System

Abstract

An object of a system according to an embodiment is to provide an experience in which an AI avatar based on a face photo of a user virtually travels around the world.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a travel setting unit, and a provision unit. The reception unit receives input of a face photo from a user. The generation unit analyzes the face photo received by the reception unit and generates an AI avatar. The travel setting unit describes specific technical means for setting the AI avatar generated by the generation unit to virtually travel around the world. The providing unit provides the photograph generated by the travel setting unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 techniques have not adequately provided a virtual experience based on a user's facial photograph, and there is room for improvement.

[0005] The system according to the embodiment aims to provide the user with the experience of virtually traveling around the world with an AI avatar based on a photograph of the user's face. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a journey setting unit, and a provision unit. The reception unit receives a facial photo from a user. The generation unit analyzes the facial photo received by the reception unit and generates an AI avatar. The journey setting unit describes specific technical means for setting the AI ​​avatar generated by the generation unit to virtually travel around the world. The provision unit provides the user with the photo generated by the journey setting unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide the user with the experience of virtually traveling around the world with an AI avatar based on a photograph of the user's face. [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 service according to an embodiment of the present invention is a service in which an AI avatar generated from a user's facial photograph is sent photos from around the world, with the AI ​​avatar traveling around the world. In this service, a user uploads a photo of their face, and a generation AI generates an AI avatar based on the photo. The generated AI avatar is set to virtually travel around the world, generating photos of landscapes and famous places in various locations. These photos are generated in real time by the generation AI and sent to the user. For example, a user uploads a photo of their face. For example, a user can upload a photo of their face taken with a smartphone. This photo is input into the generation AI. The generation AI then analyzes the input photo and generates an AI avatar that reflects the user's features. The generation AI analyzes facial features such as the contours of the face and the eyes, nose, and mouth, and generates the AI ​​avatar based on the results. For example, if the user's facial contours are round, the AI ​​avatar is generated to have a similarly round contour. The generated AI avatar is set to virtually travel around the world. The generation AI uses data on the landscapes and famous places of various locations to generate photos that make the AI ​​avatar appear to be in those locations. For example, a photo of an AI avatar standing in front of the Eiffel Tower in Paris or the Statue of Liberty in New York is generated. These photos are generated in real time by the generative AI and sent to the user. The user receives these photos on their smartphone or computer and can enjoy watching their AI avatar traveling around the world. For example, the user can post these photos on social media and share them with friends and family. This allows the service to allow users to enjoy watching their AI avatar traveling around the world. It also allows users to virtually visit famous places around the world without actually traveling. For example, even users who are unable to travel can use this service to enjoy the pleasure of virtually traveling around the world. This allows users to enjoy watching their AI avatar traveling around the world. It also allows users to virtually visit famous places around the world without actually traveling. For example, even users who are unable to travel can use this service to enjoy the pleasure of virtually traveling around the world.

[0029] The service system according to the embodiment includes a reception unit, a generation unit, a journey setting unit, and a provision unit. The reception unit receives a facial photo from a user. The user's facial photo input may include, for example, uploading a facial photo taken with a smartphone. The reception unit may also include a process for obtaining the user's consent. For example, the process may include a process in which the user signs a consent form before uploading the facial photo. The generation unit uses a generation AI to analyze the facial photo received by the reception unit and generate an AI avatar. The generation AI analyzes facial features such as the contours of the face and the eyes, nose, and mouth, and generates the AI ​​avatar based on the analysis. For example, if the contours of the face are round, the generation AI generates an AI avatar with a similarly round contour. The generation unit may also use the generation AI to estimate the user's emotions and adjust the expression of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation unit generates an AI avatar with a calm expression. The journey setting unit configures the AI ​​avatar generated by the generation unit to virtually travel around the world. The journey setting unit generates photos that make the AI ​​avatar appear to be in each location based on data on landscapes and famous places in each location. For example, the journey setting unit generates a photo of an AI avatar standing in front of the Eiffel Tower in Paris. The journey setting unit can also estimate the user's emotions and adjust the journey setting criteria based on the estimated emotions. For example, if the user is relaxed, the journey setting is set to include many tranquil landscapes. The providing unit provides the user with photos generated by the journey setting unit. The providing unit transmits the generated photos to the user in real time. For example, the providing unit provides the photos through a dedicated app. The providing unit can also transmit photos periodically by email. For example, the providing unit transmits photos to the user every week. In this way, the service system according to the embodiment can generate an AI avatar based on a user's facial photo and provide photos of the user virtually traveling around the world.

[0030] The reception unit may include a process for obtaining consent from the user. The process for obtaining consent may include, for example, a process in which the user signs a consent form before uploading a facial photograph. The reception unit may also have a function for explaining the contents of the consent form to the user. For example, the reception unit may explain the contents of the consent form by voice. The reception unit may also have a function for allowing the user to upload a facial photograph after signing the consent form. For example, the reception unit may accept the upload of a facial photograph after confirming that the consent form has been signed. In this way, privacy protection can be strengthened by obtaining the user's consent.

[0031] The generation unit can analyze the facial contour, eye, nose, and mouth features and generate an AI avatar based on the analysis. For example, the generation unit extracts the facial contour as a feature point to analyze the facial contour. The generation unit can also extract the eye shape and position as feature points to analyze the eye features. For example, if the eye shape is round, the generation unit generates an AI avatar with similarly round eyes. The generation unit can also extract the nose height and width as feature points to analyze the nose features. For example, if the nose is high, the generation unit generates an AI avatar with a similarly high nose. The generation unit can also extract the mouth shape and position as feature points to analyze the mouth features. For example, if the mouth is large, the generation unit generates an AI avatar with a similarly large mouth. This allows the generation of an AI avatar that reflects the user's facial features.

[0032] The travel setting unit can generate photos that make the AI ​​avatar appear to be in a location based on data on landscapes or famous places in each location. For example, the travel setting unit references public databases on the Internet to collect data on landscapes and famous places in each location. The travel setting unit can also use image synthesis technology to generate photos that make the AI ​​avatar appear to be in a location based on data on landscapes and famous places in each location. For example, the travel setting unit can synthesize an image of the AI ​​avatar with an image of the landscape or famous place to generate a photo that makes the AI ​​avatar appear to be in that location. The travel setting unit can also estimate the user's emotions and adjust the travel setting criteria based on the estimated emotions. For example, if the user is relaxing, the travel setting can be set to include many peaceful landscapes. This allows for the generation of photos that show the AI ​​avatar virtually traveling around the world.

[0033] The providing unit can transmit the generated photos to the user in real time. For example, a data transfer technology via the Internet is used for the real-time transmission. Furthermore, the providing unit can provide the photos through a dedicated app in order to transmit the generated photos to the user in real time. For example, the providing unit transmits the generated photos to the user's smartphone through a dedicated app. Furthermore, the providing unit can periodically transmit the generated photos by email. For example, the providing unit transmits the photos to the user every week. This allows the generated photos to be provided to the user in real time.

[0034] The providing unit can provide the photos through a dedicated app. The dedicated app includes, for example, an interface for users to view the photos. The providing unit can also develop and maintain the app in order to provide the photos through the dedicated app. For example, the providing unit periodically updates the app so that users can use the latest functions. The providing unit can also provide users with a photo download function through the dedicated app. For example, users can download the generated photos to their own devices through the dedicated app. In this way, providing the photos through the dedicated app allows users to easily access them.

