system

Abstract

The system according to the embodiment aims to visually reproduce the past scenery and buildings of a specific location. [Solution] A system according to an embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives a specification of a specific location and time from a user. The collection unit collects data on historical photographs, documents, and maps based on the specification received by the reception unit. The generation unit analyzes the data collected by the collection unit and generates a past landscape. The provision unit provides the past landscape generated by the generation unit to a user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to visually recreate the past scenery and buildings of a specific location.

[0005] The system according to the embodiment aims to visually reproduce the past scenery and buildings of a specific location. [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 a specification of a specific location and time from a user. The collection unit collects data on historical photographs, documents, and maps based on the specification received by the reception unit. The generation unit analyzes the data collected by the collection unit and generates a past landscape. The provision unit provides the past landscape generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can visually reproduce the past scenery and buildings of a specific location. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A past scenery reproduction system according to an embodiment of the present invention is a system that uses AI to reproduce past scenery and buildings. This past scenery reproduction system is a system in which a user specifies a specific location and time period, and AI reproduces past scenery and buildings based on the specified location and time period. Specifically, the AI ​​analyzes data such as historical photographs, documents, and maps to generate past scenery. The generated past scenery can be viewed by the user from a 360-degree perspective, similar to Street View. This allows the past scenery reproduction system to reproduce and provide past scenery based on the location and time period specified by the user.

[0029] A past scenery reproduction system according to an embodiment includes a receiving unit, a collecting unit, a generating unit, and a providing unit. The receiving unit receives a user's specification of a specific location and time period. The user's specified location and time period may include, but are not limited to, geographical coordinates and historical periods. The collecting unit collects historical photographs, documents, and map data based on the specification received by the receiving unit. The collected data may include, but are not limited to, the resolution of the photographs, the type of document, and the scale of the map. The collecting unit may collect related data from, for example, the Internet or a database. For example, data may be collected from a specific website or an archive database. The generating unit analyzes the data collected by the collecting unit to generate a past scenery. For example, image analysis algorithms and text mining techniques may be used for the analysis, but are not limited to, examples. The generating unit may reproduce not only the exterior of a building but also the internal structure and decoration. For example, room layouts, furniture arrangements, and decorative designs may be reproduced. The providing unit provides the past scenery generated by the generating unit to a user. The providing unit allows the user to view past scenery from a 360-degree perspective, such as street view. For example, the providing unit can provide the user with a realistic experience by using a 360-degree panoramic view or virtual reality technology. As a result, the past scenery reproduction system according to the embodiment can reproduce and provide past scenery based on a location and time specified by the user.

[0030] The collection unit can collect relevant data from the Internet or a database. For example, the collection unit can collect data from a specific website or archive database. For example, the collection unit can collect high-resolution photographic data from a website that provides historical photographs. The collection unit can also collect documentary data from a database that provides historical documents. For example, the collection unit can collect newspaper articles and academic papers related to a specific period. The collection unit can also collect map data from a website that provides map data. For example, the collection unit can collect old maps of a specific area and use them as data for recreating past landscapes. This allows for the reconstruction of past landscapes based on more information by collecting data from the Internet or a database. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can efficiently collect data using an AI model that automatically collects data on the Internet.

[0031] The generation unit can reproduce not only the exterior of a building but also its internal structure and decoration. The generation unit reproduces, for example, room layout, furniture arrangement, decorative design, etc. For example, the generation unit can reproduce the internal structure of a specific building based on historical photographs and documents. The generation unit can also reproduce not only the exterior of a building but also its internal decoration. For example, the generation unit can reproduce the interior design of a building from a specific period and provide it to the user. Furthermore, the generation unit can reproduce the internal structure and decoration of a building as a 3D model. For example, the generation unit can use 3D modeling technology to reproduce the interior of a building in detail and allow the user to view it from a 360-degree perspective. This can provide a more detailed view of past scenery by reproducing the internal structure and decoration of the building. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can analyze historical photographs and documents and efficiently generate scenery using an AI model that reproduces the internal structure of a building.

[0032] The providing unit may enable a user to view past scenery from a 360-degree perspective, similar to Street View. The providing unit may enable a user to have an immersive experience, for example, by using a 360-degree panoramic view or virtual reality technology. For example, the providing unit may display the generated past scenery as a 360-degree panoramic view, allowing the user to freely change the viewpoint while viewing. The providing unit may also enable a user to experience the past scenery by wearing a VR headset using virtual reality technology. For example, the providing unit may provide the user with an experience of walking around in the past scenery through the VR headset. Furthermore, the providing unit may provide the generated past scenery through a web browser or a mobile application. For example, the providing unit may enable a user to view the past scenery using a smartphone or tablet. This allows the user to view the past scenery from a 360-degree perspective, providing a more immersive experience. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may efficiently provide the scenery using an AI model that displays the generated scenery data as a 360-degree panoramic view.

[0033] The reception unit can analyze the user's past designation history and suggest an appropriate designation method. The reception unit can, for example, suggest new related locations and times based on locations and times previously designated by the user. For example, the reception unit can analyze the user's past designation history and suggest new locations and times related to previously designated locations and times. The reception unit can also automatically display locations and times frequently designated by the user in the past as candidates. For example, the reception unit can automatically display frequently designated locations and times as candidates based on the user's past designation history. The reception unit can also predict and suggest locations and times related to specific seasons or events based on the user's past designation history. For example, the reception unit can analyze the user's past designation history and predict and suggest locations and times related to specific seasons or events. By analyzing the past designation history, the reception unit can suggest optimal locations and times for the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can efficiently suggest designation methods using an AI model that analyzes the user's past designation history.

[0034] When a specific location and time period are specified, the reception unit can perform filtering based on the user's current interests. The reception unit, for example, can suggest locations and times related to a historical theme in which the user is currently interested. For example, the reception unit can suggest locations and times related to a historical theme based on the user's current interests. The reception unit can also suggest locations and times related to an architectural style in which the user is currently interested. For example, the reception unit can suggest locations and times related to an architectural style based on the user's current interests. The reception unit can also suggest locations and times related to a culture or event in which the user is currently interested. For example, the reception unit can suggest locations and times related to a culture or event based on the user's current interests. This allows filtering based on the user's current interests to suggest more relevant locations and times. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can efficiently perform filtering using an AI model that analyzes the user's current interests.

