System

Abstract

An object of the system according to the embodiment is to specify a building from a photograph and quickly provide information thereof.SOLUTION: A system includes a photograph acquisition unit, a structure determination unit, an information acquisition unit, and an information provision unit. The photograph acquisition unit acquires a photograph from a user. The structure determination unit analyzes the photograph acquired by the photograph acquisition unit to specify a structure. The information acquisition unit acquires information on the structure specified by the structure determination unit from an online database. The information providing unit provides the user with the information acquired by the information acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to identify buildings from photographs and provide that information quickly.

[0005] The system according to the embodiment aims to identify buildings from photographs and quickly provide information about them. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, a building determination unit, an information acquisition unit, and an information provision unit. The photo acquisition unit acquires a photo from a user. The building determination unit analyzes the photo acquired by the photo acquisition unit to identify a building. The information acquisition unit acquires information about the building identified by the building determination unit from an online database. The information provision unit provides the information acquired by the information acquisition unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can identify buildings from photographs and provide information about them quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention provides various information about historical buildings, such as shrines and temples, simply by taking a photo of the building. This system utilizes generative AI and other generative AI to identify buildings from photos and provide appropriate information to users by referencing an online database. This allows the information provision system to easily provide detailed information about buildings to a wide range of users, including foreign visitors to Japan and Japanese people who love history.

[0029] An information provision system according to an embodiment includes a photo acquisition unit, a building determination unit, an information acquisition unit, and an information provision unit. The photo acquisition unit acquires photos from a user. For example, the photo can be uploaded via a smartphone app. Alternatively, the photo can be provided using a web interface. Furthermore, the photo acquisition unit can acquire photos from cloud storage. For example, the photo can acquire photos stored by the user on Google Drive or Dropbox. The building determination unit analyzes the photos acquired by the photo acquisition unit to identify a building. For example, the generation AI can extract features of a building using image recognition technology to identify the building. Alternatively, the generation AI can use a machine learning algorithm to identify the building. Alternatively, the generation AI can analyze the shape and decoration of a building to identify the building. For example, the generation AI can identify a building based on the shape and decoration of its roof. The information acquisition unit acquires information about the building identified by the building determination unit from an online database. For example, the generation AI can refer to a public database to acquire information about the building's history and architectural style. Alternatively, the generation AI can refer to a commercial database to acquire information about important events and people. Alternatively, the generation AI can select a specific database to collect necessary information. For example, the generation AI selects an appropriate database based on the identification of the building and collects information. The information provision unit provides the user with the information acquired by the information acquisition unit. For example, the generation AI provides the user with the acquired information as a text message. The generation AI can also provide the information as an audio guide. The generation AI can also provide the information as an in-app notification. For example, the generation AI provides the information by sending a notification to the user's smartphone. As a result, the information provision system according to the embodiment identifies buildings from photos taken by the user and provides that information, improving user convenience. For example, a user can easily learn detailed information about a building by simply taking a photo with their smartphone and uploading it to the service. Furthermore, by providing information in multiple languages ​​for foreign tourists, the system is expected to be useful in the tourism industry.

[0030] The structure determination unit analyzes the environment surrounding a structure and can improve the accuracy of identifying the structure. For example, when the generation AI determines a structure, the structure determination unit analyzes the vegetation and terrain surrounding the structure. For example, it recognizes the shapes of specific trees and mountains surrounding a shrine and improves the accuracy of identifying the structure based on that. The structure determination unit can also analyze the layout of a building and its relationship to surrounding buildings. For example, the generation AI identifies a building based on the layout of the building. In this way, the accuracy of identifying the building can be improved by analyzing the environment surrounding the building.

[0031] The building determination unit can refer to past weather data and seasonal information and take into account changes in the appearance of a building. For example, when the generation AI determines a building, the building determination unit refers to past weather data. For example, if the appearance of a building changes due to rain or snow, the unit takes these changes into account when identifying the building. The building determination unit can also refer to seasonal information and take into account changes in the appearance of a building. For example, it can identify shrines where cherry blossoms bloom in spring and temples where autumn leaves can be seen in autumn. The building determination unit can also combine weather data and seasonal information to comprehensively analyze changes in the appearance of a building. For example, the generation AI can predict changes in the appearance of a building based on weather data and seasonal information, and identify the building based on that. This allows changes in the appearance of a building to be taken into account by referring to past weather data and seasonal information.

