases based on phrases the user has translated in the past. The translation unit can also improve translation accuracy for specific language pairs based on the user's past translation history. This allows for improved translation accuracy by referring to the user's past translation history. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input the user's past translation history data into a generative AI and have the generative AI perform the translation accuracy improvement.

[0096] The translation unit can optimize its translation algorithm by considering local dialects and slang during translation. For example, the translation unit can provide more natural translations by considering local dialects. For example, the translation unit can provide appropriate translations by considering local slang. Furthermore, the translation unit can provide contextually appropriate translations by considering the local cultural background. For example, the translation unit can provide more natural translations by considering local dialects. Furthermore, the translation unit can provide appropriate translations by considering local slang. This makes more natural translations possible by considering local dialects and slang. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input local dialect and slang data into a generative AI and have the generative AI perform the optimization of the translation algorithm.

[0097] The translation unit can estimate the user's emotions and adjust the display method of the translation results based on the estimated emotions. For example, the translation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the translation unit can record the user's voice and estimate emotions using voice analysis technology. The translation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the translation unit can calculate an emotion score based on changes in the user's facial expressions and adjust the display method of the translation results. The translation unit can also analyze the tone and speed of the user's voice, calculate an emotion score, and adjust the display method of the translation results. This allows for a user-friendly display by adjusting the display method of the translation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the translation unit can input user sentiment data into the generative AI and have the generative AI adjust how the translation results are displayed.

[0098] The translation unit can prioritize the selection of translation targets by considering the user's geographical location during translation. For example, if the user is in a tourist area, the translation unit will prioritize translating signs and menus related to tourist attractions. For example, if the user is in a shopping area, the translation unit can prioritize translating store signs and product descriptions. Also, if the user is in a restaurant, the translation unit can prioritize translating menus. For example, if the user is in a tourist area, the translation unit will prioritize translating signs and menus related to tourist attractions. Also, if the user is in a shopping area, the translation unit can prioritize translating store signs and product descriptions. This allows for the prioritization of translation targets by considering the user's geographical location. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the translation unit can input the user's geographical location information into a generative AI and have the generative AI perform the priority selection of translation targets.

[0099] The translation unit can analyze the user's social media activity and translate relevant information during the translation process. For example, the translation unit can translate relevant signs and menus based on photos shared by the user on social media. For example, the translation unit can analyze the content of posts from accounts the user follows on social media and translate relevant information. The translation unit can also translate relevant signs and menus based on information about places the user has checked into on social media. For example, the translation unit can translate relevant signs and menus based on photos shared by the user on social media. For example, the translation unit can analyze the content of posts from accounts the user follows on social media and translate relevant information. This allows the translation of relevant information by analyzing the user's social media activity. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input the user's social media activity data into a generative AI and have the generative AI perform the translation of relevant information.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the suggestion function estimates that the user is tired, it can suggest taking a break earlier. If the suggestion function estimates that the user is excited, it can proactively suggest new tourist attractions. Furthermore, if the suggestion function estimates that the user is depressed, it can suggest places and activities where the user can relax. This enables suggestions to be made at the appropriate time according to the user's emotions, thereby improving the user's travel experience.

[0102] The recognition unit can adjust the priority of recognition targets by referring to the user's past travel history. For example, it can prioritize recognizing places of similar interest based on information about tourist spots the user has visited in the past. It can also analyze photos of places the user has visited in the past and prioritize recognizing similar landscapes and buildings. Furthermore, it can refer to reviews and ratings of places the user has visited in the past and prioritize recognizing places with high ratings. This makes it possible to provide more personalized information by utilizing the user's past travel history.

[0103] The explanatory unit can estimate the user's emotions and adjust the tone of the explanation based on those estimates. For example, if the user is estimated to be excited, the explanatory unit can explain in a more lively tone. If the user is estimated to be relaxed, the explanatory unit can explain in a calm tone. Furthermore, if the user is estimated to be anxious, the explanatory unit can explain in a reassuring tone. This allows for explanations in an appropriate tone according to the user's emotions, thereby deepening the user's understanding.

[0104] The suggestion function can monitor the user's current activity in real time and adjust its suggestions accordingly. For example, if the user is walking, the suggestion function can suggest sightseeing spots accessible on foot. If the user is driving, the suggestion function can suggest routes and sightseeing spots suitable for driving. Furthermore, if the user is taking a break, the suggestion function can suggest nearby cafes and rest spots. This enables appropriate suggestions tailored to the user's current activity, thereby improving the user's travel experience.