[0035] The providing unit can periodically send photos by email. Periodic sending includes, for example, weekly or monthly. The providing unit can also have a function for managing a user's email address in order to periodically send photos by email. For example, the providing unit can periodically send photos to an email address registered by the user. The providing unit can also have a function for customizing the content of photos to be sent by email. For example, the providing unit can preferentially send photos of a specific location according to the user's preferences. This allows the user to receive photos periodically by periodically sending photos by email.

[0036] The reception unit can analyze the user's past facial photo upload history and select the optimal upload method. The reception unit, for example, uses data mining technology to analyze the user's past facial photo upload history. The reception unit can also suggest the optimal upload method based on the user's past facial photo upload history. For example, the reception unit can preferentially suggest an upload method that the user has frequently used in the past (e.g., uploading from a smartphone). The reception unit can also analyze the time period during which the user previously uploaded photos and send a notification prompting the user to upload during that time period. For example, the reception unit can analyze the resolution of photos the user previously uploaded and suggest uploading at the optimal resolution. This makes it possible to suggest the optimal upload method based on the user's past history.

[0037] The reception unit can perform filtering based on the user's current environment and situation when uploading a facial photo. The reception unit, for example, uses sensor technology to detect the user's current environment. The reception unit can also use location information services to detect the user's current situation. For example, when the user is outdoors, the reception unit applies a filter that removes background noise. When the user is indoors, the reception unit can also apply a filter that optimizes lighting conditions. For example, when the user is moving, the reception unit applies a filter that performs image stabilization. This allows optimal filtering to be performed according to the user's environment and situation.

[0038] The reception unit can select the optimal upload means depending on the user's input method when uploading a facial photo. The reception unit, for example, uses interface technology to detect the user's input method. The reception unit can also have a guide function to provide the optimal upload means depending on the user's input method. For example, if the user selects voice input, the reception unit provides a means for uploading a facial photo according to a voice guide. Also, if the user selects text input, the reception unit can provide a means for uploading a facial photo according to a text guide. For example, if the user selects image input, the reception unit provides a means for uploading a facial photo according to an image guide. This makes it possible to provide the optimal upload means depending on the user's input method.

[0039] When uploading a facial photo, the reception unit can prioritize uploading highly relevant photos taking into account the user's geographical location information. The reception unit, for example, uses GPS technology to acquire the user's geographical location information. The reception unit can also refer to a database to select highly relevant photos based on the user's geographical location information. For example, if the user is in a specific city, the reception unit can prioritize uploading facial photos taken in that city. Also, if the user is traveling, the reception unit can prioritize uploading facial photos taken at the user's travel destination. For example, if the user is at home, the reception unit can prioritize uploading facial photos taken at home. This makes it possible to upload optimal photos based on the user's geographical location information.

[0040] When uploading a facial photo, the reception unit can analyze the user's social media activity and upload related photos. The reception unit, for example, uses data mining technology to analyze the user's social media activity. The reception unit can also use a social media API to select related photos based on the user's social media activity. For example, the reception unit prioritizes uploading facial photos taken at locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and upload related facial photos. For example, the reception unit refers to the activity of the user's friends on social media and uploads related facial photos. This makes it possible to upload the most appropriate photo based on the user's social media activity.

[0041] The reception unit can customize the upload method by reflecting the user's past feedback when uploading a facial photo. The reception unit, for example, uses a questionnaire function to collect the user's past feedback. The reception unit can also use data analysis technology to customize the upload method based on the user's past feedback. For example, the reception unit preferentially suggests upload methods that the user has previously preferred. The reception unit can also suggest upload methods that the user has previously dissatisfied with, so as to avoid them. For example, the reception unit suggests the optimal upload method based on the user's past feedback. This makes it possible to provide the optimal upload method based on the user's past feedback.

[0042] When generating an AI avatar, the generation unit can adjust the level of detail of the generation based on the importance of the face. For example, the generation unit uses the number of feature points as a criterion to evaluate the importance of the face. The generation unit can also adjust the parameters of the generation AI to adjust the level of detail of the generation based on the importance of the face. For example, if the contours of the user's face are important, the generation unit can increase the detail of the contours. Also, if the user's eyes are distinctive, the generation unit can increase the detail of the eyes. For example, if the user's mouth is distinctive, the generation unit can increase the detail of the mouth. This makes it possible to generate an AI avatar with the optimal level of detail depending on the importance of the face.

[0043] When generating an AI avatar, the generation unit can apply different generation algorithms depending on the face category. For example, the generation unit uses clustering technology to classify face categories. The generation unit can also switch the generation AI model to apply different generation algorithms depending on the face category. For example, if the user's face is round, the generation unit applies a generation algorithm suitable for round faces. Also, if the user's face is elongated, the generation unit can apply a generation algorithm suitable for elongated faces. For example, if the user's face is square, the generation unit applies a generation algorithm suitable for square faces. This makes it possible to apply the optimal generation algorithm depending on the face category.

[0044] When generating an AI avatar, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit uses data mining technology to analyze the user's past generation results. The generation unit can also update the generation AI model to improve the accuracy of generation based on the user's past generation results. For example, the generation unit improves the accuracy of generation based on data of AI avatars generated by the user in the past. The generation unit can also generate an AI avatar that reflects the user's preferred characteristics based on the user's past generation results. For example, the generation unit analyzes the user's past generation results and applies an optimal generation algorithm. This allows the accuracy of generation to be improved based on the user's past generation results.

[0045] When generating an AI avatar, the generation unit can determine the generation priority based on the time when the facial photo was submitted. For example, the generation unit uses timestamp technology to record the time when the facial photo was submitted. The generation unit can also use a scheduling algorithm to determine the generation priority based on the time when the facial photo was submitted. For example, the generation unit can generate an AI avatar by preferentially using a facial photo that was recently submitted. The generation unit can also generate an AI avatar by preferentially using a facial photo related to a specific event. For example, the generation unit can generate an AI avatar by preferentially using a facial photo specified by a user. This allows AI avatars to be generated with optimal priority based on the time when the facial photo was submitted.

[0046] When generating AI avatars, the generation unit can adjust the order of generation based on the relevance of the facial photographs. For example, the generation unit uses content similarity as a criterion to evaluate the relevance of the facial photographs. The generation unit can also adjust parameters of the generation AI to adjust the order of generation based on the relevance of the facial photographs. For example, if the user's facial photograph is related to a specific location, the generation unit can prioritize generating an AI avatar related to that location. Also, if the user's facial photograph is related to a specific event, the generation unit can prioritize generating an AI avatar related to that event. For example, if the user's facial photograph is related to a specific theme, the generation unit can prioritize generating an AI avatar related to that theme. This allows AI avatars to be generated in an optimal order based on the relevance of the facial photographs.

[0047] When generating an AI avatar, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit uses qualifications or years of experience as criteria to evaluate the user's level of expertise. The generation unit can also adjust parameters of the generation AI to adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate an AI avatar that uses a lot of technical terminology. If the user is a beginner, the generation unit can also generate an AI avatar that avoids technical terminology. For example, the generation unit generates an AI avatar that uses appropriate technical terminology according to the user's level of expertise. This makes it possible to generate an AI avatar that uses optimal technical terminology according to the user's level of expertise.