[0032] The building assessment unit analyzes the state of deterioration of a building and the need for repair, which can be useful for conservation activities. For example, when the generation AI assesses a building, the building assessment unit analyzes the state of deterioration of the building. For example, it detects cracks in roof tiles and dirt on walls and determines the need for repair. The building assessment unit can also evaluate the state of deterioration by referring to the building's repair history. For example, the generation AI analyzes the state of deterioration of a building based on past repair data. The building assessment unit can also comprehensively evaluate the state of deterioration and the need for repair. For example, the generation AI determines the priority of conservation activities based on the state of deterioration and the urgency of repair. This allows analysis of the state of deterioration of a building and the need for repair to be useful for conservation activities.

[0033] The building determination unit can compare the user's past visit history and provide personalized information based on the visit history. For example, when the generation AI determines a building, the building determination unit compares the user's past visit history. For example, it provides related information based on information about shrines and temples visited in the past. The building determination unit can also analyze the user's interests based on the visit history. For example, the generation AI can identify the user's interests based on the visit history and provide information based on that. The building determination unit can also perform a comprehensive analysis by combining the visit history and the characteristics of the building. For example, the generation AI can provide personalized information based on the visit history and the characteristics of the building. This allows personalized information to be provided by comparing it with the user's past visit history.

[0034] The information acquisition unit can analyze historical documents and old maps related to the building to provide more detailed information. For example, when the generation AI references a database, the information acquisition unit analyzes historical documents about the building. For example, detailed information about the building is provided based on old documents and history books. The information acquisition unit can also analyze old maps to identify the building's historical location and changes. For example, the generation AI identifies the location of the building based on old maps and analyzes its changes. The information acquisition unit can also perform a comprehensive analysis by combining historical documents and old maps. For example, the generation AI provides detailed information about the building based on historical documents and old maps. This makes it possible to provide more detailed information by analyzing historical documents and old maps.

[0035] The information acquisition unit can search for artworks and literary works related to the building and provide cultural background information. For example, when the generation AI refers to a database, the information acquisition unit searches for artworks related to the building. For example, it can provide cultural background information of the building based on paintings and sculptures. The information acquisition unit can also search literary works to provide stories and episodes related to the building. For example, the generation AI can provide cultural background information of the building based on novels and poems. The information acquisition unit can also combine artworks and literary works for comprehensive analysis. For example, the generation AI can provide cultural background information of the building based on artworks and literary works. This makes it possible to provide cultural background information by searching for artworks and literary works.

[0036] The information acquisition unit can provide information on nearby related buildings in conjunction with the user's current location information. The information acquisition unit, for example, acquires the user's current location information when the generation AI references a database. For example, the generation AI identifies the user's current location based on GPS data. The information acquisition unit can also provide information on nearby related buildings based on the current location information. For example, the generation AI provides information on shrines and temples within walking distance of the current location. The information acquisition unit can also combine the current location information and building data for comprehensive analysis. For example, the generation AI provides information on nearby related buildings based on the current location information and building data. In this way, by linking with the user's current location information, information on nearby related buildings can be provided.

[0037] The information acquisition unit can customize and provide personalized information based on the user's interests and concerns. For example, the information acquisition unit analyzes the user's interests and concerns when the generation AI references a database. For example, the information acquisition unit can identify interests based on the user's search history or survey results. The information acquisition unit can also customize information based on interests. For example, the generation AI can identify interests based on the user's behavioral history or social media activity and provide information based on those interests. The information acquisition unit can also perform a comprehensive analysis by combining interests and concerns with the content of the information. For example, the generation AI can provide personalized information based on interests and concerns and the content of the information. This makes it possible to provide personalized information by customizing based on the user's interests and concerns.

[0038] The information providing unit can add a 3D model or virtual tour of the building to provide the user with the experience of virtually visiting the building. The information providing unit, for example, adds a 3D model of the building to the information provided by the generating AI. For example, the information providing unit can allow the user to view the 3D model of the building on a smartphone or tablet. The information providing unit can also provide a virtual tour. For example, the information providing unit can provide the user with the experience of virtually visiting the building by using 360-degree video or VR technology. The information providing unit can also provide a comprehensive combination of the 3D model and the virtual tour. For example, the generating AI can provide the user with the experience of virtually visiting the building based on the 3D model and the virtual tour. In this way, by adding the 3D model or virtual tour of the building, the user can be provided with the experience of virtually visiting the building.

