system

Abstract

The system according to the embodiment aims to enable the user to experience themselves as the protagonist of a story. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a synthesis unit, and a scene generation unit. The reception unit uploads a facial photo of a user. The analysis unit analyzes the facial photo uploaded by the reception unit. The synthesis unit synthesizes the facial photo analyzed by the analysis unit with the face of a protagonist in a story. The scene generation unit generates a scene for the story based on the face synthesized by the synthesis unit.

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 technologies have had the problem of making it difficult for users to experience themselves as the protagonist of a story.

[0005] The system according to the embodiment aims to enable the user to experience themselves as the protagonist of a story. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a synthesis unit, and a scene generation unit. The reception unit uploads a facial photograph of a user. The analysis unit analyzes the facial photograph uploaded by the reception unit. The synthesis unit synthesizes the facial photograph analyzed by the analysis unit with the face of a protagonist of a story. The scene generation unit generates a scene for the story based on the face synthesized by the synthesis unit. [Effects of the Invention]

[0007] The system according to the embodiment can allow the user to experience themselves as the protagonist of the story. [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 video generation system according to an embodiment of the present invention allows a user to upload a photo of their face, and uses a generation AI to create a video based on a story or novel written by a screenwriter, in which the user plays the role of the protagonist of the story. In this video generation system, the user uploads a photo of their face, and the generation AI analyzes the photo and composites it onto the face of the story's protagonist. The generation AI then generates a video in which the user plays the protagonist based on the content of the story or novel written by the screenwriter. Scenes in this video change as the story progresses, allowing the user to play various roles in the story. For example, a user takes a photo of their face and uploads it to the service. The generation AI analyzes the user's facial features and composites it onto the face of the story's protagonist. The generation AI generates scenes as the story progresses, creating a video in which the user plays various roles in the story. For example, the system generates a video in which the user plays the role of the protagonist in an adventure story, including scenes in which the user goes on various adventures. This system allows users to enjoy the experience of becoming the protagonist of a story. By watching a video in which their own face appears as the protagonist, users can experience the feeling of being immersed in the story. For example, a user can watch a video of an adventure story in which their own face appears, and enjoy the experience of going on the adventure themselves. This allows the video generation system to synthesize a photo of the user's face with the main character of the story and generate a video that matches the story.

[0029] The video generation system according to the embodiment includes a reception unit, an analysis unit, a synthesis unit, and a scene generation unit. The reception unit uploads a facial photo of a user. For example, the resolution and file format of the user's facial photo are specified, but are not limited to these examples. The reception unit allows a user to upload their facial photo to the service using, for example, a smartphone or a PC. The analysis unit analyzes the uploaded facial photo. The analysis unit analyzes the user's facial features using, for example, a face recognition algorithm. The analysis unit can also extract facial features using a feature extraction method. For example, the analysis unit analyzes the facial contours and the positions of the eyes, nose, and mouth to extract features. The synthesis unit combines the facial photo analyzed by the analysis unit with the face of a protagonist of a story. For example, the synthesis unit combines the user's facial photo with the face of the protagonist of the story using image processing technology. The synthesis unit can also combine faces using a synthesis algorithm. For example, the synthesis unit uses deep learning technology to naturally combine the user's facial photo with the face of the protagonist of the story. The scene generation unit generates story scenes based on the faces synthesized by the synthesis unit. The scene generation unit, for example, analyzes scenario data and generates scenes in accordance with the progress of the story. The scene generation unit can also generate scenes based on scene components. For example, the scene generation unit generates a scene in which the user is the protagonist of an adventure story based on the story scenario. In this way, the video generation system according to the embodiment can synthesize a facial photo of the user with the protagonist of the story and generate a video that follows the story.

[0030] The reception unit can specify the resolution and file format of the user's facial photo. Examples of resolution include, but are not limited to, the number of pixels and DPI (Dots Per Inch). Examples of file formats include, but are not limited to, JPEG, PNG, and GIF. The reception unit can specify, for example, the resolution of a facial photo taken by the user with a smartphone. The reception unit can also specify the file format of a facial photo saved by the user on a computer. For example, the reception unit can specify that a facial photo in JPEG format be uploaded. This allows the user to upload an optimal facial photo. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the resolution and file format of the facial photo uploaded by the user into the generation AI and have the generation AI select the optimal resolution and file format.

[0031] The scene generation unit can analyze scenario data of a story created by a screenwriter and generate scenes in accordance with the progress of the story. Scenario data includes, but is not limited to, text format and XML format, for example. The scene generation unit can analyze, for example, text-format scenario data and generate scenes in accordance with the progress of the story. The scene generation unit can also analyze XML-format scenario data and generate scenes. For example, the scene generation unit generates scenes in which the user becomes the protagonist of an adventure story based on the story scenario. This allows the consistency of the story to be maintained by generating scenes in accordance with the progress of the story. Some or all of the above-mentioned processing in the scene generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the scene generation unit can input scenario data to a generation AI and cause the generation AI to generate scenes.

[0032] The synthesis unit can synthesize a facial photograph of the user onto the face of the protagonist of the story. The face of the protagonist of the story includes, but is not limited to, facial shape and facial expression, for example. The synthesis unit, for example, uses image processing technology to synthesize the facial photograph of the user onto the face of the protagonist of the story. The synthesis unit can also perform facial synthesis using a synthesis algorithm. For example, the synthesis unit uses deep learning technology to naturally synthesize the facial photograph of the user onto the face of the protagonist of the story. This allows the user to experience becoming the protagonist of the story by synthesizing the facial photograph of the user onto the face of the protagonist of the story. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the synthesis unit can input a facial photograph of the user into the generation AI and cause the generation AI to execute a process of synthesizing the facial photograph onto the face of the protagonist of the story.

[0033] The scene generation unit can generate a scene in which the user is the protagonist of an adventure story. The adventure story includes, for example, a scenario genre and major events, but is not limited to these examples. The scene generation unit can, for example, analyze scenario data of the adventure story and generate a scene in which the user is the protagonist of the adventure story. The scene generation unit can also generate a scene based on a major event of the adventure story. For example, the scene generation unit generates a scene in which the user goes on a treasure hunt as the protagonist of the adventure story. This generates a scene in which the user is the protagonist of the adventure story, thereby providing an adventure experience. Some or all of the above-described processing in the scene generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the scene generation unit can input scenario data of the adventure story to the generation AI and cause the generation AI to generate scenes.

[0034] The scene generation unit can generate scenes in which the user plays multiple roles in a story. The multiple roles include, but are not limited to, a protagonist, an antagonist, and a supporting character. For example, the scene generation unit analyzes story scenario data and generates scenes in which the user plays multiple roles in the story. The scene generation unit can also generate scenes in which the user plays various roles as the story progresses. For example, the scene generation unit generates a scene in which the user adventures as the protagonist of the story or a scene in which the user fights as the antagonist. This generates scenes in which the user plays various roles in the story, thereby providing a diverse experience. Some or all of the above-described processing in the scene generation unit may be performed using, or without, a generation AI. For example, the scene generation unit may input story scenario data into the generation AI and cause the generation AI to generate scenes.