[0035] When a specific location and time period are specified, the reception unit can prioritize suggesting highly relevant locations and times based on the user's geographical location information. The reception unit, for example, prioritizes suggesting past scenery close to the user's current location. For example, the reception unit can prioritize suggesting past scenery close to the user's current location based on the user's geographical location information. The reception unit can also suggest locations and times when historical events related to the user's current location occurred. For example, the reception unit can suggest locations and times when historical events related to the current location occurred based on the user's geographical location information. The reception unit can also prioritize suggesting locations and times that are easily accessible from the user's current location. For example, the reception unit can prioritize suggesting locations and times that are easily accessible from the user's current location based on the user's geographical location information. This allows the reception unit to suggest more relevant locations and times by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can efficiently suggest highly relevant locations and times using an AI model that analyzes the user's geographical location information.

[0036] When a specific location and time period are specified, the reception unit can analyze the user's social media activity and suggest highly relevant locations and times. The reception unit can, for example, suggest locations and times frequently mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and suggest locations and times frequently mentioned. The reception unit can also predict and suggest locations and times of interest based on the content of the user's social media posts. For example, the reception unit can analyze the content of the user's social media posts and predict and suggest locations and times of interest. Furthermore, the reception unit can make suggestions based on the locations and times visited by the user's social media friends. For example, the reception unit can make suggestions based on the locations and times visited by the user's social media friends. In this way, by analyzing social media activity, it is possible to suggest locations and times based on the user's interests. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can efficiently suggest highly relevant locations and times using an AI model that analyzes the user's social media activity.

[0037] The collection unit can analyze past data collection history and select an appropriate collection method. The collection unit, for example, collects new related data based on data previously collected by the user. For example, the collection unit can analyze the user's past data collection history and collect new data related to the previously collected data. The collection unit can also automatically display data that the user frequently collected in the past as candidates. For example, the collection unit can automatically display frequently collected data as candidates based on the user's past data collection history. Furthermore, the collection unit can predict and collect data related to a specific theme or event from the user's past data collection history. For example, the collection unit can analyze the user's past data collection history and predict and collect data related to a specific theme or event. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can efficiently select a collection method using an AI model that analyzes the user's past data collection history.

[0038] The collection unit may perform filtering based on the user's current interests when collecting data. For example, the collection unit may prioritize collecting data related to historical themes in which the user is currently interested. For example, the collection unit may prioritize collecting data related to historical themes based on the user's current interests. The collection unit may also prioritize collecting data related to architectural styles in which the user is currently interested. For example, the collection unit may prioritize collecting data related to architectural styles based on the user's current interests. The collection unit may also prioritize collecting data related to cultures or events in which the user is currently interested. For example, the collection unit may prioritize collecting data related to cultures or events based on the user's current interests. This allows for filtering based on the user's current interests to collect more relevant data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may perform efficient filtering using an AI model that analyzes the user's current interests.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting data related to past scenery close to the user's current location. For example, the collection unit can prioritize collecting data related to past scenery close to the current location based on the user's geographical location information. The collection unit can also prioritize collecting data related to historical events related to the user's current location. For example, the collection unit can prioritize collecting data related to historical events related to the current location based on the user's geographical location information. Furthermore, the collection unit can also prioritize collecting data related to locations that are easily accessible from the user's current location. For example, the collection unit can prioritize collecting data related to locations that are easily accessible from the current location based on the user's geographical location information. This allows for more relevant data to be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect highly relevant data using an AI model that analyzes the user's geographical location information.

[0040] During data collection, the collection unit can analyze the user's social media activity and collect highly relevant data. The collection unit, for example, collects data related to places and times frequently mentioned by the user on social media. For example, the collection unit can analyze the user's social media activity and collect data related to places and times frequently mentioned. The collection unit can also collect data related to places and times of interest from the user's social media posts. For example, the collection unit can analyze the user's social media posts and collect data related to places and times of interest. Furthermore, the collection unit can collect data related to places and times visited by the user's social media friends. For example, the collection unit can collect data related to places and times visited by the user's social media friends. In this way, data based on the user's interests can be collected by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect highly relevant data using an AI model that analyzes the user's social media activity.

[0041] The generation unit can adjust the level of detail of the generated scenery based on the importance of the data when generating the scenery. For example, the generation unit reproduces scenery related to important historical events in detail. For example, the generation unit can reproduce scenery related to important historical events in detail based on the importance of the data. The generation unit can also reproduce general scenery in a simplified manner. For example, the generation unit can reproduce general scenery in a simplified manner based on the importance of the data. Furthermore, the generation unit can reproduce scenery in which the user is particularly interested in in detail. For example, the generation unit can reproduce scenery in which the user is particularly interested in in detail based on the importance of the data. In this way, by adjusting the level of detail of the generated scenery based on the importance of the data, more important scenery can be reproduced in detail. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently adjust the level of detail of the generated scenery using an AI model that evaluates the importance of the data.

[0042] The generation unit can apply different generation algorithms depending on the classification of data when generating a landscape. For example, when recreating the exterior of a building, the generation unit uses an algorithm based on architectural data. For example, the generation unit can use an algorithm based on architectural data to recreate the exterior of a building. The generation unit can also use an algorithm based on geographic data to recreate a natural landscape. For example, the generation unit can use an algorithm based on geographic data to recreate a natural landscape. Furthermore, the generation unit can also use an algorithm based on literature data when recreating a historical event. For example, the generation unit can use an algorithm based on literature data to recreate a historical event. In this way, by applying different generation algorithms depending on the classification of data, more appropriate landscapes can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently generate landscapes using an AI model that applies different generation algorithms depending on the classification of data.

[0043] When generating a landscape, the generation unit can determine a generation priority based on the date and time of data collection. The generation unit, for example, prioritizes generating a landscape based on the latest data. For example, the generation unit can prioritize generating a landscape based on the latest data based on the date and time of data collection. The generation unit can also prioritize generating a landscape based on data from a historically important period. For example, the generation unit can prioritize generating a landscape based on data from a historically important period based on the date and time of data collection. The generation unit can also prioritize generating a landscape based on data from a period specified by a user. For example, the generation unit can prioritize generating a landscape based on data from a period specified by a user based on the date and time of data collection. In this way, by determining the generation priority based on the date and time of data collection, more important landscapes can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently determine the generation priority using an AI model that analyzes the date and time of data collection.