[0039] The information provision unit adds past photographs and videos of the building, allowing the user to visually understand the building's historical changes. The information provision unit, for example, adds past photographs of the building to the information provided by the generation AI. For example, it provides photos of the building when it was first built or photos before restoration. The information provision unit can also provide past videos of the building. For example, the user can visually understand the building's changes based on historical videos or documentary videos. The information provision unit can also provide a comprehensive combination of past photographs and videos. For example, the generation AI can visually understand the building's historical changes based on past photographs and videos. In this way, the user can visually understand the building's historical changes by adding past photographs and videos of the building.

[0040] The information providing unit can add information about tourist spots and restaurants around the building and suggest a sightseeing plan. For example, the information providing unit adds information about tourist spots around the building to the information provided by the generation AI. For example, it provides information about famous places and tourist destinations near shrines. The information providing unit can also provide information about restaurants. For example, it provides information about restaurants and cafes. The information providing unit can also suggest a sightseeing plan by combining information about tourist spots and restaurants. For example, the generation AI can suggest the most suitable sightseeing plan for the user based on information about tourist spots and restaurants. This makes it possible to suggest a sightseeing plan by adding information about tourist spots and restaurants around the building.

[0041] The information provision unit can add information about events and special exhibitions related to the building and suggest the best time to visit. For example, the information provision unit adds event information related to the building to the information provided by the generation AI. For example, it provides information about festivals and special ceremonies held at shrines. The information provision unit can also provide information about special exhibitions. For example, it provides information about limited-time exhibitions and special exhibitions. The information provision unit can also combine event information and special exhibition information to suggest the best time to visit. For example, the generation AI can suggest the best time to visit to the user based on the event information and special exhibition information. In this way, the generation AI can suggest the best time to visit by adding information about events and special exhibitions related to the building.

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

[0043] The information provision system can also be equipped with a health management unit that monitors the user's health condition and proposes an appropriate sightseeing plan. For example, it can monitor the user's step count and heart rate and propose a shorter sightseeing route to avoid excessive exercise. The health management unit can also propose appropriate restaurants based on the user's dietary restrictions. For example, it can select a restaurant suitable for the user based on allergy information and diet information. Furthermore, the health management unit can combine the user's health data with the sightseeing plan for comprehensive analysis. For example, the generation AI can propose a reasonable sightseeing plan based on the user's health data. This makes it possible to provide a sightseeing plan that takes the user's health condition into consideration.

[0044] The information provision system may further include a travel history analysis unit that analyzes the user's past travel history and prioritizes suggesting unvisited buildings. For example, unvisited buildings may be identified based on data on buildings visited in the past. The travel history analysis unit may also suggest unvisited buildings based on the user's interests. For example, the generation AI may analyze the user's interests and suggest unvisited buildings based on those. The travel history analysis unit may also perform a comprehensive analysis by combining the travel history and the characteristics of the buildings. For example, the generation AI may suggest unvisited buildings that are optimal for the user based on the travel history and the characteristics of the buildings. In this way, unvisited buildings can be prioritized by analyzing the user's past travel history.

[0045] The information provision system may further include an activity suggestion unit that suggests related activities based on the user's interests. For example, if the user is interested in historical buildings, the system may suggest historical events or workshops taking place in the vicinity. The activity suggestion unit may also determine the priority of activities based on the user's interests. For example, the generation AI may analyze the user's interests and suggest activities based on those. The activity suggestion unit may also perform a comprehensive analysis by combining the interests and the characteristics of the activities. For example, the generation AI may suggest the most suitable activities for the user based on the interests and the characteristics of the activities. This allows the system to suggest related activities based on the user's interests.

[0046] The information provision system may further include a traffic information provision unit that provides traffic information in real time based on the user's current location information. For example, the system may identify the user's current location based on GPS data and suggest the optimal means of transportation. The traffic information provision unit may also analyze real-time traffic conditions to suggest the optimal route. For example, the generation AI may suggest the optimal route based on traffic congestion and the operation status of public transportation. The traffic information provision unit may also perform comprehensive analysis by combining the current location information and traffic data. For example, the generation AI may suggest the optimal means of transportation and route to the user based on the current location information and traffic data. This allows traffic information to be provided in real time based on the user's current location information.

[0047] The information provision system may further include a review analysis unit that provides building evaluations based on users' past reviews and evaluations. For example, the system may analyze reviews of buildings that the user has visited in the past and provide evaluations from other users. The review analysis unit may also comprehensively analyze building evaluations based on user evaluation data. For example, the generation AI may determine the building evaluation based on the user's evaluation score. The review analysis unit may also perform a comprehensive analysis by combining evaluation data and building features. For example, the generation AI may provide the user with the optimal building evaluation based on the evaluation data and building features. This allows the system to provide building evaluations based on users' past reviews and evaluations.

