system

Abstract

The system according to this embodiment aims to analyze photographs taken by tourists and provide relevant tourist information. [Solution] The system according to the embodiment comprises an analysis unit, an acquisition unit, a provision unit, and an interface unit. The analysis unit analyzes photographs taken by tourists. The acquisition unit acquires location information based on the information analyzed by the analysis unit. The provision unit provides tourist information based on the location information acquired by the acquisition unit. The interface unit provides an interface for tourists to use the system.

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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, obtaining location information from photos taken by tourists and providing related tourism information have not been sufficiently carried out, and there is room for improvement. [[ID=3**********]]

[0005] The system according to the embodiment aims to analyze photos taken by tourists and provide related tourism information. [[ID=4**********]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an acquisition unit, a provision unit, and an interface unit. The analysis unit analyzes photographs taken by tourists. The acquisition unit acquires location information based on the information analyzed by the analysis unit. The provision unit provides tourist information based on the location information acquired by the acquisition unit. The interface unit provides an interface for tourists to use the system. [Effects of the Invention]

[0007] The system according to this embodiment can analyze photographs taken by tourists and provide relevant tourist information. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages 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 tourism information provision system according to an embodiment of the present invention is a system that analyzes photographs taken by tourists and provides detailed information about the location and subject of the photograph in real time. For example, a tourist takes a photograph with a smartphone or audio glasses. Next, the tourism information provision system uses AI to analyze the photograph and identifies the subject of the photograph using location information and image recognition technology. For example, if a tourist takes a photograph of a historical building, the tourism information provision system provides the building's name, historical background, and related tourism information. Furthermore, the tourism information provision system enriches the tourist experience by providing information about the places visited in real time. For example, if a tourist takes a photograph of a painting in an art museum, the tourism information provision system provides information about the artist, the date of creation, and a description of the work. Also, if a tourist takes a photograph of a landscape in a nature park, the tourism information provision system provides information about the natural environment and flora and fauna of that location. In addition, this tourism information provision system is multilingual, allowing tourists to receive information in their own language. For example, if a Japanese-speaking tourist visits a tourist destination in France, the tourist information system will provide information about that place in Japanese. This allows tourists to access tourist information despite language barriers. In this way, the tourist information system analyzes photos taken by tourists and provides detailed information about the place and the subject of the photo in real time, thereby improving the tourist experience. In this way, the tourist information system can enhance the tourist experience.

[0029] The tourism information provision system according to this embodiment comprises an analysis unit, an acquisition unit, a provision unit, and an interface unit. The analysis unit analyzes photographs taken by tourists. The analysis unit can, for example, analyze photographs using AI and identify the subject of the photographs using image recognition technology. For example, the analysis unit can analyze the content of photographs using deep learning technology. The analysis unit can also identify the subject of photographs using pattern recognition technology. Furthermore, the analysis unit can extract detailed information from photographs using image analysis algorithms. The acquisition unit acquires location information based on the information analyzed by the analysis unit. The acquisition unit can acquire location information using, for example, GPS technology. The acquisition unit can also acquire location information using Wi-Fi location information technology. Furthermore, the acquisition unit can also acquire location information using beacon technology. The provision unit provides tourism information based on the location information acquired by the acquisition unit. The provision unit can, for example, provide descriptions of tourist destinations and event information. Furthermore, the provision unit can also provide the historical background of tourist destinations and related tourism information. Furthermore, the provision unit can also provide information on the natural environment and flora and fauna of tourist destinations. The interface unit provides an interface for tourists to use the system. For example, the interface unit can provide an interface for a smartphone application or audio glasses. Furthermore, the interface unit can provide a user interface to allow tourists to easily operate the system. In addition, the interface unit can provide a multilingual interface. As a result, the tourism information provision system according to this embodiment can improve the tourist experience.

[0030] The analysis unit analyzes photographs taken by tourists. For example, it uses AI to analyze photos and image recognition technology to identify the subject of the photograph. Specifically, it utilizes deep learning technology to recognize objects and scenes within photographs with high accuracy. For instance, it automatically identifies buildings, landscapes, and people in photographs taken by tourists and extracts their respective features. The deep learning model is pre-trained on a large dataset of tourist destination photographs, achieving high recognition accuracy. It can also identify the subject of a photograph using pattern recognition technology. For example, it can identify specific architectural styles or patterns in natural landscapes and use them to determine the subject of the photograph. Furthermore, it can extract detailed information from photographs using image analysis algorithms. For example, it can extract text information from photographs and automatically recognize the names and descriptions of tourist destinations. As a result, the analysis unit can extract diverse information from photographs taken by tourists with high accuracy and provide the data necessary for subsequent processing.

[0031] The acquisition unit acquires location information based on the information analyzed by the analysis unit. The acquisition unit can acquire location information using, for example, GPS technology. Specifically, it uses the GPS module built into the tourist's smartphone or camera to determine the exact location where the photograph was taken. It can also acquire location information using Wi-Fi location information technology. This method estimates the tourist's location based on the signal strength of Wi-Fi access points within the tourist area. Furthermore, it is also possible to acquire location information using beacon technology. It receives signals transmitted from beacons installed within the tourist area and determines the location based on the signal strength and arrival time. As a result, the acquisition unit can acquire highly accurate location information by combining various technologies, and accurately understand the places and routes that tourists have visited. Furthermore, by adjusting the frequency and accuracy of location information acquisition, the acquisition unit can efficiently collect necessary information while protecting the privacy of tourists.

[0032] The information provider unit provides tourist information based on location data acquired by the information acquisition unit. For example, the information provider unit can provide descriptions of tourist destinations and event information. Specifically, it can provide detailed explanations of the history, culture, and attractions of tourist destinations, enabling tourists to gain a deeper understanding of the place. It can also provide real-time information on currently running events and activities, suggesting experiences that tourists can enjoy on the spot. Furthermore, the information provider unit can also provide the historical background and related tourist information of tourist destinations. For example, it can provide information on the historical significance of specific buildings or ruins, or information on past events, thereby attracting tourists' interest and providing learning opportunities. It can also provide information on the natural environment and flora and fauna of tourist destinations. For example, it can provide information on the characteristics of flora and fauna inhabiting specific areas, or on seasonal changes in nature, helping tourists enjoy the beauty of nature. In this way, the information provider unit can provide tourists with diverse and abundant information, enriching their tourist experience.

[0033] The interface unit provides an interface for tourists to use the system. For example, it can provide interfaces for smartphone applications and audio glasses. Specifically, it allows tourists to easily access tourist information through smartphone applications. The applications feature an intuitive user interface, enabling users to search for information on tourist destinations and view recommended spots based on their current location. Furthermore, using audio glasses, tourists can obtain information about tourist destinations not only visually but also through audio guides. The interface unit can also provide a user interface that allows tourists to easily operate the system. For example, using voice recognition technology, tourists can search for information and give instructions by voice. The interface unit can also provide a multilingual interface. This allows tourists who speak different languages ​​to use the system without experiencing language barriers. In this way, the interface unit provides tourists with an easy-to-use and accessible interface, improving the tourist experience.

[0034] The analysis unit can identify the subject of a photograph using image recognition technology. For example, the analysis unit can analyze the content of a photograph using deep learning technology. For instance, it can use a convolutional neural network (CNN) to extract features from the photograph and identify the subject. Furthermore, the analysis unit can also identify the subject of a photograph using pattern recognition technology. For example, it can use a support vector machine (SVM) to classify patterns in the photograph and identify the subject. In addition, the analysis unit can extract detailed information from a photograph using image analysis algorithms. For example, it can use an edge detection algorithm to extract contours from the photograph and identify the subject. This allows for accurate identification of the subject of a photograph using image recognition technology.