[0048] When setting a trip, the trip setting unit can improve the accuracy of the setting by taking into account the interrelationships between the scenery and famous places of each location. For example, the trip setting unit uses geographical proximity as a criterion to evaluate the interrelationships between the scenery and famous places of each location. The trip setting unit can also refer to a database to improve the accuracy of the setting by taking into account the interrelationships between the scenery and famous places of each location. For example, the trip setting unit sets an efficient trip by taking into account the distance between the scenery and famous places of each location. The trip setting unit can also set an optimal trip by taking into account the popularity of the scenery and famous places of each location. For example, the trip setting unit sets an optimal trip by taking into account the season of the scenery and famous places of each location. This makes it possible to set an optimal trip by taking into account the interrelationships between the scenery and famous places of each location.

[0049] The trip setting unit can set the trip taking into consideration the user's attribute information. The trip setting unit, for example, uses a questionnaire function to acquire the user's attribute information. The trip setting unit can also use data analysis technology to set the optimal trip based on the user's attribute information. For example, the trip setting unit can set an appropriate trip taking into consideration the user's age. The trip setting unit can also set an appropriate trip taking into consideration the user's gender. For example, the trip setting unit can set an appropriate trip taking into consideration the user's hobbies. This makes it possible to set the optimal trip based on the user's attribute information.

[0050] When setting a trip, the trip setting unit can weight the settings based on the user's submission frequency. For example, the trip setting unit uses the number of submissions as a criterion to evaluate the user's submission frequency. The trip setting unit can also use a scheduling algorithm to weight the settings based on the user's submission frequency. For example, if the user submits frequently, the trip setting unit adjusts the trip settings according to the submission frequency. Also, if the user submits infrequently, the trip setting unit can set a special trip. For example, the trip setting unit determines the priority of the trip settings based on the user's submission frequency. This makes it possible to set optimal trip settings based on the user's submission frequency.

[0051] When setting a trip, the trip setting unit can take into account the geographic distribution of each location. For example, the trip setting unit uses a geographic information system (GIS) to evaluate the geographic distribution of each location. The trip setting unit can also refer to a geographic database to set a trip taking into account the geographic distribution of each location. For example, the trip setting unit takes into account the geographic distribution of each location to set an efficient trip. The trip setting unit can also take into account the geographic distribution of each location to set an optimal route. For example, the trip setting unit takes into account the geographic distribution of each location to set a trip according to the season. This makes it possible to set an optimal trip based on the geographic distribution of each location.

[0052] When setting a trip, the trip setting unit can refer to related literature to improve the accuracy of the setting. For example, the trip setting unit uses an academic paper database to refer to related literature. The trip setting unit can also use data analysis technology to improve the accuracy of the setting based on the related literature. For example, the trip setting unit can refer to a travel guidebook to set an optimal trip. The trip setting unit can also refer to a travel blog to set a trip that matches the user's interests. For example, the trip setting unit can refer to a travel review site to set a trip that includes highly rated places. This makes it possible to set an optimal trip by referring to related literature.

[0053] When setting a trip, the trip setting unit can take into account the market value of each location. For example, the trip setting unit uses the popularity of tourist destinations as a criterion to evaluate the market value of each location. The trip setting unit can also refer to an economic database to set a trip that takes into account the market value of each location. For example, the trip setting unit takes into account the market value of each location to set a trip that is cost-effective. The trip setting unit can also take into account the market value of each location to set a trip that includes popular places. For example, the trip setting unit takes into account the market value of each location to set a trip that suits the budget. This makes it possible to set an optimal trip based on the market value of each location.

[0054] When providing photos, the providing unit can improve the accuracy of the provision by taking into account the interrelationships between locations. For example, the providing unit uses geographical proximity as a criterion to evaluate the interrelationships between locations. The providing unit can also refer to a database to improve the accuracy of the provision by taking into account the interrelationships between locations. For example, the providing unit provides efficient photos by taking into account the distance between scenery and famous places in each location. The providing unit can also provide optimal photos by taking into account the popularity of scenery and famous places in each location. For example, the providing unit provides optimal photos by taking into account the season of scenery and famous places in each location. This makes it possible to provide optimal photos based on the interrelationships between locations.

[0055] The providing unit can provide photos taking into consideration the user's attribute information. For example, the providing unit uses a questionnaire function to acquire the user's attribute information. The providing unit can also use data analysis technology to provide optimal photos based on the user's attribute information. For example, the providing unit can provide appropriate photos taking into consideration the user's age. The providing unit can also provide appropriate photos taking into consideration the user's gender. For example, the providing unit can provide appropriate photos taking into consideration the user's hobbies. This makes it possible to provide optimal photos based on the user's attribute information.

[0056] The providing unit can weight the provision of photos based on the user's submission frequency when providing photos. For example, the providing unit uses the number of submissions as a criterion to evaluate the user's submission frequency. The providing unit can also use a scheduling algorithm to weight the provision of photos based on the user's submission frequency. For example, if a user frequently submits photos, the providing unit adjusts the photo provision according to the submission frequency. The providing unit can also provide special photos if a user rarely submits photos. For example, the providing unit determines the priority of photo provision based on the user's submission frequency. This allows optimal photo provision to be provided based on the user's submission frequency.

[0057] The providing unit can provide photos taking into account the geographic distribution of each location. For example, the providing unit uses a geographic information system (GIS) to evaluate the geographic distribution of each location. The providing unit can also refer to a geographic database to provide photos taking into account the geographic distribution of each location. For example, the providing unit provides photos efficiently taking into account the geographic distribution of each location. The providing unit can also set an optimal route taking into account the geographic distribution of each location. For example, the providing unit provides photos according to the season taking into account the geographic distribution of each location. This allows optimal photo provision based on the geographic distribution of each location.

[0058] When providing photos, the providing unit can improve the accuracy of the provided photos by referring to related literature. For example, the providing unit uses an academic paper database to refer to related literature. The providing unit can also use data analysis technology to improve the accuracy of the provided photos based on the related literature. For example, the providing unit can provide optimal photos by referring to a travel guidebook. The providing unit can also provide photos that match the user's interests by referring to a travel blog. For example, the providing unit can provide photos of highly rated places by referring to a travel review site. This allows optimal photo provision by referring to related literature.

[0059] When providing photos, the providing unit can provide them taking into account the market value of each location. For example, the providing unit uses the popularity of tourist destinations as a criterion to evaluate the market value of each location. The providing unit can also refer to an economic database to provide photos taking into account the market value of each location. For example, the providing unit provides photos with high cost performance taking into account the market value of each location. The providing unit can also provide photos of popular places taking into account the market value of each location. For example, the providing unit provides photos according to the budget taking into account the market value of each location. This allows the optimal photo to be provided based on the market value of each location.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When uploading a user's facial photo, the reception unit can analyze the user's past upload history and suggest the optimal upload method. For example, if the user has frequently uploaded from a smartphone in the past, the reception unit can preferentially suggest uploading from a smartphone. Also, if the user has uploaded during a specific time period in the past, the reception unit can send a notification encouraging the user to upload during that time period. Furthermore, if the user has uploaded high-resolution photos in the past, the reception unit can suggest uploading at a similar resolution. This makes it possible to provide the optimal upload method based on the user's past history.