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

[0049] Step 1: The photo acquisition unit acquires photos from the user. For example, photos can be uploaded via a smartphone app. Photos can also be provided using a web interface. Furthermore, the photo acquisition unit can acquire photos from cloud storage. For example, the photo acquisition unit acquires photos stored by the user on Google Drive or Dropbox. Step 2: The building determination unit analyzes the photos acquired by the photo acquisition unit to identify the building. For example, the generation AI uses image recognition technology to extract the features of the building and identify it. The generation AI can also use machine learning algorithms to identify the building. The generation AI can also analyze and identify the building's shape and decoration. For example, the generation AI can identify the building based on the shape and decoration of the roof. Step 3: The information acquisition unit acquires information about the building identified by the building determination unit from an online database. For example, the generation AI may refer to a public database to acquire information about the building's history and architectural style. The generation AI may also refer to a commercial database to acquire information about important events or people. The generation AI may also select a specific database to collect the necessary information. For example, the generation AI may select an appropriate database based on the identification of the building and collect information. Step 4: The information providing unit provides the information acquired by the information acquiring unit to the user. For example, the generation AI provides the acquired information to the user as a text message. The generation AI can also provide the information as an audio guide. The generation AI can also provide the information as an in-app notification. For example, the generation AI provides the information by sending a notification to the user's smartphone.

[0050] (Example 2) An information provision system according to an embodiment of the present invention provides various information about historical buildings, such as shrines and temples, simply by taking a photo of the building. This system utilizes generative AI and other generative AI to identify buildings from photos and provide appropriate information to users by referencing an online database. This allows the information provision system to easily provide detailed information about buildings to a wide range of users, including foreign visitors to Japan and Japanese people who love history.

[0051] An information provision system according to an embodiment includes a photo acquisition unit, a building determination unit, an information acquisition unit, and an information provision unit. The photo acquisition unit acquires photos from a user. For example, the photo can be uploaded via a smartphone app. Alternatively, the photo can be provided using a web interface. Furthermore, the photo acquisition unit can acquire photos from cloud storage. For example, the photo can acquire photos stored by the user on Google Drive or Dropbox. The building determination unit analyzes the photos acquired by the photo acquisition unit to identify a building. For example, the generation AI can extract features of a building using image recognition technology to identify the building. Alternatively, the generation AI can use a machine learning algorithm to identify the building. Alternatively, the generation AI can analyze the shape and decoration of a building to identify the building. For example, the generation AI can identify a building based on the shape and decoration of its roof. The information acquisition unit acquires information about the building identified by the building determination unit from an online database. For example, the generation AI can refer to a public database to acquire information about the building's history and architectural style. Alternatively, the generation AI can refer to a commercial database to acquire information about important events and people. Alternatively, the generation AI can select a specific database to collect necessary information. For example, the generation AI selects an appropriate database based on the identification of the building and collects information. The information provision unit provides the user with the information acquired by the information acquisition unit. For example, the generation AI provides the user with the acquired information as a text message. The generation AI can also provide the information as an audio guide. The generation AI can also provide the information as an in-app notification. For example, the generation AI provides the information by sending a notification to the user's smartphone. As a result, the information provision system according to the embodiment identifies buildings from photos taken by the user and provides that information, improving user convenience. For example, a user can easily learn detailed information about a building by simply taking a photo with their smartphone and uploading it to the service. Furthermore, by providing information in multiple languages ​​for foreign tourists, the system is expected to be useful in the tourism industry.

[0052] The structure determination unit analyzes the environment surrounding a structure and can improve the accuracy of identifying the structure. For example, when the generation AI determines a structure, the structure determination unit analyzes the vegetation and terrain surrounding the structure. For example, it recognizes the shapes of specific trees and mountains surrounding a shrine and improves the accuracy of identifying the structure based on that. The structure determination unit can also analyze the layout of a building and its relationship to surrounding buildings. For example, the generation AI identifies a building based on the layout of the building. In this way, the accuracy of identifying the building can be improved by analyzing the environment surrounding the building.

[0053] The building determination unit can refer to past weather data and seasonal information and take into account changes in the appearance of a building. For example, when the generation AI determines a building, the building determination unit refers to past weather data. For example, if the appearance of a building changes due to rain or snow, the unit takes these changes into account when identifying the building. The building determination unit can also refer to seasonal information and take into account changes in the appearance of a building. For example, it can identify shrines where cherry blossoms bloom in spring and temples where autumn leaves can be seen in autumn. The building determination unit can also combine weather data and seasonal information to comprehensively analyze changes in the appearance of a building. For example, the generation AI can predict changes in the appearance of a building based on weather data and seasonal information, and identify the building based on that. This allows changes in the appearance of a building to be taken into account by referring to past weather data and seasonal information.