[0035] The acquisition unit can acquire location information using GPS or other location information technologies. For example, the acquisition unit can acquire location information using GPS technology. For example, the acquisition unit receives signals from GPS satellites and determines the tourist's current location. The acquisition unit can also acquire location information using Wi-Fi location information technology. For example, the acquisition unit measures the signal strength of surrounding Wi-Fi access points and determines the location. Furthermore, the acquisition unit can also acquire location information using beacon technology. For example, the acquisition unit receives signals from beacons and determines the tourist's location. This allows for the acquisition of accurate location information using GPS or other location information technologies. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input GPS signals to AI, which can then analyze and output location information.

[0036] The information provider can provide tourists with real-time tourist information. For example, it can provide real-time descriptions of tourist destinations and event information. For example, it can provide real-time information on the historical background of tourist destinations and related tourist information. The information provider can also provide real-time information on the natural environment and flora and fauna of tourist destinations. For example, it can provide real-time information on the types and ecology of flora and fauna of tourist destinations. Furthermore, the information provider can provide real-time information on the latest events and activities at tourist destinations. For example, it can provide real-time information on the schedule and how to participate in events held at tourist destinations. By providing tourist information in real time, the tourist experience can be improved. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information on tourist destinations into AI, and the AI ​​can analyze and output the information in real time.

[0037] The interface unit can make the system easy for tourists to use. The interface unit provides interfaces for, for example, smartphone applications and audio glasses. For instance, the interface unit allows tourists to operate the system using their smartphones. It can also allow tourists to operate the system using audio glasses. Furthermore, the interface unit can provide a user interface to make the system easy for tourists to operate. For example, the interface unit provides an intuitive and user-friendly user interface. This improves usability by making the system easy for tourists to use. Some or all of the above processing in the interface unit may be performed using, for example, AI, or not. For example, the interface unit can input tourist operation data into the AI, which can then generate an optimal user interface.

[0038] The information provider can provide tourist information in multiple languages. For example, it can provide descriptions of tourist destinations and event information in multiple languages. For example, it can provide information about tourist destinations in multiple languages ​​such as Japanese, English, and French. The information provider can also provide the historical background and related tourist information of tourist destinations in multiple languages. For example, it can provide the historical background of tourist destinations in multiple languages ​​such as Japanese, English, and French. Furthermore, the information provider can provide information about the natural environment and flora and fauna of tourist destinations in multiple languages. For example, it can provide information about the types and ecology of flora and fauna of tourist destinations in multiple languages ​​such as Japanese, English, and French. By providing tourist information in multiple languages, it becomes possible to obtain tourist information over language barriers. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information about tourist destinations into AI, and the AI ​​can translate and output it in multiple languages.

[0039] The analysis unit can improve analysis accuracy by considering the time of day the photograph was taken and weather information. For example, in the case of a photograph taken at night, the analysis unit uses an analysis algorithm that is compatible with low-light environments. For example, the analysis unit analyzes the photograph using a deep learning model that is robust in low-light environments. The analysis unit can also perform image processing to remove raindrops and fog in the case of a photograph taken in rainy weather. For example, the analysis unit removes raindrops and fog using image filtering technology. Furthermore, the analysis unit can perform corrections to minimize the effect of shadows in the case of a photograph taken in sunny weather. For example, the analysis unit reduces the effect of shadows using an image correction algorithm. This improves analysis accuracy by considering the time of day the photograph was taken and weather information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the metadata of the photograph into AI, and the AI ​​can analyze and output the time of day the photograph was taken and weather information.

[0040] The analysis unit can identify themes by referring to the tourist's past visit history. For example, the analysis unit can prioritize identifying similar themes based on data of places the tourist has visited in the past. For example, the analysis unit can use the tourist's visit history data to identify themes of tourist destinations similar to places visited in the past. The analysis unit can also identify themes related to specific themes from the tourist's visit history. For example, the analysis unit can identify themes of related works of art based on data of museums the tourist has visited in the past. Furthermore, the analysis unit can prioritize analyzing relevant information based on themes the tourist has shown interest in in the past. For example, the analysis unit can identify themes of related buildings based on data of historical buildings the tourist has shown interest in in the past. This improves the accuracy of theme identification by referring to past visit history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the tourist's visit history data into AI, and the AI ​​can identify and output themes.

[0041] The analysis unit can analyze tourists' social media activity and supplement relevant information. For example, the analysis unit can provide relevant tourist information based on photos shared by tourists on social media. For example, the analysis unit can analyze the content of photos shared by tourists and provide information on relevant tourist destinations. The analysis unit can also analyze the content of tourists' social media posts and provide information that may be of interest to them. For example, the analysis unit can identify themes of interest from tourists' posts and provide relevant tourist information. Furthermore, the analysis unit can provide relevant tourist information based on information about accounts that tourists follow. For example, the analysis unit can provide the latest tourist information based on information about official accounts of tourist destinations that tourists follow. In this way, relevant information can be supplemented by analyzing social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourists' social media data into AI, and the AI ​​can analyze and output relevant information.

[0042] The analysis unit can select the optimal analysis algorithm by considering the tourist's device information. For example, if a high-performance device is being used, the analysis unit will select an algorithm that performs detailed analysis. For example, the analysis unit will utilize the processing power of the high-performance device to perform detailed analysis using a deep learning model. The analysis unit can also select a lightweight analysis algorithm if a low-performance device is being used. For example, the analysis unit will use a simple image analysis algorithm that matches the processing power of the low-performance device. Furthermore, the analysis unit can also select the optimal analysis algorithm by considering the device's battery level. For example, if the battery level is low, the analysis unit will select a lightweight analysis algorithm to conserve battery power. In this way, the optimal analysis algorithm can be selected by considering the device information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the tourist's device information into AI, and the AI ​​can select and output the optimal analysis algorithm.

[0043] The acquisition unit can select the optimal acquisition method by referring to the tourist's past location information history. For example, the acquisition unit can select the optimal location information acquisition method based on data of places the tourist has visited in the past. For example, the acquisition unit can select the optimal location information acquisition method using the tourist's past location information history. The acquisition unit can also select a location information acquisition method for a specific area based on the tourist's visit history. For example, the acquisition unit can select a location information acquisition method for a specific area based on the tourist's visit history. Furthermore, the acquisition unit can also select a method for acquiring detailed location information based on areas the tourist has shown interest in in the past. For example, the acquisition unit can select a method for acquiring detailed location information based on the tourist's visit history. In this way, the optimal acquisition method can be selected by referring to past location information history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the tourist's past location information history into AI, and the AI ​​can select and output the optimal acquisition method.

[0044] The acquisition unit can correct location information by considering the tourist's current speed and direction of movement. For example, if the tourist is moving at high speed, the acquisition unit frequently updates the location information to provide accurate information. For example, the acquisition unit increases the frequency of location information updates based on the tourist's speed data. Also, if the tourist is moving slowly, the acquisition unit can reduce the frequency of location information updates to conserve battery power. For example, the acquisition unit adjusts the frequency of location information updates based on the tourist's speed data. Furthermore, the acquisition unit can also correct the location information by considering the tourist's direction of movement to provide accurate information. For example, the acquisition unit corrects the location information based on the tourist's direction of movement data. This improves the accuracy of location information by considering speed and direction of movement. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the tourist's speed and direction data into AI, and the AI ​​can correct and output the location information.

[0045] The acquisition unit can prioritize the acquisition of highly relevant location information by considering the tourist's geographical location information. For example, if a tourist approaches a specific tourist destination, the acquisition unit will prioritize the acquisition of detailed location information for that tourist destination. For example, based on the tourist's geographical location information, the acquisition unit will prioritize the acquisition of detailed location information for a specific tourist destination. The acquisition unit can also prioritize the acquisition of location information related to a specific area if the tourist is in that area. For example, based on the tourist's geographical location information, the acquisition unit will prioritize the acquisition of location information related to a specific area. Furthermore, if a tourist is following a specific route, the acquisition unit can also prioritize the acquisition of location information related to that route. For example, based on the tourist's geographical location information, the acquisition unit will prioritize the acquisition of location information related to a specific route. In this way, by considering geographical location information, highly relevant location information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the tourist's geographical location information into AI, and the AI ​​can select and output highly relevant location information.