[0035] The providing unit can provide the generated video to the user. The generated video may include, but is not limited to, for example, the video resolution, file format, and playback time. The providing unit can provide the generated video to the user in streaming format, for example. The providing unit can also provide the generated video to the user in download format. For example, the providing unit provides the video through a web application or a mobile application so that the user can watch the generated video. By providing the generated video to the user, the user can experience becoming the protagonist of a story. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the generated video to the generation AI and have the generation AI select the optimal presentation method.

[0036] The reception unit can analyze the user's past facial photo upload history and select the optimal upload method. The past facial photo upload history includes, for example, the upload date and time and the device used, but is not limited to these examples. The reception unit, for example, preferentially suggests upload methods (camera, gallery, etc.) that the user has used in the past. The reception unit can also analyze the time periods when the user previously uploaded and encourage the user to upload at the optimal time period. For example, the reception unit can suggest optimal settings based on the resolution and file format used by the user in the past uploads. In this way, the optimal upload method can be suggested by analyzing the user's past upload history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal upload method.

[0037] When uploading a facial photo, the reception unit can automatically select the optimal resolution and file format based on the user's current environment and device. Examples of the current environment and device include, but are not limited to, the type of device being used and the network environment. For example, if the user is using a smartphone, the reception unit automatically selects the optimal resolution and file format. The reception unit can also recommend a high-resolution photo if the user is using a personal computer. For example, if the user is using a low-bandwidth network, the reception unit automatically selects a lightweight file format. This allows the optimal facial photo to be uploaded by automatically selecting the optimal resolution and file format based on the user's environment and device. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's device information into the generation AI and have the generation AI select the optimal resolution and file format.

[0038] When uploading a facial photo, the reception unit can prioritize obtaining a facial photo with high relevance based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and an IP address. For example, if the user is traveling, the reception unit prioritizes obtaining a facial photo taken at the travel destination. Furthermore, if the user is at home, the reception unit can prioritize obtaining a facial photo taken at home. For example, if the user is participating in a specific event, the reception unit prioritizes obtaining a facial photo taken at the event. This allows the optimal facial photo to be uploaded by obtaining a facial photo with high relevance taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location information into the generation AI and have the generation AI select a facial photo with high relevance.

[0039] When a facial photo is uploaded, the reception unit can analyze the user's social media activity and acquire related facial photos. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the reception unit prioritizes acquiring facial photos recently posted by the user on social media. The reception unit can also prioritize acquiring facial photos tagged with a specific event by the user. For example, the reception unit acquires facial photos from albums shared by the user on social media. This makes it possible to acquire related facial photos by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's social media activity data into the generation AI and have the generation AI select related facial photos.

[0040] When analyzing a facial photograph, the analysis unit can analyze the user's facial features in detail and select the optimal compositing method. Facial features include, but are not limited to, the position of the eyes and the shape of the nose. For example, the analysis unit can analyze the user's facial contour in detail and select the optimal compositing method. The analysis unit can also analyze the position of the user's eyes and mouth in detail to achieve natural compositing. For example, the analysis unit can analyze the user's skin color and texture in detail to perform realistic compositing. This allows the optimal compositing method to be selected by analyzing the user's facial features in detail. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's facial feature data into the generation AI and have the generation AI select the optimal compositing method.

[0041] When analyzing a facial photo, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial photo data. Past facial photo data includes, but is not limited to, for example, a storage period and a data format. For example, the analysis unit can refer to the user's past facial photo data to more accurately analyze facial features. The analysis unit can also perform analysis that takes facial changes into account based on the user's past facial photo data. For example, the analysis unit can use the user's past facial photo data to improve the accuracy of the analysis results. By referring to the user's past facial photo data, the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past facial photo data into the generation AI to improve the analysis accuracy of the generation AI.

[0042] When analyzing a facial photograph, the analysis unit can optimize the analysis algorithm based on the user's age and gender. Age and gender include, but are not limited to, user registration information and estimation algorithms. The analysis unit selects an appropriate analysis algorithm based on, for example, the user's age. The analysis unit can also perform analysis that emphasizes specific features based on the user's gender. For example, the analysis unit provides optimal analysis results taking into account the user's age and gender. By optimizing the analysis algorithm based on the user's age and gender, optimal analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's age and gender data into the generation AI and have the generation AI optimize the analysis algorithm.

[0043] When analyzing a facial photograph, the analysis unit can improve analysis accuracy based on the user's facial expression and pose. Facial expressions and poses include, but are not limited to, a smile, a serious expression, and a sideways look. The analysis unit can improve analysis accuracy by taking into account, for example, the user's smile or serious expression. The analysis unit can also improve analysis accuracy by taking into account the user's facial direction and angle. For example, the analysis unit can achieve natural synthesis by taking into account the user's specific pose. This can improve analysis accuracy by taking into account the user's facial expression and pose. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's facial expression and pose data into the generation AI to improve analysis accuracy.

[0044] The synthesis unit can optimize a synthesis algorithm to reflect the user's facial features in detail when synthesizing the face. Examples of synthesis algorithms include, but are not limited to, deep learning and template matching. The synthesis unit, for example, uses a synthesis algorithm that reflects the user's facial contours in detail. The synthesis unit can also use a synthesis algorithm that accurately reflects the position of the user's eyes and mouth. For example, the synthesis unit uses a synthesis algorithm that realistically reflects the user's skin color and texture. This allows the user's facial features to be reflected in detail, resulting in a natural synthesis result. Some or all of the above-described processing in the synthesis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the synthesis unit can input the user's facial feature data into the generation AI and have the generation AI optimize the synthesis algorithm.

[0045] The synthesis unit can improve synthesis accuracy by referencing the user's past facial photo data when synthesizing faces. Synthesis accuracy includes, but is not limited to, pixel-by-pixel matching and visual naturalness. For example, the synthesis unit can reference the user's past facial photo data to synthesize facial features more accurately. The synthesis unit can also perform synthesis that takes facial changes into account based on the user's past facial photo data. For example, the synthesis unit can use the user's past facial photo data to improve the accuracy of the synthesis result. In this way, by referencing the user's past facial photo data, synthesis accuracy can be improved. Some or all of the above-mentioned processing in the synthesis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the synthesis unit can input the user's past facial photo data into the generation AI to improve synthesis accuracy.