[0044] When generating scenery, the generation unit can adjust the order of generation based on the relevance of data. For example, the generation unit can first generate scenery based on data related to important historical events. For example, the generation unit can first generate scenery based on data related to important historical events based on the relevance of data. The generation unit can also first generate scenery based on data in which the user is particularly interested based on the relevance of data. For example, the generation unit can first generate scenery based on data in which the user is particularly interested based on the relevance of data. Furthermore, the generation unit can postpone generation of scenery based on general data. For example, the generation unit can postpone generation of scenery based on general data based on the relevance of data. In this way, by adjusting the order of generation based on the relevance of data, it is possible to preferentially generate scenery with higher relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently adjust the order of generation using an AI model that evaluates the relevance of data.

[0045] When providing scenery, the providing unit can select an appropriate providing method by referring to the user's past browsing history. The providing unit, for example, can provide new related scenery based on scenery that the user frequently viewed in the past. For example, the providing unit can refer to the user's past browsing history and provide new related scenery based on the frequently viewed scenery. The providing unit can also analyze trends in scenery that the user has viewed in the past and select an optimal providing method. For example, the providing unit can analyze trends in scenery viewed based on the user's past browsing history and select an optimal providing method. Furthermore, the providing unit can predict and provide scenery related to a specific theme or event based on the user's past browsing history. For example, the providing unit can predict and provide scenery related to a specific theme or event based on the user's past browsing history. In this way, the optimal scenery can be provided to the user by referring to the past browsing history. Some or all of the above-described 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 efficiently select a providing method by using an AI model that analyzes the user's past browsing history.

[0046] When providing scenery, the providing unit can perform filtering based on the user's current interests. For example, the providing unit can prioritize providing scenery related to a historical theme in which the user is currently interested. For example, the providing unit can prioritize providing scenery related to a historical theme based on the user's current interests. The providing unit can also prioritize providing scenery related to an architectural style in which the user is currently interested. For example, the providing unit can prioritize providing scenery related to an architectural style based on the user's current interests. Furthermore, the providing unit can also prioritize providing scenery related to a culture or event in which the user is currently interested. For example, the providing unit can prioritize providing scenery related to a culture or event based on the user's current interests. This allows for filtering based on the user's current interests to provide more relevant scenery. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can efficiently perform filtering using an AI model that analyzes the user's current interests.

[0047] When providing scenery, the providing unit can prioritize providing highly relevant scenery by taking into account the user's geographical location information. The providing unit, for example, prioritizes providing past scenery close to the user's current location. For example, the providing unit can prioritize providing past scenery close to the current location based on the user's geographical location information. The providing unit can also provide scenery where historical events related to the user's current location occurred. For example, the providing unit can provide scenery where historical events related to the current location occurred based on the user's geographical location information. The providing unit can also prioritize providing scenery of places that are easily accessible from the user's current location. For example, the providing unit can prioritize providing scenery of places that are easily accessible from the current location based on the user's geographical location information. This makes it possible to provide more relevant scenery by taking the user's geographical location information into account. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide highly relevant scenery by using an AI model that analyzes the user's geographical location information.

[0048] When providing scenery, the providing unit can analyze the user's social media activity and provide highly relevant scenery. The providing unit can, for example, provide scenery related to places and times frequently mentioned by the user on social media. For example, the providing unit can analyze the user's social media activity and provide scenery related to places and times frequently mentioned by the user. The providing unit can also provide scenery related to places and times of interest based on the content of the user's social media posts. For example, the providing unit can analyze the content of the user's social media posts and provide scenery related to places and times of interest. Furthermore, the providing unit can provide scenery related to places and times visited by the user's social media friends. For example, the providing unit can provide scenery related to places and times visited by the user's social media friends. In this way, by analyzing social media activity, scenery based on the user's interests and concerns can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can efficiently provide highly relevant scenery using an AI model that analyzes the user's social media activity.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The past scenery reproduction system may further include an audio guide unit. The audio guide unit may provide audio historical information and anecdotes related to the generated past scenery. For example, when a user is viewing a particular building, the user may hear an audio commentary about the building's history and architectural style. The audio guide unit may also provide a customized guide based on the user's interests. For example, if the user is interested in a particular era or culture, the audio guide unit may provide information related to that era or culture preferentially. The audio guide unit may also adjust the content of the guide to match the user's walking speed. For example, if the user is walking slowly, the audio guide unit may provide a detailed commentary, and if the user is walking quickly, the audio guide unit may provide a concise commentary that focuses on the main points. This allows the user to enjoy the past scenery not only visually but also aurally, providing a more immersive experience.

[0051] The past scenery reproduction system may further include an interactive map unit. The interactive map unit displays past scenery generated based on a location and time period specified by the user on a map and allows the user to move freely on the map. For example, when a user is viewing past scenery of a particular city, the user can move to another location on the map of that city. The interactive map unit may also display information related to a location or time period in which the user is interested on the map. For example, if the user is interested in a particular building, historical information or episodes related to that building may be displayed on the map. Furthermore, the interactive map unit may generate new scenery based on a location and time period specified by the user on the map. This allows the user to explore past scenery through the map and learn more about history.

[0052] The past scene reproduction system may further include a user feedback unit. The user feedback unit may collect feedback from users about scenes viewed by the user and use the collected feedback to improve the system. For example, the user may input their thoughts about a particular scene and suggestions for improvement. The user feedback unit may also adjust the scene generation algorithm based on the collected feedback. For example, if the user feels that the reproduction of a particular scene is poor, the unit may make adjustments to improve the reproduction based on the user feedback. Furthermore, the user feedback unit may add new functions or content based on the user feedback. This allows the system to be improved based on user opinions, providing a more satisfying experience.

[0053] The past scenery reproduction system may further include an education mode unit. The education mode unit may provide a function for using the generated past scenery for educational purposes. For example, a scenery related to a specific era or event may be generated for use in a history class and provided to students as a visual teaching material. The education mode unit may also provide quizzes and assignments related to the scenery. For example, students may deepen their historical knowledge by answering quizzes while viewing the scenery. Furthermore, the education mode unit may allow teachers to create customized scenery and content and provide them to students. This may promote use in educational settings and increase students' motivation to learn.

[0054] The past scenery reproduction system may further include a social sharing unit. The social sharing unit provides a function that allows a user to share the generated past scenery on social media. For example, while a user is viewing a particular scenery, the user can share the scenery on a social networking site. The social sharing unit may also collect comments and reactions on the scenery shared by the user and use the collected comments to improve the system. For example, the social sharing unit may collect comments from other users on the scenery shared by the user and adjust the scenery generation algorithm based on the feedback. Furthermore, the social sharing unit may also allow a user to share scenery with other users and view it collaboratively. This allows the user to enjoy past scenery together with others, providing a richer experience.