[0046] The acquisition unit can analyze tourists' social media activity and obtain relevant location information. For example, the acquisition unit prioritizes obtaining location information of places shared by tourists on social media. For example, the acquisition unit obtains relevant location information based on the content of tourists' social media posts. The acquisition unit can also analyze the content of tourists' social media posts and obtain relevant location information. For example, the acquisition unit obtains location information of places of interest from the content of tourists' posts. Furthermore, the acquisition unit can obtain relevant location information based on information of accounts that tourists follow. For example, the acquisition unit obtains relevant location information based on information of official accounts of tourist destinations that tourists follow. In this way, relevant location information can be obtained by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input tourists' social media data into AI, and the AI ​​can analyze and output relevant location information.

[0047] The information provider can adjust the level of detail provided based on the importance of the tourist information. For example, the provider can provide detailed information about important tourist destinations. For example, it can provide detailed descriptions and related information about tourist destinations. The provider can also provide concise information about general tourist destinations. For example, it can provide concise descriptions and key points about tourist destinations. Furthermore, the provider can prioritize providing information of high importance according to the tourist's interests. For example, it can provide detailed information about tourist destinations that the tourist has shown interest in. In this way, by adjusting the level of detail based on the importance of the tourist information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input tourist information importance data into AI, and the AI ​​can adjust the level of detail and output the result.

[0048] The information provider can apply different information provision algorithms depending on the category of tourist information. For example, for information about historical tourist destinations, the provider can apply an algorithm that includes detailed historical background information. For example, the provider can provide information about the historical background and related information of the tourist destination. The provider can also apply an algorithm that includes details about flora and fauna for information about the natural environment. For example, the provider can provide information about the types and ecology of flora and fauna of the tourist destination. Furthermore, the provider can apply an algorithm that includes explanations of the artworks for information about art museums and museums. For example, the provider can provide detailed explanations of the artworks in art museums and museums. By applying information provision algorithms according to the category of tourist information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input tourist information category data into AI, and the AI ​​can select and output the optimal information provision algorithm.

[0049] The information provider can determine the priority of information provision based on the timing of submission. For example, the provider may prioritize providing important information immediately after a tourist arrives at a tourist destination. For instance, it may provide a detailed description of the tourist destination and related information. The provider may also provide information about the next destination before a tourist leaves a tourist destination. For example, it may provide information about the tourist destination the tourist plans to visit next. Furthermore, the provider may prioritize providing information about a specific event before the tourist participates in that event. For example, it may provide detailed information about the event the tourist plans to attend. This allows for the provision of more appropriate information by prioritizing based on submission timing. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the provider can input the submission timing data of tourist information into an AI, which can then determine and output the prioritization.

[0050] The information provider can adjust the order of information provided based on the relevance of the tourist information. For example, the provider can prioritize providing information that the tourist has shown interest in. For example, the provider can provide detailed information about tourist destinations that the tourist has shown interest in. The provider can also prioritize providing information about places that the tourist plans to visit. For example, the provider can provide information about tourist destinations that the tourist plans to visit next. Furthermore, the provider can also prioritize providing information related to places that the tourist has visited in the past. For example, the provider can provide information related to tourist destinations that the tourist has visited in the past. By adjusting the order of information provided based on relevance, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input relevance data of tourist information into AI, and the AI ​​can adjust the order of information provision and output it.

[0051] The interface unit can select the optimal display method by referring to the tourist's past operation history when displaying the interface. For example, the interface unit provides the optimal interface based on the display method the tourist has used in the past. For example, the interface unit provides the optimal interface based on the tourist's past operation history. The interface unit can also predict and provide the tourist's preferred display method based on the tourist's operation history. For example, the interface unit predicts the tourist's preferred display method based on the tourist's operation history. Furthermore, the interface unit can prioritize displaying functions that the tourist has frequently used in the past. For example, the interface unit prioritizes displaying functions that the tourist has frequently used based on the tourist's operation history. This allows the optimal display method to be selected by referring to past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the tourist's operation history data into AI, and the AI ​​can select and output the optimal display method.

[0052] The interface unit can select the optimal display method when displaying the interface, taking into account the tourist's device information. For example, if a high-resolution device is being used, the interface unit can provide a detailed display method. For example, the interface unit can utilize the characteristics of a high-resolution device to display detailed graphics and text. The interface unit can also provide a concise display method when a low-resolution device is being used. For example, the interface unit can use a simple design and large fonts to match the characteristics of a low-resolution device. Furthermore, the interface unit can also provide the optimal display method according to the device's screen size. For example, the interface unit can adjust the layout and design considering the device's screen size. This allows the interface unit to select the optimal display method by considering device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the tourist's device information into AI, and the AI ​​can select and output the optimal display method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The tourism information system can identify themes by referring to tourists' past visit history. For example, it can prioritize the identification of similar themes based on data of places tourists have visited in the past. Specifically, it uses tourist visit history data to identify themes of tourist destinations similar to places they have visited in the past. It can also identify themes related to specific themes from tourists' visit history. For example, it can identify themes of related works of art based on data of museums tourists have visited in the past. Furthermore, it can prioritize the analysis of related information based on themes tourists have shown interest in in the past. For example, it can identify themes of related buildings based on data of historical buildings tourists have shown interest in in the past. This improves the accuracy of theme identification by referring to past visit history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourist visit history data into AI, and the AI ​​can identify and output themes.

[0055] The tourism information provision system can analyze tourists' social media activity and supplement relevant information. For example, it can provide relevant tourism information based on photos shared by tourists on social media. Specifically, it analyzes the content of photos shared by tourists and provides information on relevant tourist destinations. It can also analyze the content of tourists' social media posts and provide information that they might be interested in. For example, it can identify themes of interest from tourists' posts and provide relevant tourism information. Furthermore, it can provide relevant tourism information based on information about accounts that tourists follow. For example, it can provide the latest tourism information based on information about official accounts of tourist destinations that tourists follow. In this way, relevant information can be supplemented by analyzing social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourists' social media data into AI, and the AI ​​can analyze and output relevant information.

[0056] The tourism information provision system can prioritize the acquisition of highly relevant location information by considering the geographical location of tourists. For example, when a tourist approaches a specific tourist destination, it can prioritize the acquisition of detailed location information for that destination. Specifically, it prioritizes the acquisition of detailed location information for a specific tourist destination based on the tourist's geographical location information. It can also prioritize the acquisition of location information related to a specific area when a tourist is in that area. For example, it prioritizes the acquisition of location information related to a specific area based on the tourist's geographical location information. Furthermore, when a tourist is following a specific route, it can prioritize the acquisition of location information related to that route. For example, it prioritizes the acquisition of location information related to a specific route based on the tourist's geographical location information. In this way, by considering geographical location information, it is possible to prioritize the acquisition of highly relevant location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the tourist's geographical location information into the AI, and the AI ​​can select and output highly relevant location information.

[0057] The tourism information provision system can select the optimal analysis algorithm by considering the tourist's device information. For example, if a high-performance device is used, an algorithm that performs detailed analysis can be selected. Specifically, a detailed analysis using a deep learning model can be performed by utilizing the processing power of the high-performance device. Conversely, if a low-performance device is used, a lightweight analysis algorithm can be selected. For example, a simple image analysis algorithm can be used to match the processing power of the low-performance device. Furthermore, the system can also select the optimal analysis algorithm by considering the device's battery level. For example, if the battery level is low, a lightweight analysis algorithm can be selected to conserve battery power. In this way, the optimal analysis algorithm can be selected by considering device information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the tourist's device information into the AI, and the AI ​​can select and output the optimal analysis algorithm.

