System

Abstract

To provide a system that realistically reproduces the scenery of an actual location simply by inputting a place name.SOLUTION: A system according to the embodiment comprises a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives input of a place name. The collection unit collects data based on the place name received by the reception unit. The generation unit analyzes the data collected by the collection unit, and generates an image that reproduces the scenery of an actual location. The provision unit provides the image generated by the generation unit to a user.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to accurately reproduce the scenery of an actual location even if the place name is input.

[0005] The system according to the embodiment aims to realistically reproduce the scenery of an actual place simply by inputting the place name. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives an input of a place name. The collection unit collects data based on the place name received by the reception unit. The generation unit analyzes the data collected by the collection unit and generates an image that reproduces the scenery of the actual place. The provision unit provides the image generated by the generation unit to a user. Effect of the Invention

[0007] The system according to the embodiment can realistically reproduce the scenery of an actual place simply by inputting the place name. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The scenery reproduction system according to the embodiment of the present invention is a system that realistically reproduces the scenery of an actual place by inputting a place name. When a place name is input, this scenery reproduction system outputs a "scenery that looks like it has been seen somewhere before, but is nowhere." However, the present invention realizes the generation of an image that also has the "atmosphere of a real place." First, the user inputs a place name. For example, a specific place name such as "Tokyo Tower" or "Eiffel Tower" is input. This information is input to the generation AI. Next, the generation AI analyzes the input place name and collects data on the actual place related to the place name. For example, image data of Tokyo Tower and surrounding scenery data are collected. At this time, data can be obtained from public databases on the Internet, geographic information systems (GIS), and the like. Based on the collected data, the generation AI generates an image that realistically reproduces the scenery of the actual place. The generation AI analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, a realistic scenery image is generated taking into account the buildings and roads around Tokyo Tower, seasonal scenery, and the like. The generated image is provided to the user. The user can view the realistic scenery image generated based on the input place name. This allows the user to have an experience as if they were actually in that place. This system makes it possible to realistically reproduce the scenery of an actual place simply by inputting the place name. For example, when planning a trip, you can check the scenery of a place you want to visit in advance. It is also expected to be applied in various fields such as education and tourism. This allows the scenery reproduction system to realistically reproduce the scenery of an actual place simply by the user inputting the place name.

[0029] The scenery reproduction system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives a place name input by a user. For example, the user can input a specific place name such as "Tokyo Tower" or "Eiffel Tower". The collection unit collects data based on the place name received by the reception unit. The collection unit can acquire data, for example, from a public database on the Internet or a geographic information system (GIS). For example, the collection unit collects image data of Tokyo Tower and surrounding scenery data. The generation unit analyzes the data collected by the collection unit and generates an image that reproduces the scenery of an actual place. The generation unit analyzes, for example, the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, the generation unit generates a realistic scenery image by taking into account the buildings and roads around Tokyo Tower, the scenery of each season, and the like. The provision unit provides the image generated by the generation unit to the user. The provision unit can, for example, interactively display the generated image. The provision unit can also include a storage unit and a sharing unit for the user to store and share the generated image. As a result, the scenery reproduction system according to the embodiment can realistically reproduce the scenery of an actual place simply by the user inputting the place name. For example, when making travel plans, the scenery of a place you want to visit can be confirmed in advance. In addition, applications in various fields such as education and tourism are expected.

[0030] The reception unit accepts the user's input of a place name. For example, the user can input a specific place name such as "Tokyo Tower" or "Eiffel Tower". The reception unit provides an interface for accurately recognizing the place name input by the user and passing it to the entire system. Specifically, the reception unit has a function of analyzing the character string input by the user and presenting place name candidates. For example, if the user inputs "Tokyo", the system displays candidates such as "Tokyo Tower", "Tokyo Skytree", and "Tokyo Station" so that the user can select. The reception unit also supports voice input, allowing the user to input a place name by voice. Using voice recognition technology, the user's speech is converted into text and the place name is identified. Furthermore, the reception unit can save the user's input history and reuse place names input in the past. This allows the user to easily re-input frequently visited places or places of interest. The reception unit provides an interface for accurately recognizing the place name input by the user and passing it to the entire system. Specifically, the reception unit has a function of analyzing the character string input by the user and presenting place name candidates. For example, if a user inputs "Tokyo," the system will display candidates such as "Tokyo Tower," "Tokyo Skytree," and "Tokyo Station" for the user to select. The reception unit also supports voice input, allowing the user to input place names by voice. Using voice recognition technology, the user's speech is converted into text and the place name is identified. Furthermore, the reception unit can save the user's input history and reuse place names entered in the past. This allows the user to easily re-input frequently visited places or places of interest.

[0031] The collection unit collects data based on the place names accepted by the reception unit. The collection unit can acquire data from, for example, public databases on the Internet or geographic information systems (GIS). Specifically, the collection unit collects a wide range of information, such as image data, geographic information, meteorological data, and historical background related to the place names. For example, when collecting data related to Tokyo Tower, the collection unit collects high-resolution images of Tokyo Tower, geographic information of surrounding buildings and roads, meteorological data for each season, and information related to the history and cultural background of Tokyo Tower. In order to efficiently collect these data, the collection unit is provided with a system that integrates and centrally manages multiple data sources. For example, the collection unit acquires image data from public databases on the Internet, acquires geographic information from the GIS, and acquires meteorological information from a meteorological database. This allows the collection unit to quickly and accurately collect various data related to place names. Furthermore, the collection unit has a function of evaluating the quality of the collected data and eliminating inaccurate and duplicate data. This allows the collection unit to provide highly reliable data to the generation unit. The collection unit quickly and accurately collects various data related to place names, and can improve the performance of the entire system.

[0032] The generating unit analyzes the data collected by the collecting unit and generates an image that reproduces the scenery of the actual place. The generating unit, for example, analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. Specifically, the generating unit generates a scenery image using deep learning technology based on the collected image data. For example, the generating unit generates a realistic scenery image by considering the buildings and roads around Tokyo Tower and the scenery by season. The generating unit utilizes the collected geographic information and meteorological data in the image generation process to reproduce detailed characteristics of the scenery. For example, based on the collected meteorological data, the generating unit generates an image that reflects the scenery by season and the changes in weather. In addition, the generating unit considers the collected historical background and cultural information to reproduce the atmosphere and characteristics of the scenery more realistically. The generating unit integrates these data and generates a realistic scenery image based on the place name entered by the user. Furthermore, the generating unit has a function of evaluating the quality of the generated image and correcting or improving it as necessary. This allows the generating unit to always provide high-quality scenery images. The generation unit analyzes the collected data and generates images that realistically reproduce the scenery of the actual location, thereby providing the user with a realistic experience.