[0062] When generating an AI avatar, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation accuracy can be improved based on data from AI avatars the user has generated in the past. It can also generate an AI avatar that reflects the user's preferred features based on the user's past generation results. It can also analyze the user's past generation results and apply the optimal generation algorithm. This allows the generation accuracy to be improved based on the user's past generation results.

[0063] The providing unit can provide photos taking into consideration the user's attribute information. For example, the providing unit can provide appropriate photos taking into consideration the user's age. The providing unit can also provide appropriate photos taking into consideration the user's gender. Furthermore, the providing unit can also provide appropriate photos taking into consideration the user's hobbies. This makes it possible to provide optimal photos based on the user's attribute information.

[0064] When generating an AI avatar, the generation unit can apply different generation algorithms depending on the face category. For example, if the user has a round face, a generation algorithm suitable for round faces can be applied. If the user has a long and thin face, a generation algorithm suitable for long and thin faces can be applied. Furthermore, if the user has a square face, a generation algorithm suitable for square faces can be applied. This makes it possible to apply the optimal generation algorithm depending on the face category.

[0065] When setting a trip, the trip setting unit can improve the accuracy of the setting by taking into account the interrelationships between the scenery and famous places of each location. For example, an efficient trip is set by taking into account the distance between the scenery and famous places of each location. Also, an optimal trip can be set by taking into account the popularity of the scenery and famous places of each location. Furthermore, an optimal trip can be set by taking into account the season of the scenery and famous places of each location. In this way, an optimal trip can be set by taking into account the interrelationships between the scenery and famous places of each location.

[0066] When generating an AI avatar, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate an AI avatar that uses a lot of technical terminology. On the other hand, if the user is a beginner, the generation unit can generate an AI avatar that avoids technical terminology. Furthermore, the generation unit can generate an AI avatar that uses appropriate technical terminology according to the user's level of expertise. This makes it possible to generate an AI avatar that uses the most appropriate technical terminology according to the user's level of expertise.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives input of a facial photo from the user. Input of a facial photo from the user includes, for example, uploading a facial photo taken with a smartphone. The reception unit may also include a process for obtaining consent from the user. For example, the process may include a process in which the user signs a consent form before uploading the facial photo. Step 2: The generation unit uses the generation AI to analyze the facial photo received by the reception unit and generate an AI avatar. The generation AI analyzes facial features such as the contours of the face and the eyes, nose, and mouth, and generates an AI avatar based on that. For example, if the contours of the face are round, the generation AI generates an AI avatar with a similarly round contour. The generation unit can also use the generation AI to estimate the user's emotions and adjust the way the AI ​​avatar expresses itself based on the estimated emotions. For example, if the user is relaxed, the generation AI generates an AI avatar with a calm expression. Step 3: The travel setting unit sets up the AI ​​avatar generated by the generation unit to virtually travel around the world. Based on data on the scenery and landmarks of each location, the travel setting unit generates photos that make the AI ​​avatar appear to be in that location. For example, the travel setting unit generates a photo of the AI ​​avatar standing in front of the Eiffel Tower in Paris. The travel setting unit can also estimate the user's emotions and adjust the travel setting criteria based on the estimated emotions. For example, if the user is relaxed, the travel setting unit sets up a trip that includes many tranquil landscapes. Step 4: The providing unit provides the photos generated by the journey setting unit to the user. The providing unit transmits the generated photos to the user in real time. For example, the providing unit provides the photos through a dedicated app. The providing unit can also transmit the photos periodically by email. For example, the providing unit transmits the photos to the user every week.

[0069] (Example 2) A service according to an embodiment of the present invention is a service in which an AI avatar generated from a user's facial photograph is sent photos from around the world, with the AI ​​avatar traveling around the world. In this service, a user uploads a photo of their face, and a generation AI generates an AI avatar based on the photo. The generated AI avatar is set to virtually travel around the world, generating photos of landscapes and famous places in various locations. These photos are generated in real time by the generation AI and sent to the user. For example, a user uploads a photo of their face. For example, a user can upload a photo of their face taken with a smartphone. This photo is input into the generation AI. The generation AI then analyzes the input photo and generates an AI avatar that reflects the user's features. The generation AI analyzes facial features such as the contours of the face and the eyes, nose, and mouth, and generates the AI ​​avatar based on the results. For example, if the user's facial contours are round, the AI ​​avatar is generated to have a similarly round contour. The generated AI avatar is set to virtually travel around the world. The generation AI uses data on the landscapes and famous places of various locations to generate photos that make the AI ​​avatar appear to be in those locations. For example, a photo of an AI avatar standing in front of the Eiffel Tower in Paris or the Statue of Liberty in New York is generated. These photos are generated in real time by the generative AI and sent to the user. The user receives these photos on their smartphone or computer and can enjoy watching their AI avatar traveling around the world. For example, the user can post these photos on social media and share them with friends and family. This allows the service to allow users to enjoy watching their AI avatar traveling around the world. It also allows users to virtually visit famous places around the world without actually traveling. For example, even users who are unable to travel can use this service to enjoy the pleasure of virtually traveling around the world. This allows users to enjoy watching their AI avatar traveling around the world. It also allows users to virtually visit famous places around the world without actually traveling. For example, even users who are unable to travel can use this service to enjoy the pleasure of virtually traveling around the world.

[0070] The service system according to the embodiment includes a reception unit, a generation unit, a journey setting unit, and a provision unit. The reception unit receives a facial photo from a user. The user's facial photo input may include, for example, uploading a facial photo taken with a smartphone. The reception unit may also include a process for obtaining the user's consent. For example, the process may include a process in which the user signs a consent form before uploading the facial photo. The generation unit uses a generation AI to analyze the facial photo received by the reception unit and generate an AI avatar. The generation AI analyzes facial features such as the contours of the face and the eyes, nose, and mouth, and generates the AI ​​avatar based on the analysis. For example, if the contours of the face are round, the generation AI generates an AI avatar with a similarly round contour. The generation unit may also use the generation AI to estimate the user's emotions and adjust the expression of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation unit generates an AI avatar with a calm expression. The journey setting unit configures the AI ​​avatar generated by the generation unit to virtually travel around the world. The journey setting unit generates photos that make the AI ​​avatar appear to be in each location based on data on landscapes and famous places in each location. For example, the journey setting unit generates a photo of an AI avatar standing in front of the Eiffel Tower in Paris. The journey setting unit can also estimate the user's emotions and adjust the journey setting criteria based on the estimated emotions. For example, if the user is relaxed, the journey setting is set to include many tranquil landscapes. The providing unit provides the user with photos generated by the journey setting unit. The providing unit transmits the generated photos to the user in real time. For example, the providing unit provides the photos through a dedicated app. The providing unit can also transmit photos periodically by email. For example, the providing unit transmits photos to the user every week. In this way, the service system according to the embodiment can generate an AI avatar based on a user's facial photo and provide photos of the user virtually traveling around the world.