[0058] The tourist information provision system can select the optimal display method by referring to the tourist's past operation history. For example, it can provide the optimal interface based on the display method the tourist has used in the past. Specifically, it provides the optimal interface based on the tourist's past operation history. It can also predict and provide the tourist's preferred display method from their operation history. For example, it predicts the tourist's preferred display method based on their operation history. Furthermore, it can prioritize displaying functions that the tourist has frequently used in the past. For example, it prioritizes displaying functions that the tourist has frequently used based on their operation history. In this way, the optimal display method can be selected by referring to past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the tourist's operation history data into AI, and the AI ​​can select and output the optimal display method.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The analysis unit analyzes the photos taken by tourists. The analysis unit uses AI, deep learning technology, pattern recognition technology, and image analysis algorithms to identify the subject of the photos and extract detailed information. Step 2: The acquisition unit acquires location information based on the information analyzed by the analysis unit. The acquisition unit can acquire location information using GPS technology, Wi-Fi location information technology, and beacon technology. Step 3: The providing unit provides tourist information based on the location information acquired by the acquisition unit. The providing unit can provide descriptions of tourist destinations, event information, historical background, and information on the natural environment and flora and fauna. Step 4: The interface unit provides an interface for tourists to use the system. The interface unit can provide a smartphone application interface, an audio glasses interface, a user interface, and a multilingual interface.

[0061] (Example of form 2) The tourism information provision system according to an embodiment of the present invention is a system that analyzes photographs taken by tourists and provides detailed information about the location and subject of the photograph in real time. For example, a tourist takes a photograph with a smartphone or audio glasses. Next, the tourism information provision system uses AI to analyze the photograph and identifies the subject of the photograph using location information and image recognition technology. For example, if a tourist takes a photograph of a historical building, the tourism information provision system provides the building's name, historical background, and related tourism information. Furthermore, the tourism information provision system enriches the tourist experience by providing information about the places visited in real time. For example, if a tourist takes a photograph of a painting in an art museum, the tourism information provision system provides information about the artist, the date of creation, and a description of the work. Also, if a tourist takes a photograph of a landscape in a nature park, the tourism information provision system provides information about the natural environment and flora and fauna of that location. In addition, this tourism information provision system is multilingual, allowing tourists to receive information in their own language. For example, if a Japanese-speaking tourist visits a tourist destination in France, the tourist information system will provide information about that place in Japanese. This allows tourists to access tourist information despite language barriers. In this way, the tourist information system analyzes photos taken by tourists and provides detailed information about the place and the subject of the photo in real time, thereby improving the tourist experience. In this way, the tourist information system can enhance the tourist experience.

[0062] The tourism information provision system according to this embodiment comprises an analysis unit, an acquisition unit, a provision unit, and an interface unit. The analysis unit analyzes photographs taken by tourists. The analysis unit can, for example, analyze photographs using AI and identify the subject of the photographs using image recognition technology. For example, the analysis unit can analyze the content of photographs using deep learning technology. The analysis unit can also identify the subject of photographs using pattern recognition technology. Furthermore, the analysis unit can extract detailed information from photographs using image analysis algorithms. The acquisition unit acquires location information based on the information analyzed by the analysis unit. The acquisition unit can acquire location information using, for example, GPS technology. The acquisition unit can also acquire location information using Wi-Fi location information technology. Furthermore, the acquisition unit can also acquire location information using beacon technology. The provision unit provides tourism information based on the location information acquired by the acquisition unit. The provision unit can, for example, provide descriptions of tourist destinations and event information. Furthermore, the provision unit can also provide the historical background of tourist destinations and related tourism information. Furthermore, the provision unit can also provide information on the natural environment and flora and fauna of tourist destinations. The interface unit provides an interface for tourists to use the system. For example, the interface unit can provide an interface for a smartphone application or audio glasses. Furthermore, the interface unit can provide a user interface to allow tourists to easily operate the system. In addition, the interface unit can provide a multilingual interface. As a result, the tourism information provision system according to this embodiment can improve the tourist experience.

[0063] The analysis unit analyzes photographs taken by tourists. For example, it uses AI to analyze photos and image recognition technology to identify the subject of the photograph. Specifically, it utilizes deep learning technology to recognize objects and scenes within photographs with high accuracy. For instance, it automatically identifies buildings, landscapes, and people in photographs taken by tourists and extracts their respective features. The deep learning model is pre-trained on a large dataset of tourist destination photographs, achieving high recognition accuracy. It can also identify the subject of a photograph using pattern recognition technology. For example, it can identify specific architectural styles or patterns in natural landscapes and use them to determine the subject of the photograph. Furthermore, it can extract detailed information from photographs using image analysis algorithms. For example, it can extract text information from photographs and automatically recognize the names and descriptions of tourist destinations. As a result, the analysis unit can extract diverse information from photographs taken by tourists with high accuracy and provide the data necessary for subsequent processing.

[0064] The acquisition unit acquires location information based on the information analyzed by the analysis unit. The acquisition unit can acquire location information using, for example, GPS technology. Specifically, it uses the GPS module built into the tourist's smartphone or camera to determine the exact location where the photograph was taken. It can also acquire location information using Wi-Fi location information technology. This method estimates the tourist's location based on the signal strength of Wi-Fi access points within the tourist area. Furthermore, it is also possible to acquire location information using beacon technology. It receives signals transmitted from beacons installed within the tourist area and determines the location based on the signal strength and arrival time. As a result, the acquisition unit can acquire highly accurate location information by combining various technologies, and accurately understand the places and routes that tourists have visited. Furthermore, by adjusting the frequency and accuracy of location information acquisition, the acquisition unit can efficiently collect necessary information while protecting the privacy of tourists.

[0065] The information provider unit provides tourist information based on location data acquired by the information acquisition unit. For example, the information provider unit can provide descriptions of tourist destinations and event information. Specifically, it can provide detailed explanations of the history, culture, and attractions of tourist destinations, enabling tourists to gain a deeper understanding of the place. It can also provide real-time information on currently running events and activities, suggesting experiences that tourists can enjoy on the spot. Furthermore, the information provider unit can also provide the historical background and related tourist information of tourist destinations. For example, it can provide information on the historical significance of specific buildings or ruins, or information on past events, thereby attracting tourists' interest and providing learning opportunities. It can also provide information on the natural environment and flora and fauna of tourist destinations. For example, it can provide information on the characteristics of flora and fauna inhabiting specific areas, or on seasonal changes in nature, helping tourists enjoy the beauty of nature. In this way, the information provider unit can provide tourists with diverse and abundant information, enriching their tourist experience.

[0066] The interface unit provides an interface for tourists to use the system. For example, it can provide interfaces for smartphone applications and audio glasses. Specifically, it allows tourists to easily access tourist information through smartphone applications. The applications feature an intuitive user interface, enabling users to search for information on tourist destinations and view recommended spots based on their current location. Furthermore, using audio glasses, tourists can obtain information about tourist destinations not only visually but also through audio guides. The interface unit can also provide a user interface that allows tourists to easily operate the system. For example, using voice recognition technology, tourists can search for information and give instructions by voice. The interface unit can also provide a multilingual interface. This allows tourists who speak different languages ​​to use the system without experiencing language barriers. In this way, the interface unit provides tourists with an easy-to-use and accessible interface, improving the tourist experience.

[0067] The analysis unit can identify the subject of a photograph using image recognition technology. For example, the analysis unit can analyze the content of a photograph using deep learning technology. For instance, it can use a convolutional neural network (CNN) to extract features from the photograph and identify the subject. Furthermore, the analysis unit can also identify the subject of a photograph using pattern recognition technology. For example, it can use a support vector machine (SVM) to classify patterns in the photograph and identify the subject. In addition, the analysis unit can extract detailed information from a photograph using image analysis algorithms. For example, it can use an edge detection algorithm to extract contours from the photograph and identify the subject. This allows for accurate identification of the subject of a photograph using image recognition technology.