[0055] The past scenery reproduction system can further include a personalized recommendation unit. The personalized recommendation unit can automatically suggest related scenery based on the user's past browsing history and interests. For example, if the user is interested in a particular era or region, it can prioritize suggesting scenery related to that era or region. The personalized recommendation unit can also analyze the user's browsing history and suggest scenery that the user has not seen yet but may be interested in. Furthermore, the personalized recommendation unit can improve the content of the suggestions based on user feedback. This allows users to efficiently find scenery that matches their interests and concerns, resulting in a more satisfying experience.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives a designation of a specific location and time from a user. The location and time designated by the user may include, but are not limited to, geographic coordinates or a historical period. Step 2: The collection unit collects data on historical photographs, documents, and maps based on the specifications received by the reception unit. The collected data includes, but is not limited to, the resolution of the photographs, the type of documents, and the scale of the maps. The collection unit collects relevant data from, for example, the Internet or databases. For example, data can be collected from specific websites or archive databases. Step 3: The generation unit analyzes the data collected by the collection unit and generates a past landscape. For example, an image analysis algorithm or text mining technology may be used for the analysis, but is not limited to these examples. The generation unit can, for example, reproduce not only the exterior of a building, but also its internal structure and decoration. For example, it can reproduce the room layout, furniture arrangement, decorative design, etc. Step 4: The providing unit provides the past scenery generated by the generating unit to the user. The providing unit allows the user to view the past scenery from a 360-degree perspective, such as street view. For example, the providing unit can use a 360-degree panoramic view or virtual reality technology to allow the user to have an immersive experience.

[0058] (Example 2) A past scenery reproduction system according to an embodiment of the present invention is a system that uses AI to reproduce past scenery and buildings. This past scenery reproduction system is a system in which a user specifies a specific location and time period, and AI reproduces past scenery and buildings based on the specified location and time period. Specifically, the AI ​​analyzes data such as historical photographs, documents, and maps to generate past scenery. The generated past scenery can be viewed by the user from a 360-degree perspective, similar to Street View. This allows the past scenery reproduction system to reproduce and provide past scenery based on the location and time period specified by the user.

[0059] A past scenery reproduction system according to an embodiment includes a receiving unit, a collecting unit, a generating unit, and a providing unit. The receiving unit receives a user's specification of a specific location and time period. The user's specified location and time period may include, but are not limited to, geographical coordinates and historical periods. The collecting unit collects historical photographs, documents, and map data based on the specification received by the receiving unit. The collected data may include, but are not limited to, the resolution of the photographs, the type of document, and the scale of the map. The collecting unit may collect related data from, for example, the Internet or a database. For example, data may be collected from a specific website or an archive database. The generating unit analyzes the data collected by the collecting unit to generate a past scenery. For example, image analysis algorithms and text mining techniques may be used for the analysis, but are not limited to, examples. The generating unit may reproduce not only the exterior of a building but also the internal structure and decoration. For example, room layouts, furniture arrangements, and decorative designs may be reproduced. The providing unit provides the past scenery generated by the generating unit to a user. The providing unit allows the user to view past scenery from a 360-degree perspective, such as street view. For example, the providing unit can provide the user with a realistic experience by using a 360-degree panoramic view or virtual reality technology. As a result, the past scenery reproduction system according to the embodiment can reproduce and provide past scenery based on a location and time specified by the user.

[0060] The collection unit can collect relevant data from the Internet or a database. For example, the collection unit can collect data from a specific website or archive database. For example, the collection unit can collect high-resolution photographic data from a website that provides historical photographs. The collection unit can also collect documentary data from a database that provides historical documents. For example, the collection unit can collect newspaper articles and academic papers related to a specific period. The collection unit can also collect map data from a website that provides map data. For example, the collection unit can collect old maps of a specific area and use them as data for recreating past landscapes. This allows for the reconstruction of past landscapes based on more information by collecting data from the Internet or a database. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can efficiently collect data using an AI model that automatically collects data on the Internet.

[0061] The generation unit can reproduce not only the exterior of a building but also its internal structure and decoration. The generation unit reproduces, for example, room layout, furniture arrangement, decorative design, etc. For example, the generation unit can reproduce the internal structure of a specific building based on historical photographs and documents. The generation unit can also reproduce not only the exterior of a building but also its internal decoration. For example, the generation unit can reproduce the interior design of a building from a specific period and provide it to the user. Furthermore, the generation unit can reproduce the internal structure and decoration of a building as a 3D model. For example, the generation unit can use 3D modeling technology to reproduce the interior of a building in detail and allow the user to view it from a 360-degree perspective. This can provide a more detailed view of past scenery by reproducing the internal structure and decoration of the building. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can analyze historical photographs and documents and efficiently generate scenery using an AI model that reproduces the internal structure of a building.

[0062] The providing unit may enable a user to view past scenery from a 360-degree perspective, similar to Street View. The providing unit may enable a user to have an immersive experience, for example, by using a 360-degree panoramic view or virtual reality technology. For example, the providing unit may display the generated past scenery as a 360-degree panoramic view, allowing the user to freely change the viewpoint while viewing. The providing unit may also enable a user to experience the past scenery by wearing a VR headset using virtual reality technology. For example, the providing unit may provide the user with an experience of walking around in the past scenery through the VR headset. Furthermore, the providing unit may provide the generated past scenery through a web browser or a mobile application. For example, the providing unit may enable a user to view the past scenery using a smartphone or tablet. This allows the user to view the past scenery from a 360-degree perspective, providing a more immersive experience. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may efficiently provide the scenery using an AI model that displays the generated scenery data as a 360-degree panoramic view.

[0063] The reception unit can estimate the user's emotions and adjust the method for specifying a specific location and time based on the estimated user's emotions. For example, if the user is feeling nostalgic, the reception unit can prioritize suggesting locations and times associated with past memories. For example, if the user is feeling nostalgic, the reception unit can estimate the user's emotions and suggest locations and times associated with past memories. Furthermore, if the user is excited, the reception unit can suggest locations and times associated with historical events or important events. For example, if the user is excited, the reception unit can estimate the user's emotions and suggest locations and times associated with historical events or important events. Furthermore, if the user is relaxed, the reception unit can suggest locations and times with quiet and tranquil scenery. For example, if the user is relaxed, the reception unit can estimate the user's emotions and suggest locations and times with quiet and tranquil scenery. This allows the reception unit to adjust the specification method based on the user's emotions, thereby suggesting more appropriate locations and times. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model that estimates the user's emotions to efficiently estimate the emotions and adjust the designation method.