[0046] The synthesis unit can optimize the synthesis algorithm based on the user's age and gender when synthesizing faces. Optimization of the synthesis algorithm includes, but is not limited to, parameter adjustment and selection of training data, for example. The synthesis unit selects an appropriate synthesis algorithm based on the user's age, for example. The synthesis unit can also perform synthesis that emphasizes specific features based on the user's gender. For example, the synthesis unit provides optimal synthesis results taking into account the user's age and gender. This allows the synthesis unit to provide optimal synthesis results by optimizing the synthesis algorithm based on the user's age and gender. Some or all of the above-described processing in the synthesis unit may be performed using, or without, a generation AI. For example, the synthesis unit can input the user's age and gender data into the generation AI and have the generation AI optimize the synthesis algorithm.

[0047] The synthesis unit can improve synthesis accuracy by taking into account the user's facial expression and pose when synthesizing the face. Facial expressions and poses include, but are not limited to, smiling, serious, and sideways expressions. The synthesis unit can improve synthesis accuracy by taking into account, for example, the user's smiling or serious expression. The synthesis unit can also improve synthesis accuracy by taking into account the user's facial direction and angle. For example, the synthesis unit can achieve natural synthesis by taking into account the user's specific pose. This can improve synthesis accuracy by taking into account the user's facial expression and pose. Some or all of the above-described processing in the synthesis unit can be performed using, or without, a generation AI. For example, the synthesis unit can input the user's facial expression and pose data into the generation AI and have the generation AI improve synthesis accuracy.

[0048] The scene generation unit can adjust the level of detail of a scene in accordance with the progress of the story when generating a scene. The level of detail of a scene includes, but is not limited to, for example, background depiction and character movement. For example, the scene generation unit generates detailed scenes at the climax of the story. The scene generation unit can also generate concise scenes in the early part of the story. For example, the scene generation unit dynamically adjusts the level of detail of a scene in accordance with the progress of the story. This allows the consistency of the story to be maintained by adjusting the level of detail of a scene in accordance with the progress of the story. Some or all of the above-mentioned processing in the scene generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the scene generation unit can input story scenario data into the generation AI and have the generation AI adjust the level of detail of a scene.

[0049] When generating a scene, the scene generation unit can optimize the generation algorithm by referring to the user's past scene generation history. The scene generation history includes, for example, past scene data and generation date and time, but is not limited to these examples. For example, the scene generation unit references the history of scenes the user has previously generated and prioritizes generating scenes that the user prefers. The scene generation unit can also analyze the user's past scene generation history and select an optimal scene generation algorithm. For example, the scene generation unit adjusts the level of detail and style of a scene based on the user's past scene generation history. In this way, by referring to the user's past scene generation history, the generation algorithm can be optimized and an optimal scene can be generated. Some or all of the above-described processing in the scene generation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the scene generation unit can input the user's past scene generation history data into the generation AI and have the generation AI optimize the generation algorithm.

[0050] When generating a scene, the scene generation unit can apply different generation algorithms depending on the story category. Story categories include, but are not limited to, adventure, romance, mystery, etc. For example, the scene generation unit can apply a dynamic scene generation algorithm to an adventure story. Furthermore, the scene generation unit can also apply a romantic scene generation algorithm to a romance story. For example, the scene generation unit can apply a suspenseful scene generation algorithm to a mystery story. In this way, by applying different generation algorithms depending on the story category, optimal scenes can be generated. Some or all of the above-mentioned processing in the scene generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scene generation unit can input story category data into the generation AI and apply the generation algorithm to the generation AI.

[0051] When generating a scene, the scene generation unit can customize the scene based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in an urban area, the scene generation unit can generate a scene that reflects the urban landscape. Furthermore, if the user is in nature, the scene generation unit can also generate a scene that reflects the natural landscape. For example, the scene generation unit generates a scene that reflects the local landscape and culture based on the user's geographical location information. This allows the scene to be generated by customizing the scene taking the user's geographical location information into consideration. Some or all of the above-described processing in the scene generation unit may be performed using, or without, a generation AI. For example, the scene generation unit can input the user's geographical location information into the generation AI and have the generation AI customize the scene.

[0052] When providing a video, the providing unit can select the optimal delivery method by referring to the user's past viewing history. The viewing history includes, for example, the viewing date and time and the number of views, but is not limited to these examples. The providing unit can select the optimal delivery method, for example, based on the style of videos the user has previously viewed. The providing unit can also analyze the user's past viewing history and prioritize providing videos that the user prefers. For example, the providing unit selects a delivery method based on the user's past viewing history, depending on the viewing time and frequency. This allows the optimal video delivery method to be selected by referring to the user's past viewing history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's viewing history data into the generation AI and have the generation AI select the optimal delivery method.

[0053] When providing a video, the providing unit can automatically select the optimal resolution and file format by taking into account the user's device information. Device information includes, but is not limited to, the device type and OS version. For example, if the user is using a smartphone, the providing unit automatically selects the optimal resolution and file format. The providing unit can also provide high-resolution videos if the user is using a personal computer. For example, if the user is using a low-bandwidth network, the providing unit automatically selects a lightweight file format. This enables optimal video provision by automatically selecting the optimal resolution and file format by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's device information into the generation AI and have the generation AI select the optimal resolution and file format.

[0054] When providing videos, the providing unit can prioritize providing highly relevant videos based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP address. For example, if the user is in a specific area, the providing unit can prioritize providing videos related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing videos related to the user's travel destination. For example, the providing unit can prioritize providing videos related to local culture and scenery based on the user's geographical location information. This enables optimal video provision by providing highly relevant videos taking the user's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and have the generation AI select highly relevant videos.

[0055] When providing videos, the providing unit can analyze the user's social media activity and provide related videos. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the providing unit can provide videos related to content recently posted by the user on social media. The providing unit can also provide videos related to content tagged with a specific event by the user. For example, the providing unit can provide videos related to albums shared by the user on social media. This makes it possible to provide related videos by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and have the generation AI select related videos.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can automatically adjust the facial orientation and facial expression of facial photos uploaded by users using facial recognition technology. For example, if the user is facing at an angle, the reception unit can correct the facial orientation to face forward. Also, if the user is not smiling, the reception unit can change the face to a smiling face. This improves the quality of facial photos uploaded by users and allows for more natural synthesis results. Furthermore, the reception unit can automatically remove the background of facial photos uploaded by users and convert it into a transparent background. This allows the synthesis unit to synthesize the facial photos with the main character of a story in a natural way without being affected by the background. The reception unit can also automatically optimize the resolution of facial photos uploaded by users, allowing the synthesis unit to perform high-quality synthesis.