[0071] The reception unit may include a process for obtaining consent from the user. The process for obtaining consent may include, for example, a process in which the user signs a consent form before uploading a facial photograph. The reception unit may also have a function for explaining the contents of the consent form to the user. For example, the reception unit may explain the contents of the consent form by voice. The reception unit may also have a function for allowing the user to upload a facial photograph after signing the consent form. For example, the reception unit may accept the upload of a facial photograph after confirming that the consent form has been signed. In this way, privacy protection can be strengthened by obtaining the user's consent.

[0072] The generation unit can analyze the facial contour, eye, nose, and mouth features and generate an AI avatar based on the analysis. For example, the generation unit extracts the facial contour as a feature point to analyze the facial contour. The generation unit can also extract the eye shape and position as feature points to analyze the eye features. For example, if the eye shape is round, the generation unit generates an AI avatar with similarly round eyes. The generation unit can also extract the nose height and width as feature points to analyze the nose features. For example, if the nose is high, the generation unit generates an AI avatar with a similarly high nose. The generation unit can also extract the mouth shape and position as feature points to analyze the mouth features. For example, if the mouth is large, the generation unit generates an AI avatar with a similarly large mouth. This allows the generation of an AI avatar that reflects the user's facial features.

[0073] The travel setting unit can generate photos that make the AI ​​avatar appear to be in a location based on data on landscapes or famous places in each location. For example, the travel setting unit references public databases on the Internet to collect data on landscapes and famous places in each location. The travel setting unit can also use image synthesis technology to generate photos that make the AI ​​avatar appear to be in a location based on data on landscapes and famous places in each location. For example, the travel setting unit can synthesize an image of the AI ​​avatar with an image of the landscape or famous place to generate a photo that makes the AI ​​avatar appear to be in that location. The travel setting unit can also estimate the user's emotions and adjust the travel setting criteria based on the estimated emotions. For example, if the user is relaxing, the travel setting can be set to include many peaceful landscapes. This allows for the generation of photos that show the AI ​​avatar virtually traveling around the world.

[0074] The providing unit can transmit the generated photos to the user in real time. For example, a data transfer technology via the Internet is used for the real-time transmission. Furthermore, the providing unit can provide the photos through a dedicated app in order to transmit the generated photos to the user in real time. For example, the providing unit transmits the generated photos to the user's smartphone through a dedicated app. Furthermore, the providing unit can periodically transmit the generated photos by email. For example, the providing unit transmits the photos to the user every week. This allows the generated photos to be provided to the user in real time.

[0075] The providing unit can provide the photos through a dedicated app. The dedicated app includes, for example, an interface for users to view the photos. The providing unit can also develop and maintain the app in order to provide the photos through the dedicated app. For example, the providing unit periodically updates the app so that users can use the latest functions. The providing unit can also provide users with a photo download function through the dedicated app. For example, users can download the generated photos to their own devices through the dedicated app. In this way, providing the photos through the dedicated app allows users to easily access them.

[0076] The providing unit can periodically send photos by email. Periodic sending includes, for example, weekly or monthly. The providing unit can also have a function for managing a user's email address in order to periodically send photos by email. For example, the providing unit can periodically send photos to an email address registered by the user. The providing unit can also have a function for customizing the content of photos to be sent by email. For example, the providing unit can preferentially send photos of a specific location according to the user's preferences. This allows the user to receive photos periodically by periodically sending photos by email.

[0077] The reception unit can estimate the user's emotions and adjust the timing of uploading a facial photo based on the estimated user's emotions. The reception unit, for example, uses facial expression analysis technology to estimate the user's emotions. The reception unit can also use voice analysis technology to estimate the user's emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression analysis technology. The reception unit can also record the user's voice with a microphone and estimate the emotion using voice analysis technology. For example, the reception unit can send a notification prompting the user to upload a facial photo if the user is relaxed. The reception unit can also suggest postponing uploading a facial photo if the user is feeling stressed. For example, the reception unit can send a notification prompting the user to immediately upload a facial photo if the user is excited. This makes it possible to prompt the user to upload a facial photo at the optimal timing depending on the user's emotions.

[0078] The reception unit can analyze the user's past facial photo upload history and select the optimal upload method. The reception unit, for example, uses data mining technology to analyze the user's past facial photo upload history. The reception unit can also suggest the optimal upload method based on the user's past facial photo upload history. For example, the reception unit can preferentially suggest an upload method that the user has frequently used in the past (e.g., uploading from a smartphone). The reception unit can also analyze the time period during which the user previously uploaded photos and send a notification prompting the user to upload during that time period. For example, the reception unit can analyze the resolution of photos the user previously uploaded and suggest uploading at the optimal resolution. This makes it possible to suggest the optimal upload method based on the user's past history.

[0079] The reception unit can perform filtering based on the user's current environment and situation when uploading a facial photo. The reception unit, for example, uses sensor technology to detect the user's current environment. The reception unit can also use location information services to detect the user's current situation. For example, when the user is outdoors, the reception unit applies a filter that removes background noise. When the user is indoors, the reception unit can also apply a filter that optimizes lighting conditions. For example, when the user is moving, the reception unit applies a filter that performs image stabilization. This allows optimal filtering to be performed according to the user's environment and situation.

[0080] The reception unit can select the optimal upload means depending on the user's input method when uploading a facial photo. The reception unit, for example, uses interface technology to detect the user's input method. The reception unit can also have a guide function to provide the optimal upload means depending on the user's input method. For example, if the user selects voice input, the reception unit provides a means for uploading a facial photo according to a voice guide. Also, if the user selects text input, the reception unit can provide a means for uploading a facial photo according to a text guide. For example, if the user selects image input, the reception unit provides a means for uploading a facial photo according to an image guide. This makes it possible to provide the optimal upload means depending on the user's input method.

[0081] The reception unit can estimate the user's emotions and determine the priority of facial photos to be uploaded based on the estimated user's emotions. The reception unit, for example, uses facial expression analysis technology to estimate the user's emotions. The reception unit can also use voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the reception unit can prioritize uploading facial photos taken recently. Also, if the user is feeling stressed, the reception unit can prioritize uploading facial photos taken in the past. For example, if the user is excited, the reception unit can prioritize uploading facial photos taken at a specific event. This makes it possible to prioritize uploading the most appropriate facial photos according to the user's emotions.

[0082] When uploading a facial photo, the reception unit can prioritize uploading highly relevant photos taking into account the user's geographical location information. The reception unit, for example, uses GPS technology to acquire the user's geographical location information. The reception unit can also refer to a database to select highly relevant photos based on the user's geographical location information. For example, if the user is in a specific city, the reception unit can prioritize uploading facial photos taken in that city. Also, if the user is traveling, the reception unit can prioritize uploading facial photos taken at the user's travel destination. For example, if the user is at home, the reception unit can prioritize uploading facial photos taken at home. This makes it possible to upload optimal photos based on the user's geographical location information.

[0083] When uploading a facial photo, the reception unit can analyze the user's social media activity and upload related photos. The reception unit, for example, uses data mining technology to analyze the user's social media activity. The reception unit can also use a social media API to select related photos based on the user's social media activity. For example, the reception unit prioritizes uploading facial photos taken at locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and upload related facial photos. For example, the reception unit refers to the activity of the user's friends on social media and uploads related facial photos. This makes it possible to upload the most appropriate photo based on the user's social media activity.