[0033] The providing unit provides the image generated by the generating unit to the user. The providing unit can, for example, interactively display the generated image. Specifically, the providing unit provides an interface that allows the user to freely operate the generated image. For example, the user can zoom in and out of the generated scenery image and change the viewpoint. The providing unit can also include a storage unit and a sharing unit for the user to save and share the generated image. For example, the user can save the generated scenery image in his or her own device or share it with other users through SNS or email. Furthermore, the providing unit has a function of displaying information related to the generated image. For example, related information such as place names, historical background, and tourist information is displayed for the generated scenery image, so that the user can have a deeper understanding of the scenery. The providing unit can provide the user with high convenience and satisfaction through these functions. The providing unit can provide the user with a realistic experience by interactively displaying the generated image and providing an interface that the user can freely operate. Furthermore, the providing unit can provide the user with a function for saving and sharing the generated image, so that the user can share the generated scenery image with other users and enjoy it. As a result, the providing unit can provide the user with high convenience and satisfaction.

[0034] The collection unit can acquire data from a public database or a geographic information system on the Internet. The collection unit can acquire data from, for example, a public database on the Internet. For example, the collection unit collects data using open data or an API. The collection unit can also acquire data from a geographic information system (GIS). For example, the collection unit collects data using GIS software or a geographic database. This allows the collection unit to collect data for realistically reproducing the scenery of an actual location. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data acquired from a public database or a geographic information system on the Internet to the generation AI and have the generation AI perform analysis of the data.

[0035] The generation unit can analyze the collected data and generate an image that reflects the characteristics or atmosphere of the actual place. The generation unit, for example, analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, the generation unit generates a realistic landscape image by considering the buildings and roads around Tokyo Tower, the scenery of each season, and the like. The generation unit can also generate an image that reflects the characteristics and atmosphere of the actual place based on the collected data. For example, the generation unit generates an image by considering the shape of the building, the natural environment, cultural elements, and the like. As a result, the generation unit can generate a realistic image that reflects the characteristics and atmosphere of the actual place by analyzing the collected data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the collected data to the generation AI and cause the generation AI to generate an image.

[0036] The providing unit can interactively display the generated image. The providing unit can, for example, interactively display the generated image. For example, the providing unit enables the user to operate the generated image through a user interface. The providing unit can also provide an operation method for interactively displaying the generated image. For example, the providing unit enables the user to enlarge / reduce and rotate the image. As a result, the providing unit can make it easier for the user to operate a realistic landscape image by interactively displaying the generated image. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause the generating AI to execute the operation method for interactively displaying the generated image.

[0037] The providing unit may include a storage unit or a sharing unit for the user to save and share the generated image. The providing unit may include, for example, a storage unit for the user to save the generated image. For example, the providing unit provides a function of saving the generated image in the user's device. The providing unit may also include a sharing unit for the user to share the generated image. For example, the providing unit provides a function of sharing the generated image on social media or by email. This allows the providing unit to save and share the generated image by the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may cause the generating AI to save or share the generated image.

[0038] The reception unit can analyze the user's past place name input history and suggest an appropriate input method. The reception unit can, for example, analyze the user's past place name input history and suggest an optimal input method. For example, the reception unit can automatically display place names that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest place names to be used in a specific time period from the user's past input history. As a result, the reception unit can suggest an optimal input method based on the user's past input history, thereby making the input of place names more efficient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past place name input history to the generation AI and cause the generation AI to execute the suggestion of an optimal input method.

[0039] The reception unit can automatically complete related place names based on the user's current location information when a place name is input. For example, when a place name is input, the reception unit automatically completes related place names taking into account the user's current location information. For example, when a user inputs a current location, the reception unit automatically displays nearby place names as candidates. In addition, when a user inputs a destination, the reception unit can suggest optimal candidate locations taking into account the distance from the current location. In addition, when a user uses an app while moving, the reception unit can also update the current location in real time and automatically complete related place names. This makes it easier for the reception unit to automatically complete related place names by taking into account the user's current location information. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to execute automatic completion of related place names.

[0040] The reception unit can analyze the user's social media activity when inputting a place name and suggest related place names. For example, the reception unit can analyze the user's social media activity when inputting a place name and suggest related place names. For example, the reception unit suggests places where the user frequently checks in on social media as candidates. The reception unit can also suggest places visited by influencers the user follows as candidates. The reception unit can also suggest events and places in which the user shows interest on social media as candidates. As a result, the reception unit suggests related place names based on the user's social media activity, thereby making the input of place names more efficient. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of related place names.

[0041] The reception unit can display related place names preferentially based on the user's past travel history when inputting a place name. For example, when inputting a place name, the reception unit preferentially displays related place names in consideration of the user's past travel history. For example, the reception unit preferentially displays related place names based on places the user has visited in the past. The reception unit can also suggest other tourist spots in the same area from the user's past travel history. The reception unit can also analyze the user's past travel history and suggest new place names that may be of interest. As a result, the reception unit preferentially displays related place names based on the user's past travel history, thereby making the input of place names more efficient. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data to the generation AI and cause the generation AI to execute the preferential display of related place names.

[0042] The collection unit can select an appropriate data source based on the user's past search history when collecting data. For example, the collection unit selects an optimal data source by referring to the user's past search history when collecting data. For example, the collection unit preferentially selects a data source related to a place name searched for by the user in the past. The collection unit can also select a data source related to a specific theme from the user's past search history. The collection unit can also analyze the user's past search history and select the most reliable data source. This allows the collection unit to select an optimal data source based on the user's past search history, thereby making data collection more efficient. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's past search history data to the generation AI and cause the generation AI to select an appropriate data source.

[0043] The collection unit can preferentially collect data according to the season and time of the place name when collecting data. For example, the collection unit preferentially collects data according to the season and time of the place name when collecting data. For example, the collection unit preferentially collects scenery data for each season to reproduce a realistic scenery. The collection unit can also preferentially collect scenery data for each time period to reflect changes between day and night. The collection unit can also preferentially collect data of specific events and festivals to reproduce a realistic atmosphere. As a result, the collection unit can reproduce a more realistic scenery by preferentially collecting data according to the season and time period of the place name. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause the generation AI to collect data according to the season and time period of the place name.