[0058] When analyzing a user's facial photo, the analysis unit can perform a detailed analysis of not only facial features but also skin texture and color tone. For example, it can analyze the user's skin texture and naturally combine it with the skin of the story's protagonist. It can also analyze the user's facial color tone and adjust it to match the scene in the story. This allows the user's facial photo to blend more naturally into the scene in the story. Furthermore, the analysis unit can automatically detect accessories (glasses, hats, etc.) included in the user's facial photo and provide that information to the synthesis unit as needed. This allows the synthesis unit to perform synthesis taking the accessories into consideration. The analysis unit can also recommend facial expressions and poses that match the scene in the story based on the analysis results of the user's facial photo.

[0059] The synthesis unit can dynamically synthesize the user's facial photograph onto the face of the story protagonist by tracking the user's facial movements and expressions in real time. For example, if the user smiles, that smile can be reflected on the face of the story protagonist. Similarly, if the user shows a surprised expression, that expression can be reflected in the story scene. This allows the user's facial expressions and movements to be reflected in the story scene in real time, providing a more immersive experience. Furthermore, the synthesis unit can optimize the synthesis algorithm using deep learning technology to accurately reflect the skin texture and color tone contained in the user's facial photograph. This allows the user's facial photograph to blend more naturally into the story scene. The synthesis unit can also take into account accessories (e.g., glasses, hats) contained in the user's facial photograph when synthesizing.

[0060] The reception unit can automatically adjust not only facial features but also background and lighting conditions for face photos uploaded by users. For example, if a user uploads a face photo taken in a dark place, the reception unit can automatically adjust the lighting conditions to optimize brightness. Also, if a user uploads a face photo taken against a complex background, the reception unit can automatically blur the background. This improves the quality of face photos uploaded by users and allows more natural synthesis results. Furthermore, the reception unit can automatically optimize the resolution of face photos uploaded by users, enabling the synthesis unit to perform high-quality synthesis. The reception unit can also automatically convert the file format of face photos uploaded by users, allowing the synthesis unit to process them in the optimal format.

[0061] When analyzing a user's facial photo, the analysis unit can analyze not only facial features but also hairstyle and hair color in detail. For example, the analysis unit can analyze the user's hairstyle and naturally combine it with the hairstyle of the protagonist in the story. The analysis unit can also analyze the user's hair color and adjust the color tone to match the scene in the story. This allows the user's facial photo to blend more naturally into the scene in the story. Furthermore, the analysis unit can analyze the skin texture and color tone contained in the user's facial photo in detail and provide that information to the synthesis unit. This allows the synthesis unit to perform synthesis taking into account the skin texture and color tone. The analysis unit can also recommend facial expressions and poses that match the scene in the story based on the analysis results of the user's facial photo.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit uploads a facial photograph of the user. For example, the resolution and file format of the facial photograph of the user are specified, but are not limited to these examples. For example, the reception unit allows the user to upload their facial photograph to the service using a smartphone or a personal computer. Step 2: The analysis unit analyzes the uploaded facial photo. For example, the analysis unit analyzes the user's facial features using a facial recognition algorithm. The analysis unit can also extract facial features using a feature extraction method. For example, the analysis unit analyzes the facial contours and the positions of the eyes, nose, and mouth to extract features. Step 3: The synthesis unit synthesizes the facial photo analyzed by the analysis unit onto the face of the protagonist of the story. The synthesis unit synthesizes the user's facial photo onto the face of the protagonist of the story using, for example, image processing technology. The synthesis unit can also synthesize faces using a synthesis algorithm. For example, the synthesis unit uses deep learning technology to naturally synthesize the user's facial photo onto the face of the protagonist of the story. Step 4: The scene generation unit generates story scenes based on the faces synthesized by the synthesis unit. The scene generation unit, for example, analyzes scenario data and generates scenes in accordance with the progress of the story. The scene generation unit can also generate scenes based on scene components. For example, the scene generation unit generates a scene in which the user is the protagonist of an adventure story based on the story scenario.

[0064] (Example 2) A video generation system according to an embodiment of the present invention allows a user to upload a photo of their face, and uses a generation AI to create a video based on a story or novel written by a screenwriter, in which the user plays the role of the protagonist of the story. In this video generation system, the user uploads a photo of their face, and the generation AI analyzes the photo and composites it onto the face of the story's protagonist. The generation AI then generates a video in which the user plays the protagonist based on the content of the story or novel written by the screenwriter. Scenes in this video change as the story progresses, allowing the user to play various roles in the story. For example, a user takes a photo of their face and uploads it to the service. The generation AI analyzes the user's facial features and composites it onto the face of the story's protagonist. The generation AI generates scenes as the story progresses, creating a video in which the user plays various roles in the story. For example, the system generates a video in which the user plays the role of the protagonist in an adventure story, including scenes in which the user goes on various adventures. This system allows users to enjoy the experience of becoming the protagonist of a story. By watching a video in which their own face appears as the protagonist, users can experience the feeling of being immersed in the story. For example, a user can watch a video of an adventure story in which their own face appears, and enjoy the experience of going on the adventure themselves. This allows the video generation system to synthesize a photo of the user's face with the main character of the story and generate a video that matches the story.

[0065] The video generation system according to the embodiment includes a reception unit, an analysis unit, a synthesis unit, and a scene generation unit. The reception unit uploads a facial photo of a user. For example, the resolution and file format of the user's facial photo are specified, but are not limited to these examples. The reception unit allows a user to upload their facial photo to the service using, for example, a smartphone or a PC. The analysis unit analyzes the uploaded facial photo. The analysis unit analyzes the user's facial features using, for example, a face recognition algorithm. The analysis unit can also extract facial features using a feature extraction method. For example, the analysis unit analyzes the facial contours and the positions of the eyes, nose, and mouth to extract features. The synthesis unit combines the facial photo analyzed by the analysis unit with the face of a protagonist of a story. For example, the synthesis unit combines the user's facial photo with the face of the protagonist of the story using image processing technology. The synthesis unit can also combine faces using a synthesis algorithm. For example, the synthesis unit uses deep learning technology to naturally combine the user's facial photo with the face of the protagonist of the story. The scene generation unit generates story scenes based on the faces synthesized by the synthesis unit. The scene generation unit, for example, analyzes scenario data and generates scenes in accordance with the progress of the story. The scene generation unit can also generate scenes based on scene components. For example, the scene generation unit generates a scene in which the user is the protagonist of an adventure story based on the story scenario. In this way, the video generation system according to the embodiment can synthesize a facial photo of the user with the protagonist of the story and generate a video that follows the story.

[0066] The reception unit can specify the resolution and file format of the user's facial photo. Examples of resolution include, but are not limited to, the number of pixels and DPI (Dots Per Inch). Examples of file formats include, but are not limited to, JPEG, PNG, and GIF. The reception unit can specify, for example, the resolution of a facial photo taken by the user with a smartphone. The reception unit can also specify the file format of a facial photo saved by the user on a computer. For example, the reception unit can specify that a facial photo in JPEG format be uploaded. This allows the user to upload an optimal facial photo. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the resolution and file format of the facial photo uploaded by the user into the generation AI and have the generation AI select the optimal resolution and file format.