[0054] The building determination unit can analyze the user's emotions and prioritize buildings that are emotionally significant. The building determination unit, for example, uses an emotion estimation function to analyze the user's emotions regarding photos taken. For example, it prioritizes buildings that the user is impressed by. The building determination unit can also evaluate the importance of buildings based on the user's emotion data. For example, the generation AI determines the importance of buildings based on the user's emotion score. The building determination unit can also perform a comprehensive analysis by combining emotion data and building features. For example, the generation AI identifies buildings that are emotionally significant based on the emotion data and building features. In this way, it is possible to prioritize buildings that are emotionally significant by analyzing the user's emotions.

[0055] The building assessment unit analyzes the state of deterioration of a building and the need for repair, which can be useful for conservation activities. For example, when the generation AI assesses a building, the building assessment unit analyzes the state of deterioration of the building. For example, it detects cracks in roof tiles and dirt on walls and determines the need for repair. The building assessment unit can also evaluate the state of deterioration by referring to the building's repair history. For example, the generation AI analyzes the state of deterioration of a building based on past repair data. The building assessment unit can also comprehensively evaluate the state of deterioration and the need for repair. For example, the generation AI determines the priority of conservation activities based on the state of deterioration and the urgency of repair. This allows analysis of the state of deterioration of a building and the need for repair to be useful for conservation activities.

[0056] The building determination unit can compare the user's past visit history and provide personalized information based on the visit history. For example, when the generation AI determines a building, the building determination unit compares the user's past visit history. For example, it provides related information based on information about shrines and temples visited in the past. The building determination unit can also analyze the user's interests based on the visit history. For example, the generation AI can identify the user's interests based on the visit history and provide information based on that. The building determination unit can also perform a comprehensive analysis by combining the visit history and the characteristics of the building. For example, the generation AI can provide personalized information based on the visit history and the characteristics of the building. This allows personalized information to be provided by comparing it with the user's past visit history.

[0057] The building determination unit can analyze the user's emotions and prioritize buildings that are emotionally positive. The building determination unit, for example, uses an emotion estimation function to analyze the user's emotions toward photos taken. For example, it prioritizes buildings that the user has positive emotions about. The building determination unit can also evaluate the positivity of a building based on the user's emotion data. For example, the generation AI determines the positivity of a building based on the user's emotion score. The building determination unit can also perform a comprehensive analysis by combining emotion data and building features. For example, the generation AI identifies buildings that are emotionally positive based on the emotion data and building features. In this way, it is possible to prioritize buildings that are emotionally positive by analyzing the user's emotions.

[0058] The information acquisition unit can analyze historical documents and old maps related to the building to provide more detailed information. For example, when the generation AI references a database, the information acquisition unit analyzes historical documents about the building. For example, detailed information about the building is provided based on old documents and history books. The information acquisition unit can also analyze old maps to identify the building's historical location and changes. For example, the generation AI identifies the location of the building based on old maps and analyzes its changes. The information acquisition unit can also perform a comprehensive analysis by combining historical documents and old maps. For example, the generation AI provides detailed information about the building based on historical documents and old maps. This makes it possible to provide more detailed information by analyzing historical documents and old maps.

[0059] The information acquisition unit can search for artworks and literary works related to the building and provide cultural background information. For example, when the generation AI refers to a database, the information acquisition unit searches for artworks related to the building. For example, it can provide cultural background information of the building based on paintings and sculptures. The information acquisition unit can also search literary works to provide stories and episodes related to the building. For example, the generation AI can provide cultural background information of the building based on novels and poems. The information acquisition unit can also combine artworks and literary works for comprehensive analysis. For example, the generation AI can provide cultural background information of the building based on artworks and literary works. This makes it possible to provide cultural background information by searching for artworks and literary works.

[0060] The information acquisition unit can provide information on nearby related buildings in conjunction with the user's current location information. The information acquisition unit, for example, acquires the user's current location information when the generation AI references a database. For example, the generation AI identifies the user's current location based on GPS data. The information acquisition unit can also provide information on nearby related buildings based on the current location information. For example, the generation AI provides information on shrines and temples within walking distance of the current location. The information acquisition unit can also combine the current location information and building data for comprehensive analysis. For example, the generation AI provides information on nearby related buildings based on the current location information and building data. In this way, by linking with the user's current location information, information on nearby related buildings can be provided.