[0084] The reception unit can customize the upload method by reflecting the user's past feedback when uploading a facial photo. The reception unit, for example, uses a questionnaire function to collect the user's past feedback. The reception unit can also use data analysis technology to customize the upload method based on the user's past feedback. For example, the reception unit preferentially suggests upload methods that the user has previously preferred. The reception unit can also suggest upload methods that the user has previously dissatisfied with, so as to avoid them. For example, the reception unit suggests the optimal upload method based on the user's past feedback. This makes it possible to provide the optimal upload method based on the user's past feedback.

[0085] The generation unit can estimate the user's emotions and adjust the expression method of the AI ​​avatar based on the estimated user's emotions. The generation unit, for example, uses facial expression analysis technology to estimate the user's emotions. The generation unit can also use voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the generation unit can generate an AI avatar with a calm expression. Also, if the user is excited, the generation unit can generate an AI avatar with a lively expression. For example, if the user is sad, the generation unit can generate an AI avatar with a gentle expression. This makes it possible to generate an AI avatar with the optimal expression method according to the user's emotions.

[0086] When generating an AI avatar, the generation unit can adjust the level of detail of the generation based on the importance of the face. For example, the generation unit uses the number of feature points as a criterion to evaluate the importance of the face. The generation unit can also adjust the parameters of the generation AI to adjust the level of detail of the generation based on the importance of the face. For example, if the contours of the user's face are important, the generation unit can increase the detail of the contours. Also, if the user's eyes are distinctive, the generation unit can increase the detail of the eyes. For example, if the user's mouth is distinctive, the generation unit can increase the detail of the mouth. This makes it possible to generate an AI avatar with the optimal level of detail depending on the importance of the face.

[0087] When generating an AI avatar, the generation unit can apply different generation algorithms depending on the face category. For example, the generation unit uses clustering technology to classify face categories. The generation unit can also switch the generation AI model to apply different generation algorithms depending on the face category. For example, if the user's face is round, the generation unit applies a generation algorithm suitable for round faces. Also, if the user's face is elongated, the generation unit can apply a generation algorithm suitable for elongated faces. For example, if the user's face is square, the generation unit applies a generation algorithm suitable for square faces. This makes it possible to apply the optimal generation algorithm depending on the face category.

[0088] When generating an AI avatar, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit uses data mining technology to analyze the user's past generation results. The generation unit can also update the generation AI model to improve the accuracy of generation based on the user's past generation results. For example, the generation unit improves the accuracy of generation based on data of AI avatars generated by the user in the past. The generation unit can also generate an AI avatar that reflects the user's preferred characteristics based on the user's past generation results. For example, the generation unit analyzes the user's past generation results and applies an optimal generation algorithm. This allows the accuracy of generation to be improved based on the user's past generation results.

[0089] The generation unit can estimate the user's emotions and adjust the length of the AI ​​avatar based on the estimated user's emotions. The generation unit, for example, uses facial expression analysis technology to estimate the user's emotions. The generation unit can also use voice analysis technology to estimate the user's emotions. For example, the generation unit generates a longer AI avatar when the user is relaxed. The generation unit can also generate a shorter AI avatar when the user is in a hurry. For example, the generation unit generates a standard-length AI avatar when the user is excited. This makes it possible to generate an AI avatar with the optimal length depending on the user's emotions.

[0090] When generating an AI avatar, the generation unit can determine the generation priority based on the time when the facial photo was submitted. For example, the generation unit uses timestamp technology to record the time when the facial photo was submitted. The generation unit can also use a scheduling algorithm to determine the generation priority based on the time when the facial photo was submitted. For example, the generation unit can generate an AI avatar by preferentially using a facial photo that was recently submitted. The generation unit can also generate an AI avatar by preferentially using a facial photo related to a specific event. For example, the generation unit can generate an AI avatar by preferentially using a facial photo specified by a user. This allows AI avatars to be generated with optimal priority based on the time when the facial photo was submitted.

[0091] When generating AI avatars, the generation unit can adjust the order of generation based on the relevance of the facial photographs. For example, the generation unit uses content similarity as a criterion to evaluate the relevance of the facial photographs. The generation unit can also adjust parameters of the generation AI to adjust the order of generation based on the relevance of the facial photographs. For example, if the user's facial photograph is related to a specific location, the generation unit can prioritize generating an AI avatar related to that location. Also, if the user's facial photograph is related to a specific event, the generation unit can prioritize generating an AI avatar related to that event. For example, if the user's facial photograph is related to a specific theme, the generation unit can prioritize generating an AI avatar related to that theme. This allows AI avatars to be generated in an optimal order based on the relevance of the facial photographs.

[0092] When generating an AI avatar, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit uses qualifications or years of experience as criteria to evaluate the user's level of expertise. The generation unit can also adjust parameters of the generation AI to adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate an AI avatar that uses a lot of technical terminology. If the user is a beginner, the generation unit can also generate an AI avatar that avoids technical terminology. For example, the generation unit generates an AI avatar that uses appropriate technical terminology according to the user's level of expertise. This makes it possible to generate an AI avatar that uses optimal technical terminology according to the user's level of expertise.

[0093] The journey setting unit can estimate the user's emotions and adjust the journey setting criteria based on the estimated user's emotions. The journey setting unit, for example, uses facial expression analysis technology to estimate the user's emotions. The journey setting unit can also use voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the journey setting unit can set a journey that includes many tranquil landscapes. Also, if the user is excited, the journey setting unit can set a journey that includes many active places. For example, if the user is sad, the journey setting unit can set a journey that includes many soothing landscapes. This makes it possible to provide optimal journey setting criteria according to the user's emotions.

[0094] When setting a trip, the trip setting unit can improve the accuracy of the setting by taking into account the interrelationships between the scenery and famous places of each location. For example, the trip setting unit uses geographical proximity as a criterion to evaluate the interrelationships between the scenery and famous places of each location. The trip setting unit can also refer to a database to improve the accuracy of the setting by taking into account the interrelationships between the scenery and famous places of each location. For example, the trip setting unit sets an efficient trip by taking into account the distance between the scenery and famous places of each location. The trip setting unit can also set an optimal trip by taking into account the popularity of the scenery and famous places of each location. For example, the trip setting unit sets an optimal trip by taking into account the season of the scenery and famous places of each location. This makes it possible to set an optimal trip by taking into account the interrelationships between the scenery and famous places of each location.

[0095] The trip setting unit can set the trip taking into consideration the user's attribute information. The trip setting unit, for example, uses a questionnaire function to acquire the user's attribute information. The trip setting unit can also use data analysis technology to set the optimal trip based on the user's attribute information. For example, the trip setting unit can set an appropriate trip taking into consideration the user's age. The trip setting unit can also set an appropriate trip taking into consideration the user's gender. For example, the trip setting unit can set an appropriate trip taking into consideration the user's hobbies. This makes it possible to set the optimal trip based on the user's attribute information.