[0067] The scene generation unit can analyze scenario data of a story created by a screenwriter and generate scenes in accordance with the progress of the story. Scenario data includes, but is not limited to, text format and XML format, for example. The scene generation unit can analyze, for example, text-format scenario data and generate scenes in accordance with the progress of the story. The scene generation unit can also analyze XML-format scenario data and generate scenes. For example, the scene generation unit generates scenes in which the user becomes the protagonist of an adventure story based on the story scenario. This allows the consistency of the story to be maintained by generating scenes in accordance with the progress of the story. Some or all of the above-mentioned processing in the scene generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the scene generation unit can input scenario data to a generation AI and cause the generation AI to generate scenes.

[0068] The synthesis unit can synthesize a facial photograph of the user onto the face of the protagonist of the story. The face of the protagonist of the story includes, but is not limited to, facial shape and facial expression, for example. The synthesis unit, for example, uses image processing technology to synthesize the facial photograph of the user onto the face of the protagonist of the story. The synthesis unit can also perform facial synthesis using a synthesis algorithm. For example, the synthesis unit uses deep learning technology to naturally synthesize the facial photograph of the user onto the face of the protagonist of the story. This allows the user to experience becoming the protagonist of the story by synthesizing the facial photograph of the user onto the face of the protagonist of the story. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the synthesis unit can input a facial photograph of the user into the generation AI and cause the generation AI to execute a process of synthesizing the facial photograph onto the face of the protagonist of the story.

[0069] The scene generation unit can generate a scene in which the user is the protagonist of an adventure story. The adventure story includes, for example, a scenario genre and major events, but is not limited to these examples. The scene generation unit can, for example, analyze scenario data of the adventure story and generate a scene in which the user is the protagonist of the adventure story. The scene generation unit can also generate a scene based on a major event of the adventure story. For example, the scene generation unit generates a scene in which the user goes on a treasure hunt as the protagonist of the adventure story. This generates a scene in which the user is the protagonist of the adventure story, thereby providing an adventure experience. Some or all of the above-described processing in the scene generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the scene generation unit can input scenario data of the adventure story to the generation AI and cause the generation AI to generate scenes.

[0070] The scene generation unit can generate scenes in which the user plays multiple roles in a story. The multiple roles include, but are not limited to, a protagonist, an antagonist, and a supporting character. For example, the scene generation unit analyzes story scenario data and generates scenes in which the user plays multiple roles in the story. The scene generation unit can also generate scenes in which the user plays various roles as the story progresses. For example, the scene generation unit generates a scene in which the user adventures as the protagonist of the story or a scene in which the user fights as the antagonist. This generates scenes in which the user plays various roles in the story, thereby providing a diverse experience. Some or all of the above-described processing in the scene generation unit may be performed using, or without, a generation AI. For example, the scene generation unit may input story scenario data into the generation AI and cause the generation AI to generate scenes.

[0071] The providing unit can provide the generated video to the user. The generated video may include, but is not limited to, for example, the video resolution, file format, and playback time. The providing unit can provide the generated video to the user in streaming format, for example. The providing unit can also provide the generated video to the user in download format. For example, the providing unit provides the video through a web application or a mobile application so that the user can watch the generated video. By providing the generated video to the user, the user can experience becoming the protagonist of a story. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the generated video to the generation AI and have the generation AI select the optimal presentation method.

[0072] The reception unit can estimate the user's emotions and adjust the timing of uploading a facial photo based on the estimated user emotions. Examples of user emotions include, but are not limited to, relaxation, stress, and excitement. For example, if the user is relaxed, the reception unit sends a notification prompting the user to upload a facial photo. Furthermore, if the user is feeling stressed, the reception unit can suggest postponing the upload. For example, if the user is excited, the reception unit sends a notification prompting the user to upload immediately. This allows the facial photo to be uploaded at the optimal timing by adjusting the timing of uploading the facial photo according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of uploading the facial photo.

[0073] The reception unit can analyze the user's past facial photo upload history and select the optimal upload method. The past facial photo upload history includes, for example, the upload date and time and the device used, but is not limited to these examples. The reception unit, for example, preferentially suggests upload methods (camera, gallery, etc.) that the user has used in the past. The reception unit can also analyze the time periods when the user previously uploaded and encourage the user to upload at the optimal time period. For example, the reception unit can suggest optimal settings based on the resolution and file format used by the user in the past uploads. In this way, the optimal upload method can be suggested by analyzing the user's past upload history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal upload method.

[0074] When uploading a facial photo, the reception unit can automatically select the optimal resolution and file format based on the user's current environment and device. Examples of the current environment and device include, but are not limited to, the type of device being used and the network environment. For example, if the user is using a smartphone, the reception unit automatically selects the optimal resolution and file format. The reception unit can also recommend a high-resolution photo if the user is using a personal computer. For example, if the user is using a low-bandwidth network, the reception unit automatically selects a lightweight file format. This allows the optimal facial photo to be uploaded by automatically selecting the optimal resolution and file format based on the user's environment and device. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's device information into the generation AI and have the generation AI select the optimal resolution and file format.

[0075] The reception unit can estimate the user's emotions and determine the priority of facial photos to be uploaded based on the estimated user emotions. The priority of facial photos can include, but is not limited to, the intensity of the emotion and the frequency of past use. For example, if the user is relaxed, the reception unit can prioritize uploading facial photos taken recently. Furthermore, if the user is 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 allows the optimal facial photos to be uploaded by determining the priority of facial photos according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of facial photos.

[0076] When uploading a facial photo, the reception unit can prioritize obtaining a facial photo with high relevance based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and an IP address. For example, if the user is traveling, the reception unit prioritizes obtaining a facial photo taken at the travel destination. Furthermore, if the user is at home, the reception unit can prioritize obtaining a facial photo taken at home. For example, if the user is participating in a specific event, the reception unit prioritizes obtaining a facial photo taken at the event. This allows the optimal facial photo to be uploaded by obtaining a facial photo with high relevance taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location information into the generation AI and have the generation AI select a facial photo with high relevance.

[0077] When a facial photo is uploaded, the reception unit can analyze the user's social media activity and acquire related facial photos. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the reception unit prioritizes acquiring facial photos recently posted by the user on social media. The reception unit can also prioritize acquiring facial photos tagged with a specific event by the user. For example, the reception unit acquires facial photos from albums shared by the user on social media. This makes it possible to acquire related facial photos by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's social media activity data into the generation AI and have the generation AI select related facial photos.