[0044] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects data of a location close to the user's current location. In addition, the collection unit can preferentially collect data of a location close to the user's destination. In addition, when the user uses an app while moving, the collection unit can also collect data related to the current location in real time. As a result, the collection unit preferentially collects highly relevant data based on the geographical location information of the user, thereby making data collection more efficient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0045] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. The collection unit, for example, analyzes the social media activity of the user at the time of data collection and collects related data. For example, the collection unit collects data of places where the user frequently checks in on social media. The collection unit can also collect data of places visited by influencers followed by the user. The collection unit can also collect data of events and places in which the user shows interest on social media. As a result, the collection unit collects related data based on the user's social media activity, thereby making data collection more efficient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0046] The generation unit can adjust the detail of the generation based on the importance of the collected data when generating an image. The generation unit adjusts the detail of the generation based on the importance of the collected data when generating an image, for example. For example, the generation unit generates a detailed image based on data of important landmarks and tourist spots. The generation unit can also generate a simple image based on data of a general location. The generation unit can also generate an image that reproduces a detailed atmosphere based on data of a specific event or festival. This allows the generation unit to generate a more appropriate image by adjusting the detail of the generation based on the importance of the collected data. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the detail of the generation.

[0047] The generation unit can apply different generation algorithms depending on the category of the place name when generating an image. The generation unit can apply different generation algorithms depending on the category of the place name when generating an image. For example, in the case of a tourist destination, the generation unit applies a generation algorithm dedicated to tourist destinations. In addition, in the case of a natural landscape, the generation unit can apply a generation algorithm dedicated to natural landscapes. In addition, in the case of an urban landscape, the generation unit can also apply a generation algorithm dedicated to urban landscapes. In this way, the generation unit can generate a more appropriate image by applying a generation algorithm according to the category of the place name. Part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or can be performed without using AI. For example, the generation unit can input the category of the place name to the generation AI and cause the generation AI to apply different generation algorithms.

[0048] The generation unit can determine the priority of generation based on the time of submission of the place name when generating an image. The generation unit, for example, determines the priority of generation based on the time of submission of the place name when generating an image. For example, when the user is in a hurry, the generation unit preferentially generates an image that can be generated in a short time. In addition, when the user is relaxed, the generation unit can preferentially generate a detailed image. In addition, when the user submits during a specific event or festival, the generation unit can also preferentially generate an image related to that time. In this way, the generation unit can generate a more appropriate image by determining the priority of generation based on the time of submission of the place name. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the time of submission of the place name to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0049] The generation unit can adjust the order of generation based on the relevance of place names when generating images. For example, the generation unit adjusts the order of generation based on the relevance of place names when generating images. For example, the generation unit preferentially generates images of important landmarks and tourist spots. The generation unit can also postpone images of general places. The generation unit can also preferentially generate images related to specific events and festivals. In this way, the generation unit can generate more appropriate images by adjusting the order of generation based on the relevance of place names. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of place names to the generation AI and cause the generation AI to adjust the order of generation.

[0050] The providing unit can select an appropriate display method based on the user's past operation history when displaying an image. For example, the providing unit selects an optimal display method by referring to the user's past operation history when displaying an image. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also select a display method related to a specific theme from the user's past operation history. The providing unit can also analyze the user's past operation history and select a display method with the highest visibility. As a result, the providing unit can select an optimal display method based on the user's past operation history, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generating AI and cause the generating AI to select an appropriate display method.

[0051] The providing unit can select an appropriate display method based on the device information of the user when displaying an image. For example, the providing unit selects an optimal display method in consideration of the device information of the user when displaying an image. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can provide a display method optimized for a large screen. In addition, when the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. As a result, the providing unit can select an optimal display method based on the device information of the user, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the generating AI and cause the generating AI to select an appropriate display method.

[0052] The providing unit can select an appropriate display method based on the geographical location information of the user when displaying an image. For example, the providing unit selects an optimal display method in consideration of the geographical location information of the user when displaying an image. For example, the providing unit preferentially displays images of locations close to the user's current location. In addition, the providing unit can preferentially display images of locations close to the user's destination. In addition, when the user uses an app while moving, the providing unit can also display images related to the current location in real time. As a result, the providing unit can select an optimal display method based on the geographical location information of the user, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to select an appropriate display method.

[0053] The providing unit can analyze the social media activity of the user and display related images when the image is displayed. The providing unit can, for example, analyze the social media activity of the user and display related images when the image is displayed. For example, the providing unit displays images of places where the user frequently checks in on social media. The providing unit can also display images of places visited by influencers followed by the user. The providing unit can also display images of events and places in which the user is interested on social media. This allows the providing unit to display related images based on the user's social media activity, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to execute display of related images.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can collect historical data related to the place name input by the user. For example, the collection unit collects photos of Tokyo Tower when it was first built and records of past events. The collection unit can also collect cultural data, such as literary works and movie scenes related to the place name. Furthermore, the collection unit can preferentially collect data related to a particular era or theme according to the user's interests. This allows the collection unit to provide the user with richer information.

[0056] The providing unit can display the generated scenery image using augmented reality (AR) technology. For example, when a user views the scenery image through a smartphone, the scenery image can be displayed superimposed on an actual scenery. The providing unit can also enable the user to rotate the scenery image 360 ​​degrees and view it. Furthermore, the providing unit can provide a function that enables the user to add comments and tags to the scenery image. This allows the providing unit to allow the user to enjoy the generated scenery image in a more interactive manner.

[0057] The reception unit can provide related tourist information in real time when the user inputs a place name. For example, when the user inputs "Tokyo Tower," information on nearby tourist spots and restaurants is displayed. The reception unit can also provide traffic information and travel time from the user's current location to the destination. Furthermore, the reception unit can suggest related place names and tourist information based on the user's past search history. This allows the reception unit to provide more convenient information when the user inputs a place name.