[0061] The information acquisition unit can customize and provide personalized information based on the user's interests and concerns. For example, the information acquisition unit analyzes the user's interests and concerns when the generation AI references a database. For example, the information acquisition unit can identify interests based on the user's search history or survey results. The information acquisition unit can also customize information based on interests. For example, the generation AI can identify interests based on the user's behavioral history or social media activity and provide information based on those interests. The information acquisition unit can also perform a comprehensive analysis by combining interests and concerns with the content of the information. For example, the generation AI can provide personalized information based on interests and concerns and the content of the information. This makes it possible to provide personalized information by customizing based on the user's interests and concerns.

[0062] The information acquisition unit can analyze the user's emotions and prioritize acquisition of emotionally positive information. The information acquisition unit, for example, uses an emotion estimation function to prioritize acquisition of information that the user is most interested in. For example, information about buildings that the user has positive emotions about is provided preferentially. The information acquisition unit can also evaluate the positivity of the information based on the user's emotion data. For example, the generation AI determines the positivity of the information based on the user's emotion score. The information acquisition unit can also perform a comprehensive analysis by combining the emotion data and the content of the information. For example, the generation AI provides emotionally positive information based on the emotion data and the content of the information. In this way, by analyzing the user's emotions, emotionally positive information can be prioritized.

[0063] The information providing unit can add a 3D model or virtual tour of the building to provide the user with the experience of virtually visiting the building. The information providing unit, for example, adds a 3D model of the building to the information provided by the generating AI. For example, the information providing unit can allow the user to view the 3D model of the building on a smartphone or tablet. The information providing unit can also provide a virtual tour. For example, the information providing unit can provide the user with the experience of virtually visiting the building by using 360-degree video or VR technology. The information providing unit can also provide a comprehensive combination of the 3D model and the virtual tour. For example, the generating AI can provide the user with the experience of virtually visiting the building based on the 3D model and the virtual tour. In this way, by adding the 3D model or virtual tour of the building, the user can be provided with the experience of virtually visiting the building.

[0064] The information provision unit adds past photographs and videos of the building, allowing the user to visually understand the building's historical changes. The information provision unit, for example, adds past photographs of the building to the information provided by the generation AI. For example, it provides photos of the building when it was first built or photos before restoration. The information provision unit can also provide past videos of the building. For example, the user can visually understand the building's changes based on historical videos or documentary videos. The information provision unit can also provide a comprehensive combination of past photographs and videos. For example, the generation AI can visually understand the building's historical changes based on past photographs and videos. In this way, the user can visually understand the building's historical changes by adding past photographs and videos of the building.

[0065] The information providing unit can analyze the user's emotions and provide information that the user emotionally empathizes with preferentially. For example, the information providing unit can use an emotion estimation function to provide information that the user most emotionally empathizes with preferentially. For example, it can provide information about buildings that the user is impressed with preferentially. The information providing unit can also evaluate the degree of empathy with the information based on the user's emotion data. For example, the generation AI determines the degree of empathy with the information based on the user's emotion score. The information providing unit can also provide information comprehensively by combining the emotion data and the content of the information. For example, the generation AI provides information that the user emotionally empathizes with based on the emotion data and the content of the information. In this way, by analyzing the user's emotions, it is possible to provide information that the user emotionally empathizes with preferentially.

[0066] The information providing unit can add information about tourist spots and restaurants around the building and suggest a sightseeing plan. For example, the information providing unit adds information about tourist spots around the building to the information provided by the generation AI. For example, it provides information about famous places and tourist destinations near shrines. The information providing unit can also provide information about restaurants. For example, it provides information about restaurants and cafes. The information providing unit can also suggest a sightseeing plan by combining information about tourist spots and restaurants. For example, the generation AI can suggest the most suitable sightseeing plan for the user based on information about tourist spots and restaurants. This makes it possible to suggest a sightseeing plan by adding information about tourist spots and restaurants around the building.

[0067] The information provision unit can add information about events and special exhibitions related to the building and suggest the best time to visit. For example, the information provision unit adds event information related to the building to the information provided by the generation AI. For example, it provides information about festivals and special ceremonies held at shrines. The information provision unit can also provide information about special exhibitions. For example, it provides information about limited-time exhibitions and special exhibitions. The information provision unit can also combine event information and special exhibition information to suggest the best time to visit. For example, the generation AI can suggest the best time to visit to the user based on the event information and special exhibition information. In this way, the generation AI can suggest the best time to visit by adding information about events and special exhibitions related to the building.