[0078] The analysis unit can estimate the user's emotions and adjust the facial photo analysis method based on the estimated user emotions. Examples of facial photo analysis methods include, but are not limited to, image processing technology and machine learning algorithms. For example, if the user is relaxed, the analysis unit performs a detailed analysis to analyze facial features in detail. Furthermore, if the user is in a hurry, the analysis unit can perform a simplified analysis to quickly obtain results. For example, if the user is excited, the analysis unit can perform an analysis focusing on specific facial expressions or poses. By adjusting the facial photo analysis method according to the user's emotions, optimal analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generative AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the facial photo analysis method.

[0079] When analyzing a facial photograph, the analysis unit can analyze the user's facial features in detail and select the optimal compositing method. Facial features include, but are not limited to, the position of the eyes and the shape of the nose. For example, the analysis unit can analyze the user's facial contour in detail and select the optimal compositing method. The analysis unit can also analyze the position of the user's eyes and mouth in detail to achieve natural compositing. For example, the analysis unit can analyze the user's skin color and texture in detail to perform realistic compositing. This allows the optimal compositing method to be selected by analyzing the user's facial features in detail. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's facial feature data into the generation AI and have the generation AI select the optimal compositing method.

[0080] When analyzing a facial photo, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial photo data. Past facial photo data includes, but is not limited to, for example, a storage period and a data format. For example, the analysis unit can refer to the user's past facial photo data to more accurately analyze facial features. The analysis unit can also perform analysis that takes facial changes into account based on the user's past facial photo data. For example, the analysis unit can use the user's past facial photo data to improve the accuracy of the analysis results. By referring to the user's past facial photo data, the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past facial photo data into the generation AI to improve the analysis accuracy of the generation AI.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. Examples of display methods for the analysis results include, but are not limited to, graph display and text display. For example, when the user is relaxed, the analysis unit displays detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can display concise analysis results. For example, when the user is excited, the analysis unit displays visually appealing analysis results. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing an optimal display method. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0082] When analyzing a facial photograph, the analysis unit can optimize the analysis algorithm based on the user's age and gender. Age and gender include, but are not limited to, user registration information and estimation algorithms. The analysis unit selects an appropriate analysis algorithm based on, for example, the user's age. The analysis unit can also perform analysis that emphasizes specific features based on the user's gender. For example, the analysis unit provides optimal analysis results taking into account the user's age and gender. By optimizing the analysis algorithm based on the user's age and gender, optimal analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's age and gender data into the generation AI and have the generation AI optimize the analysis algorithm.

[0083] When analyzing a facial photograph, the analysis unit can improve analysis accuracy based on the user's facial expression and pose. Facial expressions and poses include, but are not limited to, a smile, a serious expression, and a sideways look. The analysis unit can improve analysis accuracy by taking into account, for example, the user's smile or serious expression. The analysis unit can also improve analysis accuracy by taking into account the user's facial direction and angle. For example, the analysis unit can achieve natural synthesis by taking into account the user's specific pose. This can improve analysis accuracy by taking into account the user's facial expression and pose. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's facial expression and pose data into the generation AI to improve analysis accuracy.

[0084] The synthesis unit can estimate the user's emotions and adjust the facial synthesis method based on the estimated user's emotions. Examples of facial synthesis methods include, but are not limited to, image processing technology and machine learning algorithms. For example, when the user is relaxed, the synthesis unit performs natural synthesis. Furthermore, when the user is in a hurry, the synthesis unit can also perform rapid synthesis. For example, when the user is excited, the synthesis unit performs synthesis that emphasizes a specific facial expression. By adjusting the facial synthesis method according to the user's emotions, optimal synthesis results can be obtained. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the synthesis unit may be performed using, for example, the generation AI. For example, the synthesis unit can input the user's emotion data into the generation AI and have the generation AI adjust the facial synthesis method.

[0085] The synthesis unit can optimize a synthesis algorithm to reflect the user's facial features in detail when synthesizing the face. Examples of synthesis algorithms include, but are not limited to, deep learning and template matching. The synthesis unit, for example, uses a synthesis algorithm that reflects the user's facial contours in detail. The synthesis unit can also use a synthesis algorithm that accurately reflects the position of the user's eyes and mouth. For example, the synthesis unit uses a synthesis algorithm that realistically reflects the user's skin color and texture. This allows the user's facial features to be reflected in detail, resulting in a natural synthesis result. Some or all of the above-described processing in the synthesis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the synthesis unit can input the user's facial feature data into the generation AI and have the generation AI optimize the synthesis algorithm.

[0086] The synthesis unit can improve synthesis accuracy by referencing the user's past facial photo data when synthesizing faces. Synthesis accuracy includes, but is not limited to, pixel-by-pixel matching and visual naturalness. For example, the synthesis unit can reference the user's past facial photo data to synthesize facial features more accurately. The synthesis unit can also perform synthesis that takes facial changes into account based on the user's past facial photo data. For example, the synthesis unit can use the user's past facial photo data to improve the accuracy of the synthesis result. In this way, by referencing the user's past facial photo data, synthesis accuracy can be improved. Some or all of the above-mentioned processing in the synthesis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the synthesis unit can input the user's past facial photo data into the generation AI to improve synthesis accuracy.

[0087] The synthesis unit can estimate the user's emotion and adjust the display method of the synthesis result based on the estimated user's emotion. The display method of the synthesis result includes, but is not limited to, real-time display and still image display. For example, when the user is relaxed, the synthesis unit displays a detailed synthesis result. Furthermore, when the user is in a hurry, the synthesis unit can display a concise synthesis result. For example, when the user is excited, the synthesis unit displays a visually appealing synthesis result. This allows the display method of the synthesis result to be adjusted according to the user's emotion, thereby providing an optimal display method. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the synthesis unit may be performed using, for example, the generation AI. For example, the synthesis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the synthesis result.

[0088] The synthesis unit can optimize the synthesis algorithm based on the user's age and gender when synthesizing faces. Optimization of the synthesis algorithm includes, but is not limited to, parameter adjustment and selection of training data, for example. The synthesis unit selects an appropriate synthesis algorithm based on the user's age, for example. The synthesis unit can also perform synthesis that emphasizes specific features based on the user's gender. For example, the synthesis unit provides optimal synthesis results taking into account the user's age and gender. This allows the synthesis unit to provide optimal synthesis results by optimizing the synthesis algorithm based on the user's age and gender. Some or all of the above-described processing in the synthesis unit may be performed using, or without, a generation AI. For example, the synthesis unit can input the user's age and gender data into the generation AI and have the generation AI optimize the synthesis algorithm.

[0089] The synthesis unit can improve synthesis accuracy by taking into account the user's facial expression and pose when synthesizing the face. Facial expressions and poses include, but are not limited to, smiling, serious, and sideways expressions. The synthesis unit can improve synthesis accuracy by taking into account, for example, the user's smiling or serious expression. The synthesis unit can also improve synthesis accuracy by taking into account the user's facial direction and angle. For example, the synthesis unit can achieve natural synthesis by taking into account the user's specific pose. This can improve synthesis accuracy by taking into account the user's facial expression and pose. Some or all of the above-described processing in the synthesis unit can be performed using, or without, a generation AI. For example, the synthesis unit can input the user's facial expression and pose data into the generation AI and have the generation AI improve synthesis accuracy.