[0096] When setting a trip, the trip setting unit can weight the settings based on the user's submission frequency. For example, the trip setting unit uses the number of submissions as a criterion to evaluate the user's submission frequency. The trip setting unit can also use a scheduling algorithm to weight the settings based on the user's submission frequency. For example, if the user submits frequently, the trip setting unit adjusts the trip settings according to the submission frequency. Also, if the user submits infrequently, the trip setting unit can set a special trip. For example, the trip setting unit determines the priority of the trip settings based on the user's submission frequency. This makes it possible to set optimal trip settings based on the user's submission frequency.

[0097] The travel setting unit can estimate the user's emotions and adjust the order in which the travel setting results are displayed based on the estimated user's emotions. The travel setting unit can, for example, use facial expression analysis technology to estimate the user's emotions. The travel setting unit can also use voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the travel setting unit can first display photos of calm scenery. Also, if the user is excited, the travel setting unit can first display photos of active locations. For example, if the user is sad, the travel setting unit can first display photos of soothing scenery. This allows the travel setting results to be displayed in an optimal order depending on the user's emotions.

[0098] When setting a trip, the trip setting unit can take into account the geographic distribution of each location. For example, the trip setting unit uses a geographic information system (GIS) to evaluate the geographic distribution of each location. The trip setting unit can also refer to a geographic database to set a trip taking into account the geographic distribution of each location. For example, the trip setting unit takes into account the geographic distribution of each location to set an efficient trip. The trip setting unit can also take into account the geographic distribution of each location to set an optimal route. For example, the trip setting unit takes into account the geographic distribution of each location to set a trip according to the season. This makes it possible to set an optimal trip based on the geographic distribution of each location.

[0099] When setting a trip, the trip setting unit can refer to related literature to improve the accuracy of the setting. For example, the trip setting unit uses an academic paper database to refer to related literature. The trip setting unit can also use data analysis technology to improve the accuracy of the setting based on the related literature. For example, the trip setting unit can refer to a travel guidebook to set an optimal trip. The trip setting unit can also refer to a travel blog to set a trip that matches the user's interests. For example, the trip setting unit can refer to a travel review site to set a trip that includes highly rated places. This makes it possible to set an optimal trip by referring to related literature.

[0100] When setting a trip, the trip setting unit can take into account the market value of each location. For example, the trip setting unit uses the popularity of tourist destinations as a criterion to evaluate the market value of each location. The trip setting unit can also refer to an economic database to set a trip that takes into account the market value of each location. For example, the trip setting unit takes into account the market value of each location to set a trip that is cost-effective. The trip setting unit can also take into account the market value of each location to set a trip that includes popular places. For example, the trip setting unit takes into account the market value of each location to set a trip that suits the budget. This makes it possible to set an optimal trip based on the market value of each location.

[0101] The providing unit can estimate the user's emotions and determine the priority of photos to be provided based on the estimated user's emotions. The providing unit, for example, uses facial expression analysis technology to estimate the user's emotions. The providing unit can also use voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the providing unit can preferentially provide photos of calm scenery. Also, if the user is excited, the providing unit can preferentially provide photos of active places. For example, if the user is sad, the providing unit can preferentially provide photos of soothing scenery. This makes it possible to preferentially provide optimal photos according to the user's emotions.

[0102] When providing photos, the providing unit can improve the accuracy of the provision by taking into account the interrelationships between locations. For example, the providing unit uses geographical proximity as a criterion to evaluate the interrelationships between locations. The providing unit can also refer to a database to improve the accuracy of the provision by taking into account the interrelationships between locations. For example, the providing unit provides efficient photos by taking into account the distance between scenery and famous places in each location. The providing unit can also provide optimal photos by taking into account the popularity of scenery and famous places in each location. For example, the providing unit provides optimal photos by taking into account the season of scenery and famous places in each location. This makes it possible to provide optimal photos based on the interrelationships between locations.

[0103] The providing unit can provide photos taking into consideration the user's attribute information. For example, the providing unit uses a questionnaire function to acquire the user's attribute information. The providing unit can also use data analysis technology to provide optimal photos based on the user's attribute information. For example, the providing unit can provide appropriate photos taking into consideration the user's age. The providing unit can also provide appropriate photos taking into consideration the user's gender. For example, the providing unit can provide appropriate photos taking into consideration the user's hobbies. This makes it possible to provide optimal photos based on the user's attribute information.

[0104] The providing unit can weight the provision of photos based on the user's submission frequency when providing photos. For example, the providing unit uses the number of submissions as a criterion to evaluate the user's submission frequency. The providing unit can also use a scheduling algorithm to weight the provision of photos based on the user's submission frequency. For example, if a user frequently submits photos, the providing unit adjusts the photo provision according to the submission frequency. The providing unit can also provide special photos if a user rarely submits photos. For example, the providing unit determines the priority of photo provision based on the user's submission frequency. This allows optimal photo provision to be provided based on the user's submission frequency.

[0105] The providing unit can estimate the user's emotion and adjust the display method of the photo to be provided based on the estimated user's emotion. The providing unit, for example, uses facial expression analysis technology to estimate the user's emotion. The providing unit can also use voice analysis technology to estimate the user's emotion. For example, if the user is relaxed, the providing unit can provide the photo in a calm display method. Also, if the user is excited, the providing unit can provide the photo in a dynamic display method. For example, if the user is sad, the providing unit can provide the photo in a gentle display method. This makes it possible to provide the photo in an optimal display method according to the user's emotion.

[0106] The providing unit can provide photos taking into account the geographic distribution of each location. For example, the providing unit uses a geographic information system (GIS) to evaluate the geographic distribution of each location. The providing unit can also refer to a geographic database to provide photos taking into account the geographic distribution of each location. For example, the providing unit provides photos efficiently taking into account the geographic distribution of each location. The providing unit can also set an optimal route taking into account the geographic distribution of each location. For example, the providing unit provides photos according to the season taking into account the geographic distribution of each location. This allows optimal photo provision based on the geographic distribution of each location.

[0107] When providing photos, the providing unit can improve the accuracy of the provided photos by referring to related literature. For example, the providing unit uses an academic paper database to refer to related literature. The providing unit can also use data analysis technology to improve the accuracy of the provided photos based on the related literature. For example, the providing unit can provide optimal photos by referring to a travel guidebook. The providing unit can also provide photos that match the user's interests by referring to a travel blog. For example, the providing unit can provide photos of highly rated places by referring to a travel review site. This allows optimal photo provision by referring to related literature.