[0068] The information providing unit can analyze the user's emotions and provide information that the user emotionally empathizes with preferentially. For example, the information providing unit can use an emotion estimation function to provide information that the user most emotionally empathizes with preferentially. For example, it can provide information about buildings that the user is impressed with preferentially. The information providing unit can also evaluate the degree of empathy with the information based on the user's emotion data. For example, the generation AI determines the degree of empathy with the information based on the user's emotion score. The information providing unit can also provide information comprehensively by combining the emotion data and the content of the information. For example, the generation AI provides information that the user emotionally empathizes with based on the emotion data and the content of the information. In this way, by analyzing the user's emotions, it is possible to provide information that the user emotionally empathizes with preferentially.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The information provision system can also be equipped with a health management unit that monitors the user's health condition and proposes an appropriate sightseeing plan. For example, it can monitor the user's step count and heart rate and propose a shorter sightseeing route to avoid excessive exercise. The health management unit can also propose appropriate restaurants based on the user's dietary restrictions. For example, it can select a restaurant suitable for the user based on allergy information and diet information. Furthermore, the health management unit can combine the user's health data with the sightseeing plan for comprehensive analysis. For example, the generation AI can propose a reasonable sightseeing plan based on the user's health data. This makes it possible to provide a sightseeing plan that takes the user's health condition into consideration.

[0071] The information provision system can also be equipped with a relaxation determination unit that analyzes the user's emotions and suggests tourist spots that are emotionally relaxing. For example, the emotion estimation function can be used to suggest places where the user can relax when they are feeling stressed. The relaxation determination unit can also evaluate the level of relaxation based on the user's emotional data. For example, the generation AI can determine the level of relaxation based on the user's emotional score. The relaxation determination unit can also perform a comprehensive analysis by combining the emotional data and the characteristics of the tourist spots. For example, the generation AI can identify places that are emotionally relaxing based on the emotional data and the characteristics of the tourist spots. In this way, tourist spots that are emotionally relaxing can be suggested by analyzing the user's emotions.

[0072] The information provision system may further include a travel history analysis unit that analyzes the user's past travel history and prioritizes suggesting unvisited buildings. For example, unvisited buildings may be identified based on data on buildings visited in the past. The travel history analysis unit may also suggest unvisited buildings based on the user's interests. For example, the generation AI may analyze the user's interests and suggest unvisited buildings based on those. The travel history analysis unit may also perform a comprehensive analysis by combining the travel history and the characteristics of the buildings. For example, the generation AI may suggest unvisited buildings that are optimal for the user based on the travel history and the characteristics of the buildings. In this way, unvisited buildings can be prioritized by analyzing the user's past travel history.

[0073] The information provision system may further include an event determination unit that analyzes the user's emotions and suggests events that provide an emotionally positive experience. For example, an emotion estimation function may be used to suggest events that the user feels positive about. The event determination unit may also evaluate the positivity of an event based on the user's emotion data. For example, the generation AI may determine the positivity of an event based on the user's emotion score. The event determination unit may also perform a comprehensive analysis by combining the emotion data and the event features. For example, the generation AI may identify events that provide an emotionally positive experience based on the emotion data and the event features. This allows the system to suggest events that provide an emotionally positive experience by analyzing the user's emotions.

[0074] The information provision system may further include a music provision unit that provides music according to the user's current mood. For example, an emotion estimation function may be used to provide music suitable for when the user wants to relax. The music provision unit may also select music based on the user's emotion data. For example, the generation AI may select music based on the user's emotion score. The music provision unit may also perform a comprehensive analysis by combining emotion data and music characteristics. For example, the generation AI may provide the user with music that is optimal for the user based on the emotion data and music characteristics. In this way, music that matches the user's current mood can be provided by analyzing the user's emotions.

[0075] The information provision system may further include an activity suggestion unit that suggests related activities based on the user's interests. For example, if the user is interested in historical buildings, the system may suggest historical events or workshops taking place in the vicinity. The activity suggestion unit may also determine the priority of activities based on the user's interests. For example, the generation AI may analyze the user's interests and suggest activities based on those. The activity suggestion unit may also perform a comprehensive analysis by combining the interests and the characteristics of the activities. For example, the generation AI may suggest the most suitable activities for the user based on the interests and the characteristics of the activities. This allows the system to suggest related activities based on the user's interests.