[0090] The scene generation unit can estimate the user's emotion and adjust the scene generation method based on the estimated user's emotion. Examples of scene generation methods include, but are not limited to, scenario data analysis methods and scene components. For example, the scene generation unit can generate a relaxed scene when the user is relaxed. For example, the scene generation unit can generate a fast-paced scene when the user is in a hurry. For example, the scene generation unit can generate a visually stimulating scene when the user is excited. This allows the scene generation method to be adjusted according to the user's emotion, thereby generating an optimal scene. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scene generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the scene generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the scene generation method.

[0091] The scene generation unit can adjust the level of detail of a scene in accordance with the progress of the story when generating a scene. The level of detail of a scene includes, but is not limited to, for example, background depiction and character movement. For example, the scene generation unit generates detailed scenes at the climax of the story. The scene generation unit can also generate concise scenes in the early part of the story. For example, the scene generation unit dynamically adjusts the level of detail of a scene in accordance with the progress of the story. This allows the consistency of the story to be maintained by adjusting the level of detail of a scene in accordance with the progress of the story. Some or all of the above-mentioned processing in the scene generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the scene generation unit can input story scenario data into the generation AI and have the generation AI adjust the level of detail of a scene.

[0092] When generating a scene, the scene generation unit can optimize the generation algorithm by referring to the user's past scene generation history. The scene generation history includes, for example, past scene data and generation date and time, but is not limited to these examples. For example, the scene generation unit references the history of scenes the user has previously generated and prioritizes generating scenes that the user prefers. The scene generation unit can also analyze the user's past scene generation history and select an optimal scene generation algorithm. For example, the scene generation unit adjusts the level of detail and style of a scene based on the user's past scene generation history. In this way, by referring to the user's past scene generation history, the generation algorithm can be optimized and an optimal scene can be generated. Some or all of the above-described processing in the scene generation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the scene generation unit can input the user's past scene generation history data into the generation AI and have the generation AI optimize the generation algorithm.

[0093] The scene generation unit can estimate the user's emotion and adjust the scene display method based on the estimated user's emotion. Scene display methods include, but are not limited to, real-time display and still image display. For example, when the user is relaxed, the scene generation unit displays a scene with soft colors. Furthermore, when the user is in a hurry, the scene generation unit can display a simple, highly visible scene. For example, when the user is excited, the scene generation unit displays a scene with vivid colors. This allows the scene display method to be adjusted according to the user's emotion, thereby providing an optimal display method. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scene generation unit can be performed using, for example, the generation AI. For example, the scene generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the scene display method.

[0094] When generating a scene, the scene generation unit can apply different generation algorithms depending on the story category. Story categories include, but are not limited to, adventure, romance, mystery, etc. For example, the scene generation unit can apply a dynamic scene generation algorithm to an adventure story. Furthermore, the scene generation unit can also apply a romantic scene generation algorithm to a romance story. For example, the scene generation unit can apply a suspenseful scene generation algorithm to a mystery story. In this way, by applying different generation algorithms depending on the story category, optimal scenes can be generated. Some or all of the above-mentioned processing in the scene generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scene generation unit can input story category data into the generation AI and apply the generation algorithm to the generation AI.

[0095] When generating a scene, the scene generation unit can customize the scene based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in an urban area, the scene generation unit can generate a scene that reflects the urban landscape. Furthermore, if the user is in nature, the scene generation unit can also generate a scene that reflects the natural landscape. For example, the scene generation unit generates a scene that reflects the local landscape and culture based on the user's geographical location information. This allows the scene to be generated by customizing the scene taking the user's geographical location information into consideration. Some or all of the above-described processing in the scene generation unit may be performed using, or without, a generation AI. For example, the scene generation unit can input the user's geographical location information into the generation AI and have the generation AI customize the scene.

[0096] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user emotions. Examples of video presentation methods include, but are not limited to, streaming and downloading. For example, if the user is relaxed, the providing unit can provide the video at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can provide a shortened version of the video. For example, if the user is excited, the providing unit can provide a video with visually stimulating effects. This allows for optimal video presentation by adjusting the video presentation method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the video presentation method.

[0097] When providing a video, the providing unit can select the optimal delivery method by referring to the user's past viewing history. The viewing history includes, for example, the viewing date and time and the number of views, but is not limited to these examples. The providing unit can select the optimal delivery method, for example, based on the style of videos the user has previously viewed. The providing unit can also analyze the user's past viewing history and prioritize providing videos that the user prefers. For example, the providing unit selects a delivery method based on the user's past viewing history, depending on the viewing time and frequency. This allows the optimal video delivery method to be selected by referring to the user's past viewing history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's viewing history data into the generation AI and have the generation AI select the optimal delivery method.

[0098] When providing a video, the providing unit can automatically select the optimal resolution and file format by taking into account the user's device information. Device information includes, but is not limited to, the device type and OS version. For example, if the user is using a smartphone, the providing unit automatically selects the optimal resolution and file format. The providing unit can also provide high-resolution videos if the user is using a personal computer. For example, if the user is using a low-bandwidth network, the providing unit automatically selects a lightweight file format. This enables optimal video provision by automatically selecting the optimal resolution and file format by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's device information into the generation AI and have the generation AI select the optimal resolution and file format.

[0099] The providing unit can estimate the user's emotions and determine the priority of videos to be provided based on the estimated user emotions. Examples of video priorities include, but are not limited to, viewing history and user interests. For example, if the user is relaxed, the providing unit can prioritize providing videos with relaxing content. Furthermore, if the user is in a hurry, the providing unit can prioritize providing videos that can be viewed in a short time. For example, if the user is excited, the providing unit can prioritize providing visually stimulating videos. This enables optimal video provision by determining the priority of videos to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of videos to be provided.

[0100] When providing videos, the providing unit can prioritize providing highly relevant videos based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP address. For example, if the user is in a specific area, the providing unit can prioritize providing videos related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing videos related to the user's travel destination. For example, the providing unit can prioritize providing videos related to local culture and scenery based on the user's geographical location information. This enables optimal video provision by providing highly relevant videos taking the user's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and have the generation AI select highly relevant videos.