[0058] The providing unit can display the generated scenery image in an optimized manner for the user's device. For example, the resolution and layout of the image can be adjusted according to different devices, such as a smartphone, tablet, or PC. The providing unit can also select an optimal display method based on the screen size and resolution of the user's device. Furthermore, the providing unit can automatically adjust the display of the image when the user rotates the device to landscape or portrait orientation. This allows the providing unit to comfortably view scenery images regardless of which device the user uses.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit receives the user's input of a place name. For example, the user can input a specific place name such as "Tokyo Tower" or "Eiffel Tower." Step 2: The collecting unit collects data based on the place name accepted by the accepting unit. The collecting unit can acquire data, for example, from a public database on the Internet or a geographic information system (GIS). For example, the collecting unit collects image data of Tokyo Tower and surrounding scenery data. Step 3: The generation unit analyzes the data collected by the collection unit and generates an image that reproduces the scenery of the actual place. For example, the generation unit analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, the generation unit generates a realistic scenery image by taking into account the buildings and roads around Tokyo Tower, the scenery by season, etc. Step 4: The providing unit provides the image generated by the generating unit to the user. The providing unit can, for example, interactively display the generated image. The providing unit can also include a storage unit and a sharing unit for the user to store and share the generated image.

[0061] (Example 2) The scenery reproduction system according to the embodiment of the present invention is a system that realistically reproduces the scenery of an actual place by inputting a place name. When a place name is input, this scenery reproduction system outputs a "scenery that looks like it has been seen somewhere before, but is nowhere." However, the present invention realizes the generation of an image that also has the "atmosphere of a real place." First, the user inputs a place name. For example, a specific place name such as "Tokyo Tower" or "Eiffel Tower" is input. This information is input to the generation AI. Next, the generation AI analyzes the input place name and collects data on the actual place related to the place name. For example, image data of Tokyo Tower and surrounding scenery data are collected. At this time, data can be obtained from public databases on the Internet, geographic information systems (GIS), and the like. Based on the collected data, the generation AI generates an image that realistically reproduces the scenery of the actual place. The generation AI analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, a realistic scenery image is generated taking into account the buildings and roads around Tokyo Tower, seasonal scenery, and the like. The generated image is provided to the user. The user can view the realistic scenery image generated based on the input place name. This allows the user to have an experience as if they were actually in that place. This system makes it possible to realistically reproduce the scenery of an actual place simply by inputting the place name. For example, when planning a trip, you can check the scenery of a place you want to visit in advance. It is also expected to be applied in various fields such as education and tourism. This allows the scenery reproduction system to realistically reproduce the scenery of an actual place simply by the user inputting the place name.

[0062] The scenery reproduction system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives a place name input by a user. For example, the user can input a specific place name such as "Tokyo Tower" or "Eiffel Tower". The collection unit collects data based on the place name received by the reception unit. The collection unit can acquire data, for example, from a public database on the Internet or a geographic information system (GIS). For example, the collection unit collects image data of Tokyo Tower and surrounding scenery data. The generation unit analyzes the data collected by the collection unit and generates an image that reproduces the scenery of an actual place. The generation unit analyzes, for example, the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, the generation unit generates a realistic scenery image by taking into account the buildings and roads around Tokyo Tower, the scenery of each season, and the like. The provision unit provides the image generated by the generation unit to the user. The provision unit can, for example, interactively display the generated image. The provision unit can also include a storage unit and a sharing unit for the user to store and share the generated image. As a result, the scenery reproduction system according to the embodiment can realistically reproduce the scenery of an actual place simply by the user inputting the place name. For example, when making travel plans, the scenery of a place you want to visit can be confirmed in advance. In addition, applications in various fields such as education and tourism are expected.

[0063] The reception unit accepts the user's input of a place name. For example, the user can input a specific place name such as "Tokyo Tower" or "Eiffel Tower". The reception unit provides an interface for accurately recognizing the place name input by the user and passing it to the entire system. Specifically, the reception unit has a function of analyzing the character string input by the user and presenting place name candidates. For example, if the user inputs "Tokyo", the system displays candidates such as "Tokyo Tower", "Tokyo Skytree", and "Tokyo Station" so that the user can select. The reception unit also supports voice input, allowing the user to input a place name by voice. Using voice recognition technology, the user's speech is converted into text and the place name is identified. Furthermore, the reception unit can save the user's input history and reuse place names input in the past. This allows the user to easily re-input frequently visited places or places of interest. The reception unit provides an interface for accurately recognizing the place name input by the user and passing it to the entire system. Specifically, the reception unit has a function of analyzing the character string input by the user and presenting place name candidates. For example, if a user inputs "Tokyo," the system will display candidates such as "Tokyo Tower," "Tokyo Skytree," and "Tokyo Station" for the user to select. The reception unit also supports voice input, allowing the user to input place names by voice. Using voice recognition technology, the user's speech is converted into text and the place name is identified. Furthermore, the reception unit can save the user's input history and reuse place names entered in the past. This allows the user to easily re-input frequently visited places or places of interest.

[0064] The collection unit collects data based on the place names accepted by the reception unit. The collection unit can acquire data from, for example, public databases on the Internet or geographic information systems (GIS). Specifically, the collection unit collects a wide range of information, such as image data, geographic information, meteorological data, and historical background related to the place names. For example, when collecting data related to Tokyo Tower, the collection unit collects high-resolution images of Tokyo Tower, geographic information of surrounding buildings and roads, meteorological data for each season, and information related to the history and cultural background of Tokyo Tower. In order to efficiently collect these data, the collection unit is provided with a system that integrates and centrally manages multiple data sources. For example, the collection unit acquires image data from public databases on the Internet, acquires geographic information from the GIS, and acquires meteorological information from a meteorological database. This allows the collection unit to quickly and accurately collect various data related to place names. Furthermore, the collection unit has a function of evaluating the quality of the collected data and eliminating inaccurate and duplicate data. This allows the collection unit to provide highly reliable data to the generation unit. The collection unit quickly and accurately collects various data related to place names, and can improve the performance of the entire system.

[0065] The generating unit analyzes the data collected by the collecting unit and generates an image that reproduces the scenery of the actual place. The generating unit, for example, analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. Specifically, the generating unit generates a scenery image using deep learning technology based on the collected image data. For example, the generating unit generates a realistic scenery image by considering the buildings and roads around Tokyo Tower and the scenery by season. The generating unit utilizes the collected geographic information and meteorological data in the image generation process to reproduce detailed characteristics of the scenery. For example, based on the collected meteorological data, the generating unit generates an image that reflects the scenery by season and the changes in weather. In addition, the generating unit considers the collected historical background and cultural information to reproduce the atmosphere and characteristics of the scenery more realistically. The generating unit integrates these data and generates a realistic scenery image based on the place name entered by the user. Furthermore, the generating unit has a function of evaluating the quality of the generated image and correcting or improving it as necessary. This allows the generating unit to always provide high-quality scenery images. The generation unit analyzes the collected data and generates images that realistically reproduce the scenery of the actual location, thereby providing the user with a realistic experience.