[0076] The information provision system can also be equipped with a filtering function that analyzes the user's emotions and avoids emotionally negative information. For example, an emotion estimation function can be used to filter out information that causes stress to the user. The filtering function can also evaluate the negativity of information based on the user's emotional data. For example, the generation AI determines the negativity of information based on the user's emotional score. The filtering function can also perform a comprehensive analysis by combining the emotional data and the content of the information. For example, the generation AI filters out emotionally negative information based on the emotional data and the content of the information. This makes it possible to avoid emotionally negative information by analyzing the user's emotions.

[0077] The information provision system may further include a traffic information provision unit that provides traffic information in real time based on the user's current location information. For example, the system may identify the user's current location based on GPS data and suggest the optimal means of transportation. The traffic information provision unit may also analyze real-time traffic conditions to suggest the optimal route. For example, the generation AI may suggest the optimal route based on traffic congestion and the operation status of public transportation. The traffic information provision unit may also perform comprehensive analysis by combining the current location information and traffic data. For example, the generation AI may suggest the optimal means of transportation and route to the user based on the current location information and traffic data. This allows traffic information to be provided in real time based on the user's current location information.

[0078] The information provision system may further include a review analysis unit that provides building evaluations based on users' past reviews and evaluations. For example, the system may analyze reviews of buildings that the user has visited in the past and provide evaluations from other users. The review analysis unit may also comprehensively analyze building evaluations based on user evaluation data. For example, the generation AI may determine the building evaluation based on the user's evaluation score. The review analysis unit may also perform a comprehensive analysis by combining evaluation data and building features. For example, the generation AI may provide the user with the optimal building evaluation based on the evaluation data and building features. This allows the system to provide building evaluations based on users' past reviews and evaluations.

[0079] The information provision system may further include a story provision unit that analyzes the user's emotions and provides a story that the user empathizes with emotionally. For example, an emotion estimation function may be used to provide a story that the user most empathizes with. The story provision unit may also evaluate the degree of empathy of a story based on the user's emotion data. For example, the generation AI may determine the degree of empathy of a story based on the user's emotion score. The story provision unit may also provide a comprehensive story by combining the emotion data and the content of the story. For example, the generation AI may provide an emotionally empathetic story based on the emotion data and the content of the story. In this way, it is possible to provide an emotionally empathetic story by analyzing the user's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The photo acquisition unit acquires photos from the user. For example, photos can be uploaded via a smartphone app. Photos can also be provided using a web interface. Furthermore, the photo acquisition unit can acquire photos from cloud storage. For example, the photo acquisition unit acquires photos stored by the user on Google Drive or Dropbox. Step 2: The building determination unit analyzes the photos acquired by the photo acquisition unit to identify the building. For example, the generation AI uses image recognition technology to extract the features of the building and identify it. The generation AI can also use machine learning algorithms to identify the building. The generation AI can also analyze and identify the building's shape and decoration. For example, the generation AI can identify the building based on the shape and decoration of the roof. Step 3: The information acquisition unit acquires information about the building identified by the building determination unit from an online database. For example, the generation AI may refer to a public database to acquire information about the building's history and architectural style. The generation AI may also refer to a commercial database to acquire information about important events or people. The generation AI may also select a specific database to collect the necessary information. For example, the generation AI may select an appropriate database based on the identification of the building and collect information. Step 4: The information providing unit provides the information acquired by the information acquiring unit to the user. For example, the generation AI provides the acquired information to the user as a text message. The generation AI can also provide the information as an audio guide. The generation AI can also provide the information as an in-app notification. For example, the generation AI provides the information by sending a notification to the user's smartphone.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo acquisition unit that acquires a photo from a user; a building determination unit that analyzes the photograph acquired by the photograph acquisition unit and identifies a building; an information acquisition unit that acquires information about the building identified by the building determination unit from an online database; an information providing unit that provides the information acquired by the information acquiring unit to a user; A system characterized by:

2. The building determination unit Analyzing the environment around the building and improving the accuracy of identifying the building 2. The system of claim 1.

3. The building determination unit Analyzing the deterioration status and need for repair of the buildings will be useful for conservation activities.

2. The system of claim 1.

4. The information acquisition unit Analyze historical documents and old maps related to the building to provide more detailed information 2. The system of claim 1.

5. The information providing unit Add a 3D model and virtual tour of the building, allowing users to virtually visit the building.

2. The system of claim 1.

6. The building determination unit Analyzing the user's emotions and determining emotionally significant buildings as a priority 2. The system of claim 1.

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