[0068] The acquisition unit can acquire location information using GPS or other location information technologies. For example, the acquisition unit can acquire location information using GPS technology. For example, the acquisition unit receives signals from GPS satellites and determines the tourist's current location. The acquisition unit can also acquire location information using Wi-Fi location information technology. For example, the acquisition unit measures the signal strength of surrounding Wi-Fi access points and determines the location. Furthermore, the acquisition unit can also acquire location information using beacon technology. For example, the acquisition unit receives signals from beacons and determines the tourist's location. This allows for the acquisition of accurate location information using GPS or other location information technologies. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input GPS signals to AI, which can then analyze and output location information.

[0069] The information provider can provide tourists with real-time tourist information. For example, it can provide real-time descriptions of tourist destinations and event information. For example, it can provide real-time information on the historical background of tourist destinations and related tourist information. The information provider can also provide real-time information on the natural environment and flora and fauna of tourist destinations. For example, it can provide real-time information on the types and ecology of flora and fauna of tourist destinations. Furthermore, the information provider can provide real-time information on the latest events and activities at tourist destinations. For example, it can provide real-time information on the schedule and how to participate in events held at tourist destinations. By providing tourist information in real time, the tourist experience can be improved. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information on tourist destinations into AI, and the AI ​​can analyze and output the information in real time.

[0070] The interface unit can make the system easy for tourists to use. The interface unit provides interfaces for, for example, smartphone applications and audio glasses. For instance, the interface unit allows tourists to operate the system using their smartphones. It can also allow tourists to operate the system using audio glasses. Furthermore, the interface unit can provide a user interface to make the system easy for tourists to operate. For example, the interface unit provides an intuitive and user-friendly user interface. This improves usability by making the system easy for tourists to use. Some or all of the above processing in the interface unit may be performed using, for example, AI, or not. For example, the interface unit can input tourist operation data into the AI, which can then generate an optimal user interface.

[0071] The information provider can provide tourist information in multiple languages. For example, it can provide descriptions of tourist destinations and event information in multiple languages. For example, it can provide information about tourist destinations in multiple languages ​​such as Japanese, English, and French. The information provider can also provide the historical background and related tourist information of tourist destinations in multiple languages. For example, it can provide the historical background of tourist destinations in multiple languages ​​such as Japanese, English, and French. Furthermore, the information provider can provide information about the natural environment and flora and fauna of tourist destinations in multiple languages. For example, it can provide information about the types and ecology of flora and fauna of tourist destinations in multiple languages ​​such as Japanese, English, and French. By providing tourist information in multiple languages, it becomes possible to obtain tourist information over language barriers. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information about tourist destinations into AI, and the AI ​​can translate and output it in multiple languages.

[0072] The analysis unit can estimate the tourist's emotions and adjust the photo analysis method based on the estimated emotions. For example, if the tourist is excited, the analysis unit can increase the accuracy of the analysis to provide more detailed information. For example, the analysis unit can use deep learning techniques to analyze the content of the photo in detail. The analysis unit can also simplify the analysis to provide concise information if the tourist is tired. For example, the analysis unit can use a simple image analysis algorithm to identify the subject of the photo. Furthermore, if the tourist is curious, the analysis unit can broaden the scope of the analysis to provide additional relevant information. For example, the analysis unit can combine multiple image analysis algorithms to extract detailed information from the photo. This allows for the provision of more appropriate information by adjusting the analysis method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourist emotional data into the AI, which can then analyze the emotions and output the results.

[0073] The analysis unit can improve analysis accuracy by considering the time of day the photograph was taken and weather information. For example, in the case of a photograph taken at night, the analysis unit uses an analysis algorithm that is compatible with low-light environments. For example, the analysis unit analyzes the photograph using a deep learning model that is robust in low-light environments. The analysis unit can also perform image processing to remove raindrops and fog in the case of a photograph taken in rainy weather. For example, the analysis unit removes raindrops and fog using image filtering technology. Furthermore, the analysis unit can perform corrections to minimize the effect of shadows in the case of a photograph taken in sunny weather. For example, the analysis unit reduces the effect of shadows using an image correction algorithm. This improves analysis accuracy by considering the time of day the photograph was taken and weather information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the metadata of the photograph into AI, and the AI ​​can analyze and output the time of day the photograph was taken and weather information.

[0074] The analysis unit can identify themes by referring to the tourist's past visit history. For example, the analysis unit can prioritize identifying similar themes based on data of places the tourist has visited in the past. For example, the analysis unit can use the tourist's visit history data to identify themes of tourist destinations similar to places visited in the past. The analysis unit can also identify themes related to specific themes from the tourist's visit history. For example, the analysis unit can identify themes of related works of art based on data of museums the tourist has visited in the past. Furthermore, the analysis unit can prioritize analyzing relevant information based on themes the tourist has shown interest in in the past. For example, the analysis unit can identify themes of related buildings based on data of historical buildings the tourist has shown interest in in the past. This improves the accuracy of theme identification by referring to past visit history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the tourist's visit history data into AI, and the AI ​​can identify and output themes.

[0075] The analysis unit can estimate the emotions of tourists and adjust the display method of the analysis results based on the estimated emotions of the tourists. For example, if a tourist is excited, the analysis unit can provide a visually appealing display method. For example, the analysis unit can display the analysis results using colorful graphics or animations. The analysis unit can also provide a concise and easy-to-read display method if a tourist is tired. For example, the analysis unit can display the analysis results using a simple design and large fonts. Furthermore, if a tourist is curious, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit can provide a display method that includes detailed text information and relevant links. This allows for the provision of more appropriate information by adjusting the display method according to the emotions of the tourists. 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourist emotional data into the AI, which can then analyze the emotions and output the results.

[0076] The analysis unit can analyze tourists' social media activity and supplement relevant information. For example, the analysis unit can provide relevant tourist information based on photos shared by tourists on social media. For example, the analysis unit can analyze the content of photos shared by tourists and provide information on relevant tourist destinations. The analysis unit can also analyze the content of tourists' social media posts and provide information that may be of interest to them. For example, the analysis unit can identify themes of interest from tourists' posts and provide relevant tourist information. Furthermore, the analysis unit can provide relevant tourist information based on information about accounts that tourists follow. For example, the analysis unit can provide the latest tourist information based on information about official accounts of tourist destinations that tourists follow. In this way, relevant information can be supplemented by analyzing social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourists' social media data into AI, and the AI ​​can analyze and output relevant information.

[0077] The analysis unit can select the optimal analysis algorithm by considering the tourist's device information. For example, if a high-performance device is being used, the analysis unit will select an algorithm that performs detailed analysis. For example, the analysis unit will utilize the processing power of the high-performance device to perform detailed analysis using a deep learning model. The analysis unit can also select a lightweight analysis algorithm if a low-performance device is being used. For example, the analysis unit will use a simple image analysis algorithm that matches the processing power of the low-performance device. Furthermore, the analysis unit can also select the optimal analysis algorithm by considering the device's battery level. For example, if the battery level is low, the analysis unit will select a lightweight analysis algorithm to conserve battery power. In this way, the optimal analysis algorithm can be selected by considering the device information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the tourist's device information into AI, and the AI ​​can select and output the optimal analysis algorithm.

[0078] The acquisition unit can estimate the tourist's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the tourist is excited, the acquisition unit can acquire location information frequently to provide information in real time. For example, the acquisition unit can increase the frequency of location information acquisition based on the tourist's emotion data. The acquisition unit can also reduce the frequency of location information acquisition to conserve battery power if the tourist is tired. For example, the acquisition unit can adjust the frequency of location information acquisition based on the tourist's emotion data. Furthermore, if the tourist is curious, the acquisition unit can acquire detailed location information to provide relevant information. For example, the acquisition unit can acquire detailed location information based on the tourist's emotion data. This allows for the provision of more appropriate information by adjusting the timing of location information acquisition according to the tourist's emotions. 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 processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input tourist emotional data into an AI, which can then analyze the emotions and output the results.