[0064] The reception unit can analyze the user's past designation history and suggest an appropriate designation method. The reception unit can, for example, suggest new related locations and times based on locations and times previously designated by the user. For example, the reception unit can analyze the user's past designation history and suggest new locations and times related to previously designated locations and times. The reception unit can also automatically display locations and times frequently designated by the user in the past as candidates. For example, the reception unit can automatically display frequently designated locations and times as candidates based on the user's past designation history. The reception unit can also predict and suggest locations and times related to specific seasons or events based on the user's past designation history. For example, the reception unit can analyze the user's past designation history and predict and suggest locations and times related to specific seasons or events. By analyzing the past designation history, the reception unit can suggest optimal locations and times for the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can efficiently suggest designation methods using an AI model that analyzes the user's past designation history.

[0065] When a specific location and time period are specified, the reception unit can perform filtering based on the user's current interests. The reception unit, for example, can suggest locations and times related to a historical theme in which the user is currently interested. For example, the reception unit can suggest locations and times related to a historical theme based on the user's current interests. The reception unit can also suggest locations and times related to an architectural style in which the user is currently interested. For example, the reception unit can suggest locations and times related to an architectural style based on the user's current interests. The reception unit can also suggest locations and times related to a culture or event in which the user is currently interested. For example, the reception unit can suggest locations and times related to a culture or event based on the user's current interests. This allows filtering based on the user's current interests to suggest more relevant locations and times. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can efficiently perform filtering using an AI model that analyzes the user's current interests.

[0066] The reception unit can estimate the user's emotions and determine the priority of the locations and times to be specified based on the estimated user's emotions. For example, if the user is feeling nostalgic, the reception unit can prioritize displaying locations and times associated with past memories. For example, if the user is feeling nostalgic, the reception unit can prioritize displaying locations and times associated with past memories. Furthermore, if the user is excited, the reception unit can prioritize displaying locations and times associated with historical events or important events. For example, if the user is excited, the reception unit can prioritize displaying locations and times associated with historical events or important events. Furthermore, if the user is relaxed, the reception unit can prioritize displaying locations and times with quiet and tranquil scenery. For example, if the user is relaxed, the reception unit can prioritize displaying locations and times with quiet and tranquil scenery. By determining the priority based on the user's emotions, more appropriate locations and times can be suggested. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model that estimates a user's emotions to efficiently estimate emotions and determine priorities.

[0067] When a specific location and time period are specified, the reception unit can prioritize suggesting highly relevant locations and times based on the user's geographical location information. The reception unit, for example, prioritizes suggesting past scenery close to the user's current location. For example, the reception unit can prioritize suggesting past scenery close to the user's current location based on the user's geographical location information. The reception unit can also suggest locations and times when historical events related to the user's current location occurred. For example, the reception unit can suggest locations and times when historical events related to the current location occurred based on the user's geographical location information. The reception unit can also prioritize suggesting locations and times that are easily accessible from the user's current location. For example, the reception unit can prioritize suggesting locations and times that are easily accessible from the user's current location based on the user's geographical location information. This allows the reception unit to suggest more relevant locations and times by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can efficiently suggest highly relevant locations and times using an AI model that analyzes the user's geographical location information.

[0068] When a specific location and time period are specified, the reception unit can analyze the user's social media activity and suggest highly relevant locations and times. The reception unit can, for example, suggest locations and times frequently mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and suggest locations and times frequently mentioned. The reception unit can also predict and suggest locations and times of interest based on the content of the user's social media posts. For example, the reception unit can analyze the content of the user's social media posts and predict and suggest locations and times of interest. Furthermore, the reception unit can make suggestions based on the locations and times visited by the user's social media friends. For example, the reception unit can make suggestions based on the locations and times visited by the user's social media friends. In this way, by analyzing social media activity, it is possible to suggest locations and times based on the user's interests. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can efficiently suggest highly relevant locations and times using an AI model that analyzes the user's social media activity.

[0069] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling nostalgic, the collection unit can prioritize collecting data related to past memories. For example, the collection unit can estimate the user's emotions and prioritize collecting data related to past memories when the user is feeling nostalgic. Furthermore, if the user is excited, the collection unit can prioritize collecting data related to historical events or important events. For example, the collection unit can estimate the user's emotions and prioritize collecting data related to historical events or important events when the user is excited. Furthermore, the collection unit can prioritize collecting data related to quiet and tranquil scenery when the user is relaxed. For example, the collection unit can estimate the user's emotions and prioritize collecting data related to quiet and tranquil scenery when the user is relaxed. This allows for adjusting the timing of data collection based on the user's emotions, thereby collecting more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use an AI model that estimates a user's emotions to efficiently estimate emotions and adjust the timing of data collection.

[0070] The collection unit can analyze past data collection history and select an appropriate collection method. The collection unit, for example, collects new related data based on data previously collected by the user. For example, the collection unit can analyze the user's past data collection history and collect new data related to the previously collected data. The collection unit can also automatically display data that the user frequently collected in the past as candidates. For example, the collection unit can automatically display frequently collected data as candidates based on the user's past data collection history. Furthermore, the collection unit can predict and collect data related to a specific theme or event from the user's past data collection history. For example, the collection unit can analyze the user's past data collection history and predict and collect data related to a specific theme or event. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can efficiently select a collection method using an AI model that analyzes the user's past data collection history.

[0071] The collection unit may perform filtering based on the user's current interests when collecting data. For example, the collection unit may prioritize collecting data related to historical themes in which the user is currently interested. For example, the collection unit may prioritize collecting data related to historical themes based on the user's current interests. The collection unit may also prioritize collecting data related to architectural styles in which the user is currently interested. For example, the collection unit may prioritize collecting data related to architectural styles based on the user's current interests. The collection unit may also prioritize collecting data related to cultures or events in which the user is currently interested. For example, the collection unit may prioritize collecting data related to cultures or events based on the user's current interests. This allows for filtering based on the user's current interests to collect more relevant data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may perform efficient filtering using an AI model that analyzes the user's current interests.