[0105] The translation unit can estimate the user's emotions and adjust the level of detail in the translation based on that estimation. For example, if the unit estimates the user is in a hurry, it can provide a concise translation. If the unit estimates the user is relaxed, it can provide a detailed translation. Furthermore, if the unit estimates the user is confused, it can provide an easy-to-understand translation. This enables translations with an appropriate level of detail that matches the user's emotions, leading to a deeper understanding of the message.

[0106] The recognition unit can adjust the priority of recognition targets by referring to the user's current weather information. For example, on sunny days, it can prioritize the recognition of outdoor tourist attractions. On rainy days, it can prioritize the recognition of indoor tourist attractions and museums. Furthermore, on snowy days, it can prioritize the recognition of ski resorts and winter activities. This enables appropriate recognition according to the user's current weather information, improving the user's travel experience.

[0107] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those estimates. For example, if the suggestion function estimates the user to be excited, it can suggest active activities or events. If the suggestion function estimates the user to be relaxed, it can suggest relaxing places or activities. Furthermore, if the suggestion function estimates the user to be anxious, it can suggest places or activities that provide a sense of security. This enables the system to provide appropriate suggestions tailored to the user's emotions, thereby improving the user's travel experience.

[0108] The recognition unit can adjust the priority of recognition targets by referring to the user's current location information. For example, if the user is in a tourist area, it can prioritize recognizing major tourist spots. If the user is in a shopping area, it can prioritize recognizing popular stores and shopping malls. Furthermore, if the user is in a nature park, it can prioritize recognizing scenic spots and hiking trails. This enables appropriate recognition based on the user's current location information, improving the user's travel experience.

[0109] The explanatory unit can estimate the user's emotions and adjust the content of the explanation based on those estimates. For example, if the user is estimated to be excited, the explanatory unit can provide an explanation that includes many interesting facts and anecdotes. If the user is estimated to be relaxed, the explanatory unit can provide a calm explanation. Furthermore, if the user is estimated to be anxious, the explanatory unit can provide a reassuring explanation. This allows for explanations tailored to the user's emotions, thereby deepening the user's understanding.