[0079] The acquisition unit can select the optimal acquisition method by referring to the tourist's past location information history. For example, the acquisition unit can select the optimal location information acquisition method based on data of places the tourist has visited in the past. For example, the acquisition unit can select the optimal location information acquisition method using the tourist's past location information history. The acquisition unit can also select a location information acquisition method for a specific area based on the tourist's visit history. For example, the acquisition unit can select a location information acquisition method for a specific area based on the tourist's visit history. Furthermore, the acquisition unit can also select a method for acquiring detailed location information based on areas the tourist has shown interest in in the past. For example, the acquisition unit can select a method for acquiring detailed location information based on the tourist's visit history. In this way, the optimal acquisition method can be selected by referring to past location information history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the tourist's past location information history into AI, and the AI ​​can select and output the optimal acquisition method.

[0080] The acquisition unit can correct location information by considering the tourist's current speed and direction of movement. For example, if the tourist is moving at high speed, the acquisition unit frequently updates the location information to provide accurate information. For example, the acquisition unit increases the frequency of location information updates based on the tourist's speed data. Also, if the tourist is moving slowly, the acquisition unit can reduce the frequency of location information updates to conserve battery power. For example, the acquisition unit adjusts the frequency of location information updates based on the tourist's speed data. Furthermore, the acquisition unit can also correct the location information by considering the tourist's direction of movement to provide accurate information. For example, the acquisition unit corrects the location information based on the tourist's direction of movement data. This improves the accuracy of location information by considering speed and direction of movement. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the tourist's speed and direction data into AI, and the AI ​​can correct and output the location information.

[0081] The acquisition unit can estimate the tourist's emotions and determine the priority of location information to acquire based on the estimated emotions. For example, if the tourist is excited, the acquisition unit will prioritize acquiring detailed location information of tourist attractions. For example, based on the tourist's emotion data, the acquisition unit will prioritize acquiring detailed location information of tourist attractions. The acquisition unit can also prioritize acquiring location information of resting places if the tourist is tired. For example, based on the tourist's emotion data, the acquisition unit will prioritize acquiring location information of resting places. Furthermore, if the tourist is curious, the acquisition unit can also prioritize acquiring location information of relevant tourist attractions. For example, based on the tourist's emotion data, the acquisition unit will prioritize acquiring location information of relevant tourist attractions. By prioritizing location information according to the tourist's emotions, more appropriate information can be provided. 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 processing described above in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input tourist emotion data into AI, which can then analyze and output the emotions.

[0082] The acquisition unit can prioritize the acquisition of highly relevant location information by considering the tourist's geographical location information. For example, if a tourist approaches a specific tourist destination, the acquisition unit will prioritize the acquisition of detailed location information for that tourist destination. For example, based on the tourist's geographical location information, the acquisition unit will prioritize the acquisition of detailed location information for a specific tourist destination. The acquisition unit can also prioritize the acquisition of location information related to a specific area if the tourist is in that area. For example, based on the tourist's geographical location information, the acquisition unit will prioritize the acquisition of location information related to a specific area. Furthermore, if a tourist is following a specific route, the acquisition unit can also prioritize the acquisition of location information related to that route. For example, based on the tourist's geographical location information, the acquisition unit will prioritize the acquisition of location information related to a specific route. In this way, by considering geographical location information, highly relevant location information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the tourist's geographical location information into AI, and the AI ​​can select and output highly relevant location information.

[0083] The acquisition unit can analyze tourists' social media activity and obtain relevant location information. For example, the acquisition unit prioritizes obtaining location information of places shared by tourists on social media. For example, the acquisition unit obtains relevant location information based on the content of tourists' social media posts. The acquisition unit can also analyze the content of tourists' social media posts and obtain relevant location information. For example, the acquisition unit obtains location information of places of interest from the content of tourists' posts. Furthermore, the acquisition unit can obtain relevant location information based on information of accounts that tourists follow. For example, the acquisition unit obtains relevant location information based on information of official accounts of tourist destinations that tourists follow. In this way, relevant location information can be obtained by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input tourists' social media data into AI, and the AI ​​can analyze and output relevant location information.

[0084] The information provider can estimate the emotions of tourists and adjust the way tourist information is provided based on the estimated emotions. For example, if a tourist is excited, the information provider can increase the amount of information to provide more detailed information. For example, the information provider can provide a detailed description of the tourist attraction and related information. Conversely, if a tourist is tired, the information provider can reduce the amount of information to provide more concise information. For example, the information provider can provide a concise description of the tourist attraction and its key points. Furthermore, if a tourist is curious, the information provider can increase the amount of information to provide additional relevant information. For example, the information provider can provide detailed historical and background information about the tourist attraction. This allows for the provision of more appropriate information by adjusting the method of delivery according to the emotions of tourists. 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 processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input tourist emotional data into an AI, which can then analyze and output the emotions.

[0085] The information provider can adjust the level of detail provided based on the importance of the tourist information. For example, the provider can provide detailed information about important tourist destinations. For example, it can provide detailed descriptions and related information about tourist destinations. The provider can also provide concise information about general tourist destinations. For example, it can provide concise descriptions and key points about tourist destinations. Furthermore, the provider can prioritize providing information of high importance according to the tourist's interests. For example, it can provide detailed information about tourist destinations that the tourist has shown interest in. In this way, by adjusting the level of detail based on the importance of the tourist information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input tourist information importance data into AI, and the AI ​​can adjust the level of detail and output the result.

[0086] The information provider can apply different information provision algorithms depending on the category of tourist information. For example, for information about historical tourist destinations, the provider can apply an algorithm that includes detailed historical background information. For example, the provider can provide information about the historical background and related information of the tourist destination. The provider can also apply an algorithm that includes details about flora and fauna for information about the natural environment. For example, the provider can provide information about the types and ecology of flora and fauna of the tourist destination. Furthermore, the provider can apply an algorithm that includes explanations of the artworks for information about art museums and museums. For example, the provider can provide detailed explanations of the artworks in art museums and museums. By applying information provision algorithms according to the category of tourist information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input tourist information category data into AI, and the AI ​​can select and output the optimal information provision algorithm.

[0087] The service provider can estimate the emotions of tourists and adjust the way tourist information is displayed based on the estimated emotions. For example, if a tourist is excited, the service provider can provide a visually appealing display method. For example, the service provider can display tourist information using colorful graphics and animations. The service provider can also provide a concise and easily readable display method if a tourist is tired. For example, the service provider can display tourist information using a simple design and large fonts. Furthermore, if a tourist is curious, the service provider can provide a display method that includes detailed information. For example, the service provider can provide a display method that includes detailed text information and relevant links. This allows for the provision of more appropriate information by adjusting the display method according to the emotions of tourists. 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 processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input tourist emotional data into an AI, which can then analyze and output the emotions.

[0088] The information provider can determine the priority of information provision based on the timing of submission. For example, the provider may prioritize providing important information immediately after a tourist arrives at a tourist destination. For instance, it may provide a detailed description of the tourist destination and related information. The provider may also provide information about the next destination before a tourist leaves a tourist destination. For example, it may provide information about the tourist destination the tourist plans to visit next. Furthermore, the provider may prioritize providing information about a specific event before the tourist participates in that event. For example, it may provide detailed information about the event the tourist plans to attend. This allows for the provision of more appropriate information by prioritizing based on submission timing. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the provider can input the submission timing data of tourist information into an AI, which can then determine and output the prioritization.

[0089] The information provider can adjust the order of information provided based on the relevance of the tourist information. For example, the provider can prioritize providing information that the tourist has shown interest in. For example, the provider can provide detailed information about tourist destinations that the tourist has shown interest in. The provider can also prioritize providing information about places that the tourist plans to visit. For example, the provider can provide information about tourist destinations that the tourist plans to visit next. Furthermore, the provider can also prioritize providing information related to places that the tourist has visited in the past. For example, the provider can provide information related to tourist destinations that the tourist has visited in the past. By adjusting the order of information provided based on relevance, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input relevance data of tourist information into AI, and the AI ​​can adjust the order of information provision and output it.