[0072] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling nostalgic, the collection unit can prioritize collecting data related to past memories. For example, the collection unit can estimate the user's emotions and prioritize collecting data related to past memories when the user is feeling nostalgic. Furthermore, if the user is excited, the collection unit can prioritize collecting data related to historical events or important events. For example, the collection unit can estimate the user's emotions and prioritize collecting data related to historical events or important events when the user is excited. Furthermore, the collection unit can prioritize collecting data related to quiet and tranquil scenery when the user is relaxed. For example, the collection unit can estimate the user's emotions and prioritize collecting data related to quiet and tranquil scenery when the user is relaxed. This allows more appropriate data to be collected by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use an AI model that estimates a user's emotions to efficiently estimate emotions and determine the priority of data.

[0073] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting data related to past scenery close to the user's current location. For example, the collection unit can prioritize collecting data related to past scenery close to the current location based on the user's geographical location information. The collection unit can also prioritize collecting data related to historical events related to the user's current location. For example, the collection unit can prioritize collecting data related to historical events related to the current location based on the user's geographical location information. Furthermore, the collection unit can also prioritize collecting data related to locations that are easily accessible from the user's current location. For example, the collection unit can prioritize collecting data related to locations that are easily accessible from the current location based on the user's geographical location information. This allows for more relevant data to be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect highly relevant data using an AI model that analyzes the user's geographical location information.

[0074] During data collection, the collection unit can analyze the user's social media activity and collect highly relevant data. The collection unit, for example, collects data related to places and times frequently mentioned by the user on social media. For example, the collection unit can analyze the user's social media activity and collect data related to places and times frequently mentioned. The collection unit can also collect data related to places and times of interest from the user's social media posts. For example, the collection unit can analyze the user's social media posts and collect data related to places and times of interest. Furthermore, the collection unit can collect data related to places and times visited by the user's social media friends. For example, the collection unit can collect data related to places and times visited by the user's social media friends. In this way, data based on the user's interests can be collected by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect highly relevant data using an AI model that analyzes the user's social media activity.

[0075] The generation unit can estimate the user's emotions and adjust the scenery generation expression method based on the estimated user's emotions. For example, if the user is feeling nostalgic, the generation unit can generate a scenery associated with past memories in warm colors. For example, if the user is feeling nostalgic, the generation unit can generate a scenery associated with past memories in warm colors. Furthermore, if the user is excited, the generation unit can generate a scenery that emphasizes historical events or important events. For example, if the user is excited, the generation unit can generate a scenery that emphasizes historical events or important events. Furthermore, if the user is relaxed, the generation unit can generate a quiet and calm scenery. For example, if the user is relaxed, the generation unit can generate a quiet and calm scenery. By adjusting the scenery generation expression method based on the user's emotions, more appropriate scenery can be generated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may use an AI model that estimates a user's emotions to efficiently estimate the emotions and adjust the expression method for generating scenery.

[0076] The generation unit can adjust the level of detail of the generated scenery based on the importance of the data when generating the scenery. For example, the generation unit reproduces scenery related to important historical events in detail. For example, the generation unit can reproduce scenery related to important historical events in detail based on the importance of the data. The generation unit can also reproduce general scenery in a simplified manner. For example, the generation unit can reproduce general scenery in a simplified manner based on the importance of the data. Furthermore, the generation unit can reproduce scenery in which the user is particularly interested in in detail. For example, the generation unit can reproduce scenery in which the user is particularly interested in in detail based on the importance of the data. In this way, by adjusting the level of detail of the generated scenery based on the importance of the data, more important scenery can be reproduced in detail. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently adjust the level of detail of the generated scenery using an AI model that evaluates the importance of the data.

[0077] The generation unit can apply different generation algorithms depending on the classification of data when generating a landscape. For example, when recreating the exterior of a building, the generation unit uses an algorithm based on architectural data. For example, the generation unit can use an algorithm based on architectural data to recreate the exterior of a building. The generation unit can also use an algorithm based on geographic data to recreate a natural landscape. For example, the generation unit can use an algorithm based on geographic data to recreate a natural landscape. Furthermore, the generation unit can also use an algorithm based on literature data when recreating a historical event. For example, the generation unit can use an algorithm based on literature data to recreate a historical event. In this way, by applying different generation algorithms depending on the classification of data, more appropriate landscapes can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently generate landscapes using an AI model that applies different generation algorithms depending on the classification of data.

[0078] The generation unit can estimate the user's emotions and adjust the length of the scenery to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point scenery. For example, the generation unit can estimate the user's emotions and generate a short and to-the-point scenery when the user is in a hurry. The generation unit can also generate a longer scenery with detailed explanations when the user is relaxed. For example, the generation unit can estimate the user's emotions and generate a longer scenery with detailed explanations when the user is relaxed. Furthermore, the generation unit can generate a scenery with visually stimulating effects when the user is excited. For example, the generation unit can estimate the user's emotions and generate a scenery with visually stimulating effects when the user is excited. This allows the user to adjust the length of the scenery based on the user's emotions, thereby providing a more appropriate scenery. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may use an AI model that estimates the user's emotions to efficiently estimate the emotions and adjust the length of the scenery.

[0079] When generating a landscape, the generation unit can determine a generation priority based on the date and time of data collection. The generation unit, for example, prioritizes generating a landscape based on the latest data. For example, the generation unit can prioritize generating a landscape based on the latest data based on the date and time of data collection. The generation unit can also prioritize generating a landscape based on data from a historically important period. For example, the generation unit can prioritize generating a landscape based on data from a historically important period based on the date and time of data collection. The generation unit can also prioritize generating a landscape based on data from a period specified by a user. For example, the generation unit can prioritize generating a landscape based on data from a period specified by a user based on the date and time of data collection. In this way, by determining the generation priority based on the date and time of data collection, more important landscapes can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently determine the generation priority using an AI model that analyzes the date and time of data collection.

[0080] When generating scenery, the generation unit can adjust the order of generation based on the relevance of data. For example, the generation unit can first generate scenery based on data related to important historical events. For example, the generation unit can first generate scenery based on data related to important historical events based on the relevance of data. The generation unit can also first generate scenery based on data in which the user is particularly interested based on the relevance of data. For example, the generation unit can first generate scenery based on data in which the user is particularly interested based on the relevance of data. Furthermore, the generation unit can postpone generation of scenery based on general data. For example, the generation unit can postpone generation of scenery based on general data based on the relevance of data. In this way, by adjusting the order of generation based on the relevance of data, it is possible to preferentially generate scenery with higher relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can efficiently adjust the order of generation using an AI model that evaluates the relevance of data.