[0110] The suggestion function can monitor the user's current health status and adjust its suggestions accordingly. For example, if the user is tired, the suggestion function can suggest a rest. If the user is seeking healthy exercise, the suggestion function can suggest walking or hiking trails. Furthermore, if the user is feeling unwell, the suggestion function can suggest nearby medical facilities or pharmacies. This enables appropriate suggestions tailored to the user's current health condition, thereby improving the user's travel experience.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The recognition unit analyzes the image captured by the user to recognize the surrounding scenery and buildings. For example, it uses image analysis algorithms and deep learning technology to extract features from the image and identify buildings and landmarks. It can also analyze the image in real time to recognize the surrounding scenery and buildings. Step 2: The explanation unit explains the history and cultural background based on the information recognized by the recognition unit. For example, it refers to a pre-prepared database and uses natural language processing and speech synthesis technologies to explain the history and cultural background based on the recognized information. Step 3: The suggestion unit proposes the optimal route based on the information obtained by the explanation unit, taking into account the user's current location and destination. For example, it may use an algorithm that calculates a route that allows the user to travel efficiently by referring to traffic information and map data. It can also propose a personalized route by considering the user's interests and past travel history. Step 4: The translation unit translates local signs and menus in real time and reads them aloud, based on the route proposed by the proposal unit. For example, it analyzes images of signs and menus taken by the user, extracts the text, and translates it into the user's native language using machine translation technology. It can also read the translated text aloud using speech synthesis technology.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the recognition unit, explanation unit, proposal unit, and translation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit analyzes images taken by the user using the camera 42 of the smart device 14 to recognize buildings and landmarks. The explanation unit is implemented in the specific processing unit 290 of the data processing unit 12 and explains the history and cultural background based on the recognized information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal route considering the user's current location and destination. The translation unit is implemented in the control unit 46A of the smart device 14 and translates local signs and menus in real time and reads them aloud. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the recognition unit, explanation unit, suggestion unit, and translation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit analyzes images taken by the user using the camera 42 of the smart glasses 214 to recognize buildings and landmarks. The explanation unit is implemented in the specific processing unit 290 of the data processing unit 12 and explains the history and cultural background based on the recognized information. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal route considering the user's current location and destination. The translation unit is implemented in the control unit 46A of the smart glasses 214 and translates local signs and menus in real time and reads them aloud. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the recognition unit, explanation unit, suggestion unit, and translation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit analyzes images taken by the user using the camera 42 of the headset terminal 314 to recognize buildings and landmarks. The explanation unit is implemented in the specific processing unit 290 of the data processing unit 12 and explains the history and cultural background based on the recognized information. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal route considering the user's current location and destination. The translation unit is implemented in the control unit 46A of the headset terminal 314 and translates local signs and menus in real time and reads them aloud. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the recognition unit, explanation unit, proposal unit, and translation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit analyzes images taken by the user using the camera 42 of the robot 414 to recognize buildings and landmarks. The explanation unit is implemented in the specific processing unit 290 of the data processing unit 12 and explains the history and cultural background based on the recognized information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal route considering the user's current location and destination. The translation unit is implemented in the control unit 46A of the robot 414 and translates local signs and menus in real time and reads them aloud. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A recognition unit analyzes images taken by the user to recognize the surrounding scenery and buildings, Based on the information recognized by the aforementioned recognition unit, an explanatory unit explains the historical and cultural background, Based on the information obtained by the explanatory unit, a proposal unit proposes the optimal route considering the user's current location and destination. The system includes a translation unit that translates local signs and menus in real time and reads them aloud, based on the route proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned recognition unit, It analyzes images taken by the user to recognize buildings and landmarks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The above explanatory section is, This section describes the history and cultural background of buildings and landmarks recognized by the recognition unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, It suggests the optimal route considering the user's current location and destination. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned translation department, It translates local signs and menus in real time and reads them aloud. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose personalized itineraries based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recognition unit, It recognizes the user's real-time environment and provides optimal information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, It estimates the user's emotions and adjusts the priority of what to recognize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, During recognition, the system improves recognition accuracy by referencing the user's past shooting history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, During recognition, the recognition algorithm is optimized according to weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, During recognition, the system prioritizes selecting recognition targets by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, During recognition, the system analyzes the user's social media activity and identifies relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The above explanatory section is, It estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The above explanatory section is, When explaining, adjust the level of detail in the explanation based on the perceived importance of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 16) The above explanatory section is, When providing explanations, different explanation algorithms are applied depending on the target category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The above explanatory section is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The above explanatory section is, When explaining, prioritize the explanation based on the historical background of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 19) The above explanatory section is, When explaining, refer to relevant literature to improve the accuracy of the explanation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, the system refers to the user's past travel history to propose the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a suggestion, we adjust the route considering the user's current physical condition and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, the system will suggest the optimal route considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned translation department, During translation, the system improves translation accuracy by referencing the user's past translation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned translation department, During translation, the translation algorithm is optimized to take into account local dialects and slang. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned translation department, It estimates the user's emotions and adjusts how the translation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned translation department, During translation, the system prioritizes selecting translation targets based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned translation department, During translation, the system analyzes the user's social media activity and translates relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit analyzes images taken by the user to recognize the surrounding scenery and buildings, Based on the information recognized by the aforementioned recognition unit, an explanatory unit explains the historical and cultural background, Based on the information obtained by the explanatory unit, a proposal unit proposes the optimal route considering the user's current location and destination. The system includes a translation unit that translates local signs and menus in real time and reads them aloud, based on the route proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned recognition unit, It analyzes images taken by the user to recognize buildings and landmarks. The system according to feature 1.

3. The above explanatory section is, This section explains the history and cultural background of buildings and landmarks recognized by the aforementioned recognition unit. The system according to feature 1.

4. The aforementioned proposal section is, It suggests the optimal route considering the user's current location and destination. The system according to feature 1.

5. The aforementioned translation department, It translates local signs and menus in real time and reads them aloud. The system according to feature 1.

6. The aforementioned proposal section is, We propose personalized itineraries based on the user's interests. The system according to feature 1.

7. The aforementioned recognition unit, It recognizes the user's real-time environment and provides optimal information. The system according to feature 1.

8. The aforementioned recognition unit, It estimates the user's emotions and adjusts the priority of what to recognize based on the estimated user emotions. The system according to feature 1.

9. The aforementioned recognition unit, During recognition, the system improves recognition accuracy by referencing the user's past shooting history. The system according to feature 1.

10. The aforementioned recognition unit, During recognition, the recognition algorithm is optimized according to weather and time of day. The system according to feature 1.

Images