[0066] The providing unit provides the image generated by the generating unit to the user. The providing unit can, for example, interactively display the generated image. Specifically, the providing unit provides an interface that allows the user to freely operate the generated image. For example, the user can zoom in and out of the generated scenery image and change the viewpoint. The providing unit can also include a storage unit and a sharing unit for the user to save and share the generated image. For example, the user can save the generated scenery image in his or her own device or share it with other users through SNS or email. Furthermore, the providing unit has a function of displaying information related to the generated image. For example, related information such as place names, historical background, and tourist information is displayed for the generated scenery image, so that the user can have a deeper understanding of the scenery. The providing unit can provide the user with high convenience and satisfaction through these functions. The providing unit can provide the user with a realistic experience by interactively displaying the generated image and providing an interface that the user can freely operate. Furthermore, the providing unit can provide the user with a function for saving and sharing the generated image, so that the user can share the generated scenery image with other users and enjoy it. As a result, the providing unit can provide the user with high convenience and satisfaction.

[0067] The collection unit can acquire data from a public database or a geographic information system on the Internet. The collection unit can acquire data from, for example, a public database on the Internet. For example, the collection unit collects data using open data or an API. The collection unit can also acquire data from a geographic information system (GIS). For example, the collection unit collects data using GIS software or a geographic database. This allows the collection unit to collect data for realistically reproducing the scenery of an actual location. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data acquired from a public database or a geographic information system on the Internet to the generation AI and have the generation AI perform analysis of the data.

[0068] The generation unit can analyze the collected data and generate an image that reflects the characteristics or atmosphere of the actual place. The generation unit, for example, analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, the generation unit generates a realistic landscape image by considering the buildings and roads around Tokyo Tower, the scenery of each season, and the like. The generation unit can also generate an image that reflects the characteristics and atmosphere of the actual place based on the collected data. For example, the generation unit generates an image by considering the shape of the building, the natural environment, cultural elements, and the like. As a result, the generation unit can generate a realistic image that reflects the characteristics and atmosphere of the actual place by analyzing the collected data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the collected data to the generation AI and cause the generation AI to generate an image.

[0069] The providing unit can interactively display the generated image. The providing unit can, for example, interactively display the generated image. For example, the providing unit enables the user to operate the generated image through a user interface. The providing unit can also provide an operation method for interactively displaying the generated image. For example, the providing unit enables the user to enlarge / reduce and rotate the image. As a result, the providing unit can make it easier for the user to operate a realistic landscape image by interactively displaying the generated image. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause the generating AI to execute the operation method for interactively displaying the generated image.

[0070] The providing unit may include a storage unit or a sharing unit for the user to save and share the generated image. The providing unit may include, for example, a storage unit for the user to save the generated image. For example, the providing unit provides a function of saving the generated image in the user's device. The providing unit may also include a sharing unit for the user to share the generated image. For example, the providing unit provides a function of sharing the generated image on social media or by email. This allows the providing unit to save and share the generated image by the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may cause the generating AI to save or share the generated image.

[0071] The reception unit can estimate the user's emotion and customize the place name input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and customizes the place name input interface based on the estimated user's emotion. For example, when the user is excited, the reception unit provides a colorful and visually stimulating interface. When the user is relaxed, the reception unit can provide an interface with calm colors to make the input work enjoyable. When the user is stressed, the reception unit can provide a simple and intuitive interface to minimize the input procedure. As a result, the reception unit provides an interface according to the user's emotion, making the input of place names more comfortable. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The reception unit can analyze the user's past place name input history and suggest an appropriate input method. The reception unit can, for example, analyze the user's past place name input history and suggest an optimal input method. For example, the reception unit can automatically display place names that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest place names to be used in a specific time period from the user's past input history. As a result, the reception unit can suggest an optimal input method based on the user's past input history, thereby making the input of place names more efficient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past place name input history to the generation AI and cause the generation AI to execute the suggestion of an optimal input method.

[0073] The reception unit can automatically complete related place names based on the user's current location information when a place name is input. For example, when a place name is input, the reception unit automatically completes related place names taking into account the user's current location information. For example, when a user inputs a current location, the reception unit automatically displays nearby place names as candidates. In addition, when a user inputs a destination, the reception unit can suggest optimal candidate locations taking into account the distance from the current location. In addition, when a user uses an app while moving, the reception unit can also update the current location in real time and automatically complete related place names. This makes it easier for the reception unit to automatically complete related place names by taking into account the user's current location information. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to execute automatic completion of related place names.

[0074] The reception unit can estimate the user's emotion and present input candidates for place names based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and presents input candidates for place names based on the estimated user's emotion. For example, when the user is excited, the reception unit presents popular tourist spots and hot spots as candidates. In addition, when the user is relaxed, the reception unit can present quiet places and places rich in nature as candidates. In addition, when the user is stressed, the reception unit can present relaxing places and healing spots as candidates. In this way, the reception unit presents input candidates for place names according to the user's emotion, making the input of place names more comfortable. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0075] The reception unit can analyze the user's social media activity when inputting a place name and suggest related place names. For example, the reception unit can analyze the user's social media activity when inputting a place name and suggest related place names. For example, the reception unit suggests places where the user frequently checks in on social media as candidates. The reception unit can also suggest places visited by influencers the user follows as candidates. The reception unit can also suggest events and places in which the user shows interest on social media as candidates. As a result, the reception unit suggests related place names based on the user's social media activity, thereby making the input of place names more efficient. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of related place names.

[0076] The reception unit can display related place names preferentially based on the user's past travel history when inputting a place name. For example, when inputting a place name, the reception unit preferentially displays related place names in consideration of the user's past travel history. For example, the reception unit preferentially displays related place names based on places the user has visited in the past. The reception unit can also suggest other tourist spots in the same area from the user's past travel history. The reception unit can also analyze the user's past travel history and suggest new place names that may be of interest. As a result, the reception unit preferentially displays related place names based on the user's past travel history, thereby making the input of place names more efficient. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data to the generation AI and cause the generation AI to execute the preferential display of related place names.