[0108] When providing photos, the providing unit can provide them taking into account the market value of each location. For example, the providing unit uses the popularity of tourist destinations as a criterion to evaluate the market value of each location. The providing unit can also refer to an economic database to provide photos taking into account the market value of each location. For example, the providing unit provides photos with high cost performance taking into account the market value of each location. The providing unit can also provide photos of popular places taking into account the market value of each location. For example, the providing unit provides photos according to the budget taking into account the market value of each location. This allows the optimal photo to be provided based on the market value of each location. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, journey setting 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 reception device 38 of the smart device 14 and receives input of a facial photo from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the facial photo using a generation AI to generate an AI avatar. The journey setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets up the generated AI avatar to virtually travel around the world. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated photo to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, journey setting 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 microphone 238 of the smart glasses 214 and receives input of a facial photo from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the facial photo using a generation AI to generate an AI avatar. The journey setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets up the generated AI avatar to virtually travel around the world. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated photo to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, journey setting 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 microphone 238 of the headset-type terminal 314 and receives input of a facial photo from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the facial photo using a generation AI to generate an AI avatar. The journey setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets up the generated AI avatar to virtually travel around the world. The provision unit is realized by the display 343 of the headset-type terminal 314 and provides the generated photo to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, journey setting 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 microphone 238 of the robot 414 and receives input of a facial photograph from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the facial photograph using a generation AI to generate an AI avatar. The journey setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets up the generated AI avatar to virtually travel around the world. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated photograph to the user.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When uploading a user's facial photo, the reception unit can analyze the user's past upload history and suggest the optimal upload method. For example, if the user has frequently uploaded from a smartphone in the past, the reception unit can preferentially suggest uploading from a smartphone. Also, if the user has uploaded during a specific time period in the past, the reception unit can send a notification encouraging the user to upload during that time period. Furthermore, if the user has uploaded high-resolution photos in the past, the reception unit can suggest uploading at a similar resolution. This makes it possible to provide the optimal upload method based on the user's past history.

[0111] When generating an AI avatar, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation accuracy can be improved based on data from AI avatars the user has generated in the past. It can also generate an AI avatar that reflects the user's preferred features based on the user's past generation results. It can also analyze the user's past generation results and apply the optimal generation algorithm. This allows the generation accuracy to be improved based on the user's past generation results.

[0112] The trip setting unit can estimate the user's emotions and adjust the trip setting criteria based on the estimated user's emotions. For example, if the user is relaxed, the trip setting can be set to include many calming landscapes. If the user is excited, the trip setting can be set to include many active places. Furthermore, if the user is sad, the trip setting can be set to include many soothing landscapes. This makes it possible to provide optimal trip setting criteria according to the user's emotions.

[0113] The providing unit can estimate the user's emotions and determine the priority of photos to be provided based on the estimated user's emotions. For example, if the user is relaxed, photos of calm scenery can be provided preferentially. Also, if the user is excited, photos of active places can be provided preferentially. Furthermore, if the user is sad, photos of soothing scenery can be provided preferentially. In this way, it is possible to provide the most suitable photos preferentially according to the user's emotions.

[0114] The providing unit can provide photos taking into consideration the user's attribute information. For example, the providing unit can provide appropriate photos taking into consideration the user's age. The providing unit can also provide appropriate photos taking into consideration the user's gender. Furthermore, the providing unit can also provide appropriate photos taking into consideration the user's hobbies. This makes it possible to provide optimal photos based on the user's attribute information.

[0115] The reception unit can estimate the user's emotions and adjust the timing of uploading a facial photo based on the estimated user emotions. For example, if the user is relaxed, the reception unit can send a notification urging the user to upload a facial photo. Also, if the user is feeling stressed, the reception unit can suggest that the user postpone uploading a facial photo. Furthermore, if the user is excited, the reception unit can send a notification urging the user to upload a facial photo immediately. In this way, the reception unit can prompt the user to upload a facial photo at the optimal timing according to the user's emotions.

[0116] When generating an AI avatar, the generation unit can apply different generation algorithms depending on the face category. For example, if the user has a round face, a generation algorithm suitable for round faces can be applied. If the user has a long and thin face, a generation algorithm suitable for long and thin faces can be applied. Furthermore, if the user has a square face, a generation algorithm suitable for square faces can be applied. This makes it possible to apply the optimal generation algorithm depending on the face category.

[0117] When setting a trip, the trip setting unit can improve the accuracy of the setting by taking into account the interrelationships between the scenery and famous places of each location. For example, an efficient trip is set by taking into account the distance between the scenery and famous places of each location. Also, an optimal trip can be set by taking into account the popularity of the scenery and famous places of each location. Furthermore, an optimal trip can be set by taking into account the season of the scenery and famous places of each location. In this way, an optimal trip can be set by taking into account the interrelationships between the scenery and famous places of each location.

[0118] The providing unit can estimate the user's emotion and adjust the display method of the photos to be provided based on the estimated user's emotion. For example, if the user is relaxed, the photos can be provided in a calm display method. If the user is excited, the photos can be provided in a dynamic display method. Furthermore, if the user is sad, the photos can be provided in a gentle display method. In this way, the photos can be provided in the optimal display method according to the user's emotion.

[0119] When generating an AI avatar, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate an AI avatar that uses a lot of technical terminology. On the other hand, if the user is a beginner, the generation unit can generate an AI avatar that avoids technical terminology. Furthermore, the generation unit can generate an AI avatar that uses appropriate technical terminology according to the user's level of expertise. This makes it possible to generate an AI avatar that uses the most appropriate technical terminology according to the user's level of expertise.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit receives input of a facial photo from the user. Input of a facial photo from the user includes, for example, uploading a facial photo taken with a smartphone. The reception unit may also include a process for obtaining consent from the user. For example, the process may include a process in which the user signs a consent form before uploading the facial photo. Step 2: The generation unit uses the generation AI to analyze the facial photo received by the reception unit and generate an AI avatar. The generation AI analyzes facial features such as the contours of the face and the eyes, nose, and mouth, and generates an AI avatar based on that. For example, if the contours of the face are round, the generation AI generates an AI avatar with a similarly round contour. The generation unit can also use the generation AI to estimate the user's emotions and adjust the way the AI ​​avatar expresses itself based on the estimated emotions. For example, if the user is relaxed, the generation AI generates an AI avatar with a calm expression. Step 3: The travel setting unit sets up the AI ​​avatar generated by the generation unit to virtually travel around the world. Based on data on the scenery and landmarks of each location, the travel setting unit generates photos that make the AI ​​avatar appear to be in that location. For example, the travel setting unit generates a photo of the AI ​​avatar standing in front of the Eiffel Tower in Paris. The travel setting unit can also estimate the user's emotions and adjust the travel setting criteria based on the estimated emotions. For example, if the user is relaxed, the travel setting unit sets up a trip that includes many tranquil landscapes. Step 4: The providing unit provides the photos generated by the journey setting unit to the user. The providing unit transmits the generated photos to the user in real time. For example, the providing unit provides the photos through a dedicated app. The providing unit can also transmit the photos periodically by email. For example, the providing unit transmits the photos to the user every week.

[0122] 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.

[0123] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an input of a face photo from a user; a generation unit that analyzes the facial photo received by the reception unit and generates an AI avatar; A travel setting section that describes specific technical means for setting the AI ​​avatar generated by the generation section to virtually travel around the world; a providing unit that provides the user with the photos generated by the travel setting unit. A system characterized by:

2. The reception unit Include a process for obtaining user consent 2. The system of claim 1.

3. The generation unit Analyzes facial contours, eyes, nose, and mouth features, and generates an AI avatar based on that.

2. The system of claim 1.

4. The journey setting unit Based on data on landscapes or famous places in various locations, an AI avatar generates photos that make it appear as if it is in that location.

2. The system of claim 1.

5. The providing unit Send generated photos to users in real time 2. The system of claim 1.

6. The providing unit Provide photos through a dedicated app 2. The system of claim 1.

7. The providing unit Send photos periodically via email 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the timing of uploading facial photos based on the estimated user emotions by describing specific technical measures.

2. The system of claim 1.

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