[0090] The interface unit can estimate the tourist's emotions and adjust the interface display method based on the estimated emotions. For example, if the tourist is excited, the interface unit can provide a visually appealing interface. For example, the interface unit can display the interface using colorful graphics and animations. Also, if the tourist is tired, the interface unit can provide a concise and easily readable interface. For example, the interface unit can display the interface using a simple design and large fonts. Furthermore, if the tourist is curious, the interface unit can provide an interface containing detailed information. For example, the interface unit can provide an interface containing detailed text information and relevant links. This allows for a more appropriate interface to be provided by adjusting the display method according to the tourist's emotions. 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 processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input tourist emotion data into an AI, which can then analyze and output the emotions.

[0091] The interface unit can select the optimal display method by referring to the tourist's past operation history when displaying the interface. For example, the interface unit provides the optimal interface based on the display method the tourist has used in the past. For example, the interface unit provides the optimal interface based on the tourist's past operation history. The interface unit can also predict and provide the tourist's preferred display method based on the tourist's operation history. For example, the interface unit predicts the tourist's preferred display method based on the tourist's operation history. Furthermore, the interface unit can prioritize displaying functions that the tourist has frequently used in the past. For example, the interface unit prioritizes displaying functions that the tourist has frequently used based on the tourist's operation history. This allows the optimal display method to be selected by referring to past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the tourist's operation history data into AI, and the AI ​​can select and output the optimal display method.

[0092] The interface unit can estimate the tourist's emotions and adjust the interface's operating procedures based on the estimated emotions. For example, if the tourist is excited, the interface unit can provide intuitive and simple operating procedures. For example, the interface unit can provide intuitive and simple operating procedures based on the tourist's emotion data. The interface unit can also provide simplified operating procedures if the tourist is tired. For example, the interface unit simplifies the operating procedures based on the tourist's emotion data. Furthermore, if the tourist is curious, the interface unit can provide detailed operating procedures. For example, the interface unit provides detailed operating procedures based on the tourist's emotion data. This allows for a more appropriate interface to be provided by adjusting the operating procedures according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input tourist emotional data into the AI, which can then analyze and output the emotions.

[0093] The interface unit can select the optimal display method when displaying the interface, taking into account the tourist's device information. For example, if a high-resolution device is being used, the interface unit can provide a detailed display method. For example, the interface unit can utilize the characteristics of a high-resolution device to display detailed graphics and text. The interface unit can also provide a concise display method when a low-resolution device is being used. For example, the interface unit can use a simple design and large fonts to match the characteristics of a low-resolution device. Furthermore, the interface unit can also provide the optimal display method according to the device's screen size. For example, the interface unit can adjust the layout and design considering the device's screen size. This allows the interface unit to select the optimal display method by considering device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the tourist's device information into AI, and the AI ​​can select and output the optimal display method.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The tourist information provision system can estimate the emotions of tourists and adjust the way tourist information is provided based on the estimated emotions. For example, if a tourist is excited, the amount of information can be increased to provide more detailed information. Specifically, it can provide detailed descriptions of tourist attractions and related information. Conversely, if a tourist is tired, the amount of information can be reduced to provide more concise information. For example, it can provide a concise description of tourist attractions and key points. Furthermore, if a tourist is curious, the amount of information can be increased to provide additional relevant information. For example, it can provide detailed historical and background information of tourist attractions. In this way, by adjusting the method of provision according to the emotions of tourists, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the provision unit may be performed using AI, for example, or not using AI. For example, the provision unit can input tourist emotion data into an AI, and the AI ​​can analyze the emotions and output them.

[0096] The tourism information system can identify themes by referring to tourists' past visit history. For example, it can prioritize the identification of similar themes based on data of places tourists have visited in the past. Specifically, it uses tourist visit history data to identify themes of tourist destinations similar to places they have visited in the past. It can also identify themes related to specific themes from tourists' visit history. For example, it can identify themes of related works of art based on data of museums tourists have visited in the past. Furthermore, it can prioritize the analysis of related information based on themes tourists have shown interest in in the past. For example, it can identify themes of related buildings based on data of historical buildings tourists have shown interest in in the past. This improves the accuracy of theme identification by referring to past visit history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourist visit history data into AI, and the AI ​​can identify and output themes.

[0097] The tourist information system can estimate the emotions of tourists and adjust the display method of the analysis results based on the estimated emotions. For example, if a tourist is excited, a visually appealing display method can be provided. Specifically, the analysis results can be displayed using colorful graphics or animations. If a tourist is tired, a concise and easily readable display method can be provided. For example, the analysis results can be displayed using a simple design and large fonts. Furthermore, if a tourist is curious, a display method including detailed information can be provided. For example, a display method including detailed text information and relevant links can be provided. In this way, by adjusting the display method according to the emotions of tourists, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourist emotion data into the AI, and the AI ​​can analyze the emotions and output them.

[0098] The tourism information provision system can analyze tourists' social media activity and supplement relevant information. For example, it can provide relevant tourism information based on photos shared by tourists on social media. Specifically, it analyzes the content of photos shared by tourists and provides information on relevant tourist destinations. It can also analyze the content of tourists' social media posts and provide information that they might be interested in. For example, it can identify themes of interest from tourists' posts and provide relevant tourism information. Furthermore, it can provide relevant tourism information based on information about accounts that tourists follow. For example, it can provide the latest tourism information based on information about official accounts of tourist destinations that tourists follow. In this way, relevant information can be supplemented by analyzing social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input tourists' social media data into AI, and the AI ​​can analyze and output relevant information.

[0099] The tourist information provision system can estimate the emotions of tourists and determine the priority of location information to be acquired based on the estimated emotions. For example, if a tourist is excited, detailed location information of tourist attractions can be prioritized. Specifically, detailed location information of tourist attractions is prioritized based on the tourist's emotion data. Also, if a tourist is tired, location information of rest areas can be prioritized. For example, location information of rest areas is prioritized based on the tourist's emotion data. Furthermore, if a tourist is curious, location information of relevant tourist attractions can be prioritized. For example, location information of relevant tourist attractions is prioritized based on the tourist's emotion data. In this way, by determining the priority of location information according to the tourist's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input tourist emotional data into an AI, which can then analyze the emotions and output the results.

[0100] The tourism information provision system can prioritize the acquisition of highly relevant location information by considering the geographical location of tourists. For example, when a tourist approaches a specific tourist destination, it can prioritize the acquisition of detailed location information for that destination. Specifically, it prioritizes the acquisition of detailed location information for a specific tourist destination based on the tourist's geographical location information. It can also prioritize the acquisition of location information related to a specific area when a tourist is in that area. For example, it prioritizes the acquisition of location information related to a specific area based on the tourist's geographical location information. Furthermore, when a tourist is following a specific route, it can prioritize the acquisition of location information related to that route. For example, it prioritizes the acquisition of location information related to a specific route based on the tourist's geographical location information. In this way, by considering geographical location information, it is possible to prioritize the acquisition of highly relevant location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the tourist's geographical location information into the AI, and the AI ​​can select and output highly relevant location information.

[0101] The tourist information system can estimate the emotions of tourists and adjust the interface display method based on the estimated emotions. For example, if a tourist is excited, a visually appealing interface can be provided. Specifically, the interface can be displayed using colorful graphics and animations. If a tourist is tired, a concise and easily readable interface can be provided. For example, the interface can be displayed using a simple design and large fonts. Furthermore, if a tourist is curious, an interface containing detailed information can be provided. For example, an interface containing detailed text information and relevant links can be provided. In this way, a more appropriate interface can be provided by adjusting the display method according to the emotions of tourists. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can input tourist emotion data into the AI, and the AI ​​can analyze the emotions and output them.