[0081] The providing unit can estimate the user's emotions and adjust the way in which scenery is provided based on the estimated user's emotions. For example, if the user is feeling nostalgic, the providing unit can provide scenery associated with past memories in warm colors. For example, if the user is feeling nostalgic, the providing unit can provide scenery associated with past memories in warm colors. Furthermore, if the user is excited, the providing unit can provide scenery that emphasizes historical events or important events. For example, the providing unit can estimate the user's emotions and provide scenery that emphasizes historical events or important events when the user is excited. Furthermore, the providing unit can provide quiet and calm scenery when the user is relaxed. For example, the providing unit can estimate the user's emotions and provide quiet and calm scenery when the user is relaxed. This allows the system to provide more appropriate scenery by adjusting the way in which scenery is provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model that estimates the user's emotions to efficiently estimate the emotions and adjust the way the scenery is provided.

[0082] When providing scenery, the providing unit can select an appropriate providing method by referring to the user's past browsing history. The providing unit, for example, can provide new related scenery based on scenery that the user frequently viewed in the past. For example, the providing unit can refer to the user's past browsing history and provide new related scenery based on the frequently viewed scenery. The providing unit can also analyze trends in scenery that the user has viewed in the past and select an optimal providing method. For example, the providing unit can analyze trends in scenery viewed based on the user's past browsing history and select an optimal providing method. Furthermore, the providing unit can predict and provide scenery related to a specific theme or event based on the user's past browsing history. For example, the providing unit can predict and provide scenery related to a specific theme or event based on the user's past browsing history. In this way, the optimal scenery can be provided to the user by referring to the past browsing history. Some or all of the above-described 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 efficiently select a providing method by using an AI model that analyzes the user's past browsing history.

[0083] When providing scenery, the providing unit can perform filtering based on the user's current interests. For example, the providing unit can prioritize providing scenery related to a historical theme in which the user is currently interested. For example, the providing unit can prioritize providing scenery related to a historical theme based on the user's current interests. The providing unit can also prioritize providing scenery related to an architectural style in which the user is currently interested. For example, the providing unit can prioritize providing scenery related to an architectural style based on the user's current interests. Furthermore, the providing unit can also prioritize providing scenery related to a culture or event in which the user is currently interested. For example, the providing unit can prioritize providing scenery related to a culture or event based on the user's current interests. This allows for filtering based on the user's current interests to provide more relevant scenery. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can efficiently perform filtering using an AI model that analyzes the user's current interests.

[0084] The providing unit can estimate the user's emotions and determine the priority of providing scenery based on the estimated user's emotions. For example, if the user is feeling nostalgic, the providing unit can prioritize providing scenery related to past memories. For example, if the user is feeling nostalgic, the providing unit can prioritize providing scenery related to past memories. Furthermore, if the user is excited, the providing unit can prioritize providing scenery that highlights historical events or important events. For example, if the user is excited, the providing unit can prioritize providing scenery that highlights historical events or important events. Furthermore, if the user is relaxed, the providing unit can prioritize providing quiet and calm scenery. For example, if the user is relaxed, the providing unit can prioritize providing quiet and calm scenery. This allows for more appropriate scenery to be provided by determining the priority of providing scenery based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model that estimates a user's emotions to efficiently estimate the emotions and determine the priority of providing scenery.

[0085] When providing scenery, the providing unit can prioritize providing highly relevant scenery by taking into account the user's geographical location information. The providing unit, for example, prioritizes providing past scenery close to the user's current location. For example, the providing unit can prioritize providing past scenery close to the current location based on the user's geographical location information. The providing unit can also provide scenery where historical events related to the user's current location occurred. For example, the providing unit can provide scenery where historical events related to the current location occurred based on the user's geographical location information. The providing unit can also prioritize providing scenery of places that are easily accessible from the user's current location. For example, the providing unit can prioritize providing scenery of places that are easily accessible from the current location based on the user's geographical location information. This makes it possible to provide more relevant scenery by taking the user's geographical location information into account. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide highly relevant scenery by using an AI model that analyzes the user's geographical location information.

[0086] When providing scenery, the providing unit can analyze the user's social media activity and provide highly relevant scenery. The providing unit can, for example, provide scenery related to places and times frequently mentioned by the user on social media. For example, the providing unit can analyze the user's social media activity and provide scenery related to places and times frequently mentioned by the user. The providing unit can also provide scenery related to places and times of interest based on the content of the user's social media posts. For example, the providing unit can analyze the content of the user's social media posts and provide scenery related to places and times of interest. Furthermore, the providing unit can provide scenery related to places and times visited by the user's social media friends. For example, the providing unit can provide scenery related to places and times visited by the user's social media friends. In this way, by analyzing social media activity, scenery based on the user's interests and concerns can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can efficiently provide highly relevant scenery using an AI model that analyzes the user's social media activity. === Hard Collateral 1-1 === 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 a location and time specification from a user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data on historical photographs, documents, and maps from the Internet or databases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate a past landscape. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated past landscape to the user. === Hard Collateral 1-2 === 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 a location and time designation from a user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data on historical photographs, documents, and maps from the Internet or a database. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate a past scene. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated past scene to the user. === Hard Collateral 1-3 === 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 a location and time designation from a user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data on historical photographs, documents, and maps from the Internet or a database. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate past scenery. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated past scenery to the user. === Hard Collateral 1-4 === 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 a location and time specification from a user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data on historical photographs, documents, and maps from the Internet or a database. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate a past scene. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated past scene to the user.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The past scenery reproduction system may further include an audio guide unit. The audio guide unit may provide audio historical information and anecdotes related to the generated past scenery. For example, when a user is viewing a particular building, the user may hear an audio commentary about the building's history and architectural style. The audio guide unit may also provide a customized guide based on the user's interests. For example, if the user is interested in a particular era or culture, the audio guide unit may provide information related to that era or culture preferentially. The audio guide unit may also adjust the content of the guide to match the user's walking speed. For example, if the user is walking slowly, the audio guide unit may provide a detailed commentary, and if the user is walking quickly, the audio guide unit may provide a concise commentary that focuses on the main points. This allows the user to enjoy the past scenery not only visually but also aurally, providing a more immersive experience.

[0089] The collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. For example, if the user is feeling nostalgic, data related to past memories can be prioritized for collection. Also, if the user is excited, data related to historical events or important happenings can be prioritized for collection. Furthermore, if the user is relaxed, data related to quiet and tranquil scenery can be prioritized for collection. Thus, by determining the priority of data collection based on the user's emotions, more appropriate data can be collected. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI.