[0077] The collection unit can estimate the user's emotion and determine the priority of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data collection based on the estimated user's emotion. For example, when the user is excited, the collection unit preferentially collects data of popular tourist spots and hot spots. In addition, when the user is relaxed, the collection unit can preferentially collect data of quiet places and places rich in nature. In addition, when the user is stressed, the collection unit can also preferentially collect data of places where the user can relax and healing spots. In this way, the collection unit can collect more appropriate data by determining the priority of data collection according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI determine the priorities of data collection.

[0078] The collection unit can select an appropriate data source based on the user's past search history when collecting data. For example, the collection unit selects an optimal data source by referring to the user's past search history when collecting data. For example, the collection unit preferentially selects a data source related to a place name searched for by the user in the past. The collection unit can also select a data source related to a specific theme from the user's past search history. The collection unit can also analyze the user's past search history and select the most reliable data source. This allows the collection unit to select an optimal data source based on the user's past search history, thereby making data collection more efficient. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's past search history data to the generation AI and cause the generation AI to select an appropriate data source.

[0079] The collection unit can preferentially collect data according to the season and time of the place name when collecting data. For example, the collection unit preferentially collects data according to the season and time of the place name when collecting data. For example, the collection unit preferentially collects scenery data for each season to reproduce a realistic scenery. The collection unit can also preferentially collect scenery data for each time period to reflect changes between day and night. The collection unit can also preferentially collect data of specific events and festivals to reproduce a realistic atmosphere. As a result, the collection unit can reproduce a more realistic scenery by preferentially collecting data according to the season and time period of the place name. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause the generation AI to collect data according to the season and time period of the place name.

[0080] The collection unit can estimate the user's emotion and adjust the data collection method based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the data collection method based on the estimated user's emotion. For example, when the user is excited, the collection unit quickly collects data and reflects it in real time. Also, when the user is relaxed, the collection unit can collect detailed data and reproduce a highly accurate scene. Also, when the user is stressed, the collection unit can adopt a simple and intuitive data collection method. Thereby, the collection unit can collect more appropriate data by adjusting the data collection method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the data collection method.

[0081] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects data of a location close to the user's current location. In addition, the collection unit can preferentially collect data of a location close to the user's destination. In addition, when the user uses an app while moving, the collection unit can also collect data related to the current location in real time. As a result, the collection unit preferentially collects highly relevant data based on the geographical location information of the user, thereby making data collection more efficient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0082] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. The collection unit, for example, analyzes the social media activity of the user at the time of data collection and collects related data. For example, the collection unit collects data of places where the user frequently checks in on social media. The collection unit can also collect data of places visited by influencers followed by the user. The collection unit can also collect data of events and places in which the user shows interest on social media. As a result, the collection unit collects related data based on the user's social media activity, thereby making data collection more efficient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0083] The generation unit can estimate the user's emotion and adjust the expression method of image generation based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of image generation based on the estimated user's emotion. For example, the generation unit generates a soft color scheme and a calm landscape when the user is relaxed. In addition, the generation unit can generate a vivid color scheme and a dynamic landscape when the user is excited. In addition, the generation unit can generate a simple and calm landscape when the user is stressed. Thereby, the generation unit can generate a more appropriate image by adjusting the expression method of image generation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of image generation.

[0084] The generation unit can adjust the detail of the generation based on the importance of the collected data when generating an image. The generation unit adjusts the detail of the generation based on the importance of the collected data when generating an image, for example. For example, the generation unit generates a detailed image based on data of important landmarks and tourist spots. The generation unit can also generate a simple image based on data of a general location. The generation unit can also generate an image that reproduces a detailed atmosphere based on data of a specific event or festival. This allows the generation unit to generate a more appropriate image by adjusting the detail of the generation based on the importance of the collected data. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the detail of the generation.

[0085] The generation unit can apply different generation algorithms depending on the category of the place name when generating an image. The generation unit can apply different generation algorithms depending on the category of the place name when generating an image. For example, in the case of a tourist destination, the generation unit applies a generation algorithm dedicated to tourist destinations. In addition, in the case of a natural landscape, the generation unit can apply a generation algorithm dedicated to natural landscapes. In addition, in the case of an urban landscape, the generation unit can also apply a generation algorithm dedicated to urban landscapes. In this way, the generation unit can generate a more appropriate image by applying a generation algorithm according to the category of the place name. Part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or can be performed without using AI. For example, the generation unit can input the category of the place name to the generation AI and cause the generation AI to apply different generation algorithms.

[0086] The generation unit can estimate the user's emotion and adjust the length of the image to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the image to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short image that covers the main points. Also, when the user is relaxed, the generation unit can generate a longer image including a detailed explanation. Also, when the user is excited, the generation unit can generate an image with a visually stimulating effect. In this way, the generation unit can adjust the length of the image according to the user's emotion to generate a more appropriate image. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the image.

[0087] The generation unit can determine the priority of generation based on the time of submission of the place name when generating an image. The generation unit, for example, determines the priority of generation based on the time of submission of the place name when generating an image. For example, when the user is in a hurry, the generation unit preferentially generates an image that can be generated in a short time. In addition, when the user is relaxed, the generation unit can preferentially generate a detailed image. In addition, when the user submits during a specific event or festival, the generation unit can also preferentially generate an image related to that time. In this way, the generation unit can generate a more appropriate image by determining the priority of generation based on the time of submission of the place name. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the time of submission of the place name to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0088] The generation unit can adjust the order of generation based on the relevance of place names when generating images. For example, the generation unit adjusts the order of generation based on the relevance of place names when generating images. For example, the generation unit preferentially generates images of important landmarks and tourist spots. The generation unit can also postpone images of general places. The generation unit can also preferentially generate images related to specific events and festivals. In this way, the generation unit can generate more appropriate images by adjusting the order of generation based on the relevance of place names. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of place names to the generation AI and cause the generation AI to adjust the order of generation.

[0089] The providing unit can estimate the user's emotion and adjust the image display method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the image display method based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides a soft color scheme and a gentle display method. Also, when the user is excited, the providing unit can provide a vivid color scheme and a dynamic display method. Also, when the user is stressed, the providing unit can provide a simple and calm display method. Thereby, the providing unit adjusts the image display method according to the user's emotion, thereby enabling more appropriate display. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the image display method.