[0102] The tourism information provision system can select the optimal analysis algorithm by considering the tourist's device information. For example, if a high-performance device is used, an algorithm that performs detailed analysis can be selected. Specifically, a detailed analysis using a deep learning model can be performed by utilizing the processing power of the high-performance device. Conversely, if a low-performance device is used, a lightweight analysis algorithm can be selected. For example, a simple image analysis algorithm can be used to match the processing power of the low-performance device. Furthermore, the system can also select the optimal analysis algorithm by considering the device's battery level. For example, if the battery level is low, a lightweight analysis algorithm can be selected to conserve battery power. In this way, the optimal analysis algorithm can be selected by considering device information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the tourist's device information into the AI, and the AI ​​can select and output the optimal analysis algorithm.

[0103] The tourist information system can estimate the emotions of tourists and adjust the interface operation procedures based on the estimated emotions. For example, if a tourist is excited, intuitive and simple operation procedures can be provided. Specifically, intuitive and simple operation procedures can be provided based on the tourist's emotion data. Also, if a tourist is tired, the operation procedures can be simplified. For example, the operation procedures can be simplified based on the tourist's emotion data. Furthermore, if a tourist is curious, detailed operation procedures can be provided. For example, detailed operation procedures can be provided based on the tourist's emotion data. In this way, a more appropriate interface can be provided by adjusting the operation procedures according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input tourist emotion data into the AI, and the AI ​​can analyze the emotions and output them.

[0104] The tourist information provision system can select the optimal display method by referring to the tourist's past operation history. For example, it can provide the optimal interface based on the display method the tourist has used in the past. Specifically, it provides the optimal interface based on the tourist's past operation history. It can also predict and provide the tourist's preferred display method from their operation history. For example, it predicts the tourist's preferred display method based on their operation history. Furthermore, it can prioritize displaying functions that the tourist has frequently used in the past. For example, it prioritizes displaying functions that the tourist has frequently used based on their operation history. In this way, the optimal display method can be selected by referring to past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the tourist's operation history data into AI, and the AI ​​can select and output the optimal display method.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The analysis unit analyzes the photos taken by tourists. The analysis unit uses AI, deep learning technology, pattern recognition technology, and image analysis algorithms to identify the subject of the photos and extract detailed information. Step 2: The acquisition unit acquires location information based on the information analyzed by the analysis unit. The acquisition unit can acquire location information using GPS technology, Wi-Fi location information technology, and beacon technology. Step 3: The providing unit provides tourist information based on the location information acquired by the acquisition unit. The providing unit can provide descriptions of tourist destinations, event information, historical background, and information on the natural environment and flora and fauna. Step 4: The interface unit provides an interface for tourists to use the system. The interface unit can provide a smartphone application interface, an audio glasses interface, a user interface, and a multilingual interface.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the analysis unit, acquisition unit, provision unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit acquires photographs taken by tourists using the camera 42 of the smart device 14 and performs AI-based image analysis using the identification processing unit 290 of the data processing unit 12. The acquisition unit acquires location information using the GPS function or Wi-Fi location information technology of the smart device 14. The provision unit generates tourist information using the identification processing unit 290 of the data processing unit 12 and provides it to tourists through the display 40A and speaker 40B of the smart device 14. The interface unit provides a user interface using the control unit 46A of the smart device 14, and realizes a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the analysis unit, acquisition unit, provision unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit acquires photographs taken by tourists using the camera 42 of the smart glasses 214 and performs AI-based image analysis using the identification processing unit 290 of the data processing unit 12. The acquisition unit acquires location information using the GPS function or Wi-Fi location information technology of the smart glasses 214. The provision unit generates tourist information using the identification processing unit 290 of the data processing unit 12 and provides it to tourists through the speaker 240 of the smart glasses 214. The interface unit provides a user interface using the control unit 46A of the smart glasses 214, and realizes a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the analysis unit, acquisition unit, provision unit, and interface unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit acquires photographs taken by tourists using the camera 42 of the headset terminal 314 and performs AI-based image analysis using the identification processing unit 290 of the data processing unit 12. The acquisition unit acquires location information using, for example, the GPS function or Wi-Fi location information technology of the headset terminal 314. The provision unit generates tourist information using, for example, the identification processing unit 290 of the data processing unit 12 and provides it to tourists through the display 343 and speaker 240 of the headset terminal 314. The interface unit provides a user interface using, for example, the control unit 46A of the headset terminal 314, and realizes a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the analysis unit, acquisition unit, provision unit, and interface unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit acquires photographs taken by tourists using the camera 42 of the robot 414 and performs AI-based image analysis using the specific processing unit 290 of the data processing unit 12. The acquisition unit acquires location information using, for example, the GPS function or Wi-Fi location information technology of the robot 414. The provision unit generates tourist information using, for example, the specific processing unit 290 of the data processing unit 12 and provides it to tourists through the speaker 240 of the robot 414. The interface unit provides a user interface using, for example, the control unit 46A of the robot 414, realizing a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) An analysis unit that analyzes photos taken by tourists, An acquisition unit that acquires location information based on the information analyzed by the aforementioned analysis unit, A provisioning unit provides tourist information based on location information acquired by the acquisition unit, It includes an interface section for tourists to use the system. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Identifying the subject of a photograph using image recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Location information is obtained using GPS and other location-based technologies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Providing tourists with real-time tourist information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The interface unit is To make the system easy for tourists to use The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing tourist information in multiple languages The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate the emotions of tourists and adjust the photo analysis method based on the estimated emotions of the tourists. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Improve analysis accuracy by considering the time of day the photos were taken and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Identify the subject by referring to the tourist's past visit history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the emotions of tourists and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, Analyze tourists' social media activity and supplement relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The optimal analysis algorithm is selected by considering the device information of the tourists. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, The system estimates the emotions of tourists and adjusts the timing of location data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, The optimal acquisition method is selected by referring to the tourist's past location history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, The location information is corrected considering the tourist's current speed and direction of movement. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, The system estimates the sentiment of tourists and prioritizes the location information to be acquired based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, Prioritize obtaining highly relevant location information, taking into account the geographical location of tourists. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, Analyze tourists' social media activity and obtain relevant location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the sentiments of tourists and adjust the way tourist information is provided based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The level of detail provided is adjusted based on the importance of the tourist information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Apply different delivery algorithms depending on the category of tourist information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the sentiments of tourists and adjusts how tourist information is displayed based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Prioritizing the provision of tourist information based on when it is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The order in which tourist information is presented is adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The interface unit is It estimates the emotions of tourists and adjusts the interface display based on the estimated emotions of the tourists. The system described in Appendix 1, characterized by the features described herein. (Note 26) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the tourist's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The interface unit is It estimates the emotions of tourists and adjusts the interface operation procedures based on the estimated emotions of the tourists. The system described in Appendix 1, characterized by the features described herein. (Note 28) The interface unit is When displaying the interface, the optimal display method is selected considering the tourist's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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. An analysis unit that analyzes photos taken by tourists, An acquisition unit that acquires location information based on the information analyzed by the aforementioned analysis unit, A provisioning unit provides tourist information based on location information acquired by the acquisition unit, It includes an interface section for tourists to use the system. A system characterized by the following features.

2. The aforementioned analysis unit, Identifying the subject of a photograph using image recognition technology. The system according to feature 1.

3. The acquisition unit is, Location information is obtained using GPS and other location-based technologies. The system according to feature 1.

4. The aforementioned supply unit is, Providing tourists with real-time tourist information. The system according to feature 1.

5. The interface unit is To make the system easy for tourists to use The system according to feature 1.

6. The aforementioned supply unit is, Providing tourist information in multiple languages The system according to feature 1.

7. The aforementioned analysis unit, We estimate the emotions of tourists and adjust the photo analysis method based on the estimated emotions of the tourists. The system according to feature 1.

8. The aforementioned analysis unit, Improve analysis accuracy by considering the time of day the photos were taken and weather information. The system according to feature 1.

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