[0090] The generation unit can estimate the user's emotions and adjust the way in which scenery is generated based on the estimated user emotions. For example, if the user is feeling nostalgic, a scenery related to past memories can be generated in warm colors. If the user is excited, a scenery that emphasizes historical events or important happenings can be generated. Furthermore, if the user is relaxed, a quiet and calm scenery can be generated. By adjusting the way in which scenery is generated based on the user's emotions, more appropriate scenery can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI.

[0091] The providing unit can estimate the user's emotions and adjust the way in which scenery is provided based on the estimated user's emotions. For example, if the user is feeling nostalgic, scenery related to past memories can be provided in warm colors. If the user is excited, scenery that highlights historical events or important happenings can be provided. Furthermore, if the user is relaxed, quiet and calm scenery can be provided. In this way, by adjusting the way in which scenery is provided based on the user's emotions, more appropriate scenery can be provided. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI.

[0092] The providing unit can estimate the user's emotions and determine the priority of scenery provision based on the estimated user emotions. For example, if the user is feeling nostalgic, scenery related to past memories can be provided preferentially. Also, if the user is excited, scenery highlighting historical events or important happenings can be provided preferentially. Furthermore, if the user is relaxed, quiet and calm scenery can be provided preferentially. In this way, by determining the priority of scenery provision based on the user's emotions, more appropriate scenery can be provided. Emotion estimation is realized using an emotion estimation function using an emotion engine or generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI.

[0093] The past scenery reproduction system may further include an interactive map unit. The interactive map unit displays past scenery generated based on a location and time period specified by the user on a map and allows the user to move freely on the map. For example, when a user is viewing past scenery of a particular city, the user can move to another location on the map of that city. The interactive map unit may also display information related to a location or time period in which the user is interested on the map. For example, if the user is interested in a particular building, historical information or episodes related to that building may be displayed on the map. Furthermore, the interactive map unit may generate new scenery based on a location and time period specified by the user on the map. This allows the user to explore past scenery through the map and learn more about history.

[0094] The past scene reproduction system may further include a user feedback unit. The user feedback unit may collect feedback from users about scenes viewed by the user and use the collected feedback to improve the system. For example, the user may input their thoughts about a particular scene and suggestions for improvement. The user feedback unit may also adjust the scene generation algorithm based on the collected feedback. For example, if the user feels that the reproduction of a particular scene is poor, the unit may make adjustments to improve the reproduction based on the user feedback. Furthermore, the user feedback unit may add new functions or content based on the user feedback. This allows the system to be improved based on user opinions, providing a more satisfying experience.

[0095] The past scenery reproduction system may further include an education mode unit. The education mode unit may provide a function for using the generated past scenery for educational purposes. For example, a scenery related to a specific era or event may be generated for use in a history class and provided to students as a visual teaching material. The education mode unit may also provide quizzes and assignments related to the scenery. For example, students may deepen their historical knowledge by answering quizzes while viewing the scenery. Furthermore, the education mode unit may allow teachers to create customized scenery and content and provide them to students. This may promote use in educational settings and increase students' motivation to learn.

[0096] The past scenery reproduction system may further include a social sharing unit. The social sharing unit provides a function that allows a user to share the generated past scenery on social media. For example, while a user is viewing a particular scenery, the user can share the scenery on a social networking site. The social sharing unit may also collect comments and reactions on the scenery shared by the user and use the collected comments to improve the system. For example, the social sharing unit may collect comments from other users on the scenery shared by the user and adjust the scenery generation algorithm based on the feedback. Furthermore, the social sharing unit may also allow a user to share scenery with other users and view it collaboratively. This allows the user to enjoy past scenery together with others, providing a richer experience.

[0097] The past scenery reproduction system can further include a personalized recommendation unit. The personalized recommendation unit can automatically suggest related scenery based on the user's past browsing history and interests. For example, if the user is interested in a particular era or region, it can prioritize suggesting scenery related to that era or region. The personalized recommendation unit can also analyze the user's browsing history and suggest scenery that the user has not seen yet but may be interested in. Furthermore, the personalized recommendation unit can improve the content of the suggestions based on user feedback. This allows users to efficiently find scenery that matches their interests and concerns, resulting in a more satisfying experience.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit receives a designation of a specific location and time from a user. The location and time designated by the user may include, but are not limited to, geographic coordinates or a historical period. Step 2: The collection unit collects data on historical photographs, documents, and maps based on the specifications received by the reception unit. The collected data includes, but is not limited to, the resolution of the photographs, the type of documents, and the scale of the maps. The collection unit collects relevant data from, for example, the Internet or databases. For example, data can be collected from specific websites or archive databases. Step 3: The generation unit analyzes the data collected by the collection unit and generates a past landscape. For example, an image analysis algorithm or text mining technology may be used for the analysis, but is not limited to these examples. The generation unit can, for example, reproduce not only the exterior of a building, but also its internal structure and decoration. For example, it can reproduce the room layout, furniture arrangement, decorative design, etc. Step 4: The providing unit provides the past scenery generated by the generating unit to the user. The providing unit allows the user to view the past scenery from a 360-degree perspective, such as street view. For example, the providing unit can use a 360-degree panoramic view or virtual reality technology to allow the user to have an immersive experience.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 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 a designation of a specific location and time from a user; a collection unit that collects data on historical photographs, documents, and maps based on the specifications received by the reception unit; a generation unit that analyzes the data collected by the collection unit and generates past scenery; a providing unit that provides the user with the past scenery generated by the generating unit. A system characterized by:

2. The collecting unit Collect relevant data from the internet or databases 2. The system of claim 1.

3. The generation unit Reproduce not only the exterior of the building, but also the internal structure and decoration 2. The system of claim 1.

4. The providing unit Allows users to view past landscapes in a 360-degree perspective, similar to Street View 2. The system of claim 1.

5. The reception unit Inferring user emotions and adjusting how to specify a specific location and time based on the estimated user emotions 2. The system of claim 1.

6. The reception unit Analyze the user's past selection history and suggest the appropriate selection method 2. The system of claim 1.

7. The reception unit Filtering based on a user's current interests or concerns when specifying a specific location and time period 2. The system of claim 1.

8. The reception unit To determine a method for estimating a user's emotion and prioritizing a location and a time to specify based on the estimated user's emotion.

2. The system of claim 1.

9. The reception unit When specifying a specific location and time, the app will prioritize suggestions based on the user's geographic location to find the most relevant location and time.

2. The system of claim 1.

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