[0090] The providing unit can select an appropriate display method based on the user's past operation history when displaying an image. For example, the providing unit selects an optimal display method by referring to the user's past operation history when displaying an image. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also select a display method related to a specific theme from the user's past operation history. The providing unit can also analyze the user's past operation history and select a display method with the highest visibility. As a result, the providing unit can select an optimal display method based on the user's past operation history, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generating AI and cause the generating AI to select an appropriate display method.

[0091] The providing unit can select an appropriate display method based on the device information of the user when displaying an image. For example, the providing unit selects an optimal display method in consideration of the device information of the user when displaying an image. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can provide a display method optimized for a large screen. In addition, when the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. As a result, the providing unit can select an optimal display method based on the device information of the user, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the generating AI and cause the generating AI to select an appropriate display method.

[0092] The providing unit can estimate the user's emotion and adjust the image operation procedure based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the image operation procedure based on the estimated user's emotion. For example, the providing unit provides a simple and intuitive operation procedure when the user is nervous. In addition, the providing unit can provide a detailed operation procedure when the user is relaxed. In addition, the providing unit can also provide a procedure that allows the user to operate quickly when the user is in a hurry. In this way, the providing unit provides an operation procedure according to the user's emotion, thereby enabling more appropriate operation. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the operation procedure.

[0093] The providing unit can select an appropriate display method based on the geographical location information of the user when displaying an image. For example, the providing unit selects an optimal display method in consideration of the geographical location information of the user when displaying an image. For example, the providing unit preferentially displays images of locations close to the user's current location. In addition, the providing unit can preferentially display images of locations close to the user's destination. In addition, when the user uses an app while moving, the providing unit can also display images related to the current location in real time. As a result, the providing unit can select an optimal display method based on the geographical location information of the user, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to select an appropriate display method.

[0094] The providing unit can analyze the social media activity of the user and display related images when the image is displayed. The providing unit can, for example, analyze the social media activity of the user and display related images when the image is displayed. For example, the providing unit displays images of places where the user frequently checks in on social media. The providing unit can also display images of places visited by influencers followed by the user. The providing unit can also display images of events and places in which the user is interested on social media. This allows the providing unit to display related images based on the user's social media activity, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to execute display of related images.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The reception unit can support voice input when the user inputs a place name. For example, when the user inputs "Tokyo Tower" by voice, the reception unit converts the voice into text and recognizes it as a place name. In addition, the reception unit can learn the pronunciation characteristics of the user and use an individually optimized voice recognition model to improve the accuracy of the voice input. Furthermore, the reception unit can estimate the user's emotions during voice input and provide feedback according to the emotions. For example, if the user is excited, the reception unit can respond with a cheerful voice, and if the user is relaxed, the reception unit can respond with a calm voice.

[0097] The collection unit can collect historical data related to the place name input by the user. For example, the collection unit collects photos of Tokyo Tower when it was first built and records of past events. The collection unit can also collect cultural data, such as literary works and movie scenes related to the place name. Furthermore, the collection unit can preferentially collect data related to a particular era or theme according to the user's interests. This allows the collection unit to provide the user with richer information.

[0098] The generation unit can reproduce the scenery of the time period specified by the user based on the collected data. For example, the generation unit generates the scenery of Tokyo Tower in the morning or the Eiffel Tower at night. The generation unit can also generate scenery according to a specific season or weather. For example, the generation unit can reproduce the scenery of Tokyo Tower in cherry blossom season or the Eiffel Tower with snow falling. Furthermore, the generation unit can estimate the user's emotions and adjust the color tone and atmosphere of the scenery according to the emotions. For example, if the user is relaxed, the generation unit can generate a scenery with soft colors, and if the user is excited, the generation unit can generate a scenery with vivid colors.

[0099] The providing unit can display the generated scenery image using augmented reality (AR) technology. For example, when a user views the scenery image through a smartphone, the scenery image can be displayed superimposed on an actual scenery. The providing unit can also enable the user to rotate the scenery image 360 ​​degrees and view it. Furthermore, the providing unit can provide a function that enables the user to add comments and tags to the scenery image. This allows the providing unit to allow the user to enjoy the generated scenery image in a more interactive manner.

[0100] The providing unit can automatically generate a caption that reflects the user's emotions when sharing the generated scenery image on social media. For example, if the user is excited, the providing unit can generate a caption such as "This place is really amazing!". Also, if the user is relaxed, the providing unit can generate a caption such as "I want to relax here." Furthermore, the providing unit can automatically apply filters and effects to the shared image according to the user's emotions. This allows the providing unit to share the generated scenery image more attractively.

[0101] The reception unit can provide related tourist information in real time when the user inputs a place name. For example, when the user inputs "Tokyo Tower," information on nearby tourist spots and restaurants is displayed. The reception unit can also provide traffic information and travel time from the user's current location to the destination. Furthermore, the reception unit can suggest related place names and tourist information based on the user's past search history. This allows the reception unit to provide more convenient information when the user inputs a place name.

[0102] The collection unit can estimate the user's emotions and adjust the scope of data collection based on the estimated emotions. For example, when the user is excited, a variety of information is collected from a wide range of data sources. When the user is relaxed, data focused on a specific theme can be collected. Furthermore, when the user is stressed, the collection unit can also prioritize collection of simple and intuitive information. This allows the collection unit to provide more appropriate information by collecting data according to the user's emotions.

[0103] The generation unit can predict and generate future scenery related to a place name specified by the user. For example, the generation unit generates the future scenery around Tokyo Tower, taking into account urban development plans and the effects of climate change. The generation unit can also reproduce the scenery of future events and festivals that the user is interested in. Furthermore, the generation unit can estimate the user's emotions and generate a future scenery scenario according to the emotions. For example, if the user is excited, a lively future scenery of a downtown area can be generated, and if the user is relaxed, a quiet future scenery of a park can be generated.

[0104] The providing unit can display the generated scenery image in an optimized manner for the user's device. For example, the resolution and layout of the image can be adjusted according to different devices, such as a smartphone, tablet, or PC. The providing unit can also select an optimal display method based on the screen size and resolution of the user's device. Furthermore, the providing unit can automatically adjust the display of the image when the user rotates the device to landscape or portrait orientation. This allows the providing unit to comfortably view scenery images regardless of which device the user uses.

[0105] The providing unit can customize the generated landscape image based on the user's emotions. For example, when the user is relaxed, a display combining soft colors and calm music can be provided. When the user is excited, a display combining vivid colors and lively music can be provided. Furthermore, when the user is stressed, the providing unit can provide a simple and calming display method to enhance the relaxation effect. In this way, the providing unit can provide a more comfortable experience by providing a customized display according to the user's emotions.

[0106] The process flow of the second embodiment will be briefly described below.

[0107] Step 1: The reception unit receives the user's input of a place name. For example, the user can input a specific place name such as "Tokyo Tower" or "Eiffel Tower." Step 2: The collecting unit collects data based on the place name accepted by the accepting unit. The collecting unit can acquire data, for example, from a public database on the Internet or a geographic information system (GIS). For example, the collecting unit collects image data of Tokyo Tower and surrounding scenery data. Step 3: The generation unit analyzes the data collected by the collection unit and generates an image that reproduces the scenery of the actual place. For example, the generation unit analyzes the collected data and generates an image that reflects the characteristics and atmosphere of the actual place. For example, the generation unit generates a realistic scenery image by taking into account the buildings and roads around Tokyo Tower, the scenery by season, etc. Step 4: The providing unit provides the image generated by the generating unit to the user. The providing unit can, for example, interactively display the generated image. The providing unit can also include a storage unit and a sharing unit for the user to store and share the generated image.

[0108] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0110] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the user's input of a place name. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires data from a public database on the Internet or a geographic information system (GIS). The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and generates an image that reproduces the scenery of the actual place. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0115] 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.

[0116] 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 the voice according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0118] 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 performed in a secure state.

[0119] Fig. 4 shows an example of 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.

[0120] 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.

[0121] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0125] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0126] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the user's input of a place name. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires data from a public database on the Internet or a geographic information system (GIS). The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and generates an image that reproduces the scenery of the actual place. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] 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.

[0136] 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.

[0137] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 voice indicating a user input for 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives the user's input of a place name. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires data from a public database on the Internet or a geographic information system (GIS). The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and generates an image that reproduces the scenery of the actual place. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0150] 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 performed in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0152] 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.

[0153] 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.

[0154] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0156] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] 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 a voice indicating a user input for 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.

[0158] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0160] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the user's input of a place name. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires data from a public database on the Internet or a geographic information system (GIS). The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and generates an image that reproduces the scenery of the actual place. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0162] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0164] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0166] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0168] 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.

[0169] 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 Universal Serial Bus (USB) 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.

[0170] In addition, 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 upon request from the data processing device 12.

[0171] 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.

[0172] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0173] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0174] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0175] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0177] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0179] (Appendix 1) A reception unit that receives an input of a place name; a collection unit that collects data based on the place names accepted by the acceptance unit; A generating unit that analyzes the data collected by the collecting unit and generates an image that reproduces a scene of an actual place; a providing unit that provides the image generated by the generating unit to a user; A system characterized by: (Appendix 2) The collecting unit includes: Obtain data from public databases or geographic information systems on the Internet 2. The system of claim 1, (Appendix 3) The generation unit is Analyze the collected data and generate images that reflect the characteristics or atmosphere of the actual place 2. The system of claim 1, (Appendix 4) The providing unit is View the generated images interactively 2. The system of claim 1, (Appendix 5) The providing unit is Equipped with a storage or sharing section for users to store and share generated images 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotion and customize a place name input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past place name input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 8) The reception unit is When typing a place name, auto-complete related place names based on the user's current location 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and present place name input candidates based on the estimated user's emotions 2. The system of claim 1, (Appendix 10) The reception unit is When you type a place name, it analyzes your social media activity to suggest related place names. 2. The system of claim 1, (Appendix 11) The reception unit is When entering a place name, the app prioritizes relevant place names based on the user's past travel history. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Inferring user sentiment and prioritizing data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting data, select the appropriate data source based on the user's past search history 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting data, prioritize collection of place-name data according to season and time of day. 2. The system of claim 1, (Appendix 15) The collecting unit includes: Inferring user emotions and adjusting data collection methods based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The collecting unit includes: When collecting data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 17) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the user's emotions and adjust the image generation expression method based on the estimated user's emotions. 2. The system of claim 1, (Appendix 19) The generation unit is During image generation, the level of detail generated is adjusted based on the importance of the collected data 2. The system of claim 1, (Appendix 20) The generation unit is When generating images, different generation algorithms are applied depending on the place name category. 2. The system of claim 1, (Appendix 21) The generation unit is Estimate the user's emotion and adjust the length of the image to be generated based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The generation unit is When generating images, the priority of generation is determined based on the time of submission of place names. 2. The system of claim 1, (Appendix 23) The generation unit is When generating images, adjust the order of generation based on the relevance of place names. 2. The system of claim 1, (Appendix 24) The providing unit is Inferring a user's emotion and adjusting the way an image is displayed based on the estimated user's emotion 2. The system of claim 1, (Appendix 25) The providing unit is When displaying images, the appropriate display method is selected based on the user's past operation history. 2. The system of claim 1, (Appendix 26) The providing unit is When displaying images, select the appropriate display method based on the user's device information. 2. The system of claim 1, (Appendix 27) The providing unit is Estimating a user's emotion and adjusting an image operation procedure based on the estimated user's emotion 2. The system of claim 1, (Appendix 28) The providing unit is When displaying images, choose the appropriate display method based on the user's geographic location. 2. The system of claim 1, (Appendix 29) The providing unit is When viewing an image, it analyzes the user's social media activity and displays related images. 2. The system of claim 1, [Explanation of symbols]

[0180] 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 reception unit that receives an input of a place name; a collection unit that collects data based on the place names accepted by the acceptance unit; A generating unit that analyzes the data collected by the collecting unit and generates an image that reproduces a scene of an actual place; a providing unit that provides the image generated by the generating unit to a user; A system characterized by:

2. The collecting unit includes: Obtain data from public databases or geographic information systems on the Internet 2. The system of claim 1 .

3. The generation unit is Analyzing the data collected by the collection unit to generate an image that reflects the characteristics or atmosphere of the actual location.

2. The system of claim 1 .

4. The providing unit is The image generated by the generating unit is interactively displayed.

2. The system of claim 1 .

5. The reception unit is To estimate a user's emotion and customize a place name input interface based on the estimated user's emotion.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past place name input history and suggest appropriate input methods 2. The system of claim 1 .

7. The reception unit is When typing a place name, auto-complete related place names based on the user's current location 2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and present place name input candidates based on the estimated user's emotions 2. The system of claim 1 .

Images