[0101] When providing videos, the providing unit can analyze the user's social media activity and provide related videos. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the providing unit can provide videos related to content recently posted by the user on social media. The providing unit can also provide videos related to content tagged with a specific event by the user. For example, the providing unit can provide videos related to albums shared by the user on social media. This makes it possible to provide related videos by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and have the generation AI select related videos. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, synthesis unit, and scene generation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing a user to upload a photo of their face to the service using a smartphone or a personal computer. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features using a face recognition algorithm. The synthesis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and synthesizes the user's facial photo with the face of the protagonist of the story using image processing technology. The scene generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes scenario data and generates scenes in accordance with the progress of the story. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, synthesis unit, and scene generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, allowing a user to upload a photo of their face to the service using a smartphone or a personal computer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features using a face recognition algorithm. The synthesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and synthesizes the user's facial photo with the face of the protagonist of the story using image processing technology. The scene generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes scenario data and generates scenes in accordance with the progress of the story. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, synthesis unit, and scene generation unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314, allowing a user to upload a photo of their face to the service using a smartphone or personal computer. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features using a face recognition algorithm. The synthesis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and synthesizes the user's facial photo with the face of the story's protagonist using image processing technology. The scene generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes scenario data and generates scenes in accordance with the progress of the story. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, synthesis unit, and scene generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to upload a photo of their face to the service using a smartphone or a personal computer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features using a facial recognition algorithm. The synthesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and synthesizes the user's facial photo with the face of the protagonist of the story using image processing technology. The scene generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes scenario data and generates scenes in accordance with the progress of the story.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can automatically adjust the facial orientation and facial expression of facial photos uploaded by users using facial recognition technology. For example, if the user is facing at an angle, the reception unit can correct the facial orientation to face forward. Also, if the user is not smiling, the reception unit can change the face to a smiling face. This improves the quality of facial photos uploaded by users and allows for more natural synthesis results. Furthermore, the reception unit can automatically remove the background of facial photos uploaded by users and convert it into a transparent background. This allows the synthesis unit to synthesize the facial photos with the main character of a story in a natural way without being affected by the background. The reception unit can also automatically optimize the resolution of facial photos uploaded by users, allowing the synthesis unit to perform high-quality synthesis.

[0104] When analyzing a user's facial photo, the analysis unit can perform a detailed analysis of not only facial features but also skin texture and color tone. For example, it can analyze the user's skin texture and naturally combine it with the skin of the story's protagonist. It can also analyze the user's facial color tone and adjust it to match the scene in the story. This allows the user's facial photo to blend more naturally into the scene in the story. Furthermore, the analysis unit can automatically detect accessories (glasses, hats, etc.) included in the user's facial photo and provide that information to the synthesis unit as needed. This allows the synthesis unit to perform synthesis taking the accessories into consideration. The analysis unit can also recommend facial expressions and poses that match the scene in the story based on the analysis results of the user's facial photo.

[0105] When analyzing the story scenario data, the scene generation unit can customize scenes by taking into account the user's preferences and past viewing history. For example, if the user likes adventure stories that they have viewed in the past, the scene generation unit can generate scenes that emphasize adventure elements. Also, if the user likes romance stories, the scene generation unit can generate romantic scenes. This makes it possible to provide scenes that match the user's preferences. Furthermore, when analyzing the story scenario data, the scene generation unit can adjust the tempo and atmosphere of a scene by taking into account the user's current emotional state. For example, if the user is relaxed, the scene generation unit can generate a slow-paced scene, and if the user is excited, the scene generation unit can generate a fast-paced scene. When analyzing the story scenario data, the scene generation unit can also customize scenes by taking into account the user's geographical location information.

[0106] The synthesis unit can dynamically synthesize the user's facial photograph onto the face of the story protagonist by tracking the user's facial movements and expressions in real time. For example, if the user smiles, that smile can be reflected on the face of the story protagonist. Similarly, if the user shows a surprised expression, that expression can be reflected in the story scene. This allows the user's facial expressions and movements to be reflected in the story scene in real time, providing a more immersive experience. Furthermore, the synthesis unit can optimize the synthesis algorithm using deep learning technology to accurately reflect the skin texture and color tone contained in the user's facial photograph. This allows the user's facial photograph to blend more naturally into the story scene. The synthesis unit can also take into account accessories (e.g., glasses, hats) contained in the user's facial photograph when synthesizing.

[0107] When generating a story scene, the scene generation unit can estimate the user's emotion and adjust the atmosphere and color tone of the scene based on the estimated emotion. For example, if the user is relaxed, the scene can be generated with soft color tones. On the other hand, if the user is excited, the scene can be generated with vivid color tones. This makes it possible to provide a scene that matches the user's emotion. Furthermore, when generating a story scene, the scene generation unit can adjust the level of detail and style of the scene by taking into account the user's past viewing history. For example, the scene generation unit can generate a preferred scene based on the style of a scene that the user has viewed in the past. When generating a story scene, the scene generation unit can also customize the scene by taking into account the user's geographical location information. For example, if the user is in an urban area, the scene generation unit can generate a scene that reflects the urban landscape.

[0108] When providing the generated video to the user, the providing unit can estimate the user's emotions and adjust the video providing method based on the estimated emotions. For example, if the user is relaxed, the video can be provided at a leisurely pace. On the other hand, if the user is in a hurry, a shortened version of the video can be provided. This makes it possible to provide the optimal video according to the user's emotions. Furthermore, when providing the generated video to the user, the providing unit can select the optimal providing method by taking into account the user's past viewing history. For example, the providing unit can prioritize providing videos that the user prefers based on the style of videos the user has viewed in the past. When providing the generated video to the user, the providing unit can also automatically select the optimal resolution and file format by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can automatically select the optimal resolution and file format.

[0109] The reception unit can automatically adjust not only facial features but also background and lighting conditions for face photos uploaded by users. For example, if a user uploads a face photo taken in a dark place, the reception unit can automatically adjust the lighting conditions to optimize brightness. Also, if a user uploads a face photo taken against a complex background, the reception unit can automatically blur the background. This improves the quality of face photos uploaded by users and allows more natural synthesis results. Furthermore, the reception unit can automatically optimize the resolution of face photos uploaded by users, enabling the synthesis unit to perform high-quality synthesis. The reception unit can also automatically convert the file format of face photos uploaded by users, allowing the synthesis unit to process them in the optimal format.

[0110] When analyzing a user's facial photo, the analysis unit can analyze not only facial features but also hairstyle and hair color in detail. For example, the analysis unit can analyze the user's hairstyle and naturally combine it with the hairstyle of the protagonist in the story. The analysis unit can also analyze the user's hair color and adjust the color tone to match the scene in the story. This allows the user's facial photo to blend more naturally into the scene in the story. Furthermore, the analysis unit can analyze the skin texture and color tone contained in the user's facial photo in detail and provide that information to the synthesis unit. This allows the synthesis unit to perform synthesis taking into account the skin texture and color tone. The analysis unit can also recommend facial expressions and poses that match the scene in the story based on the analysis results of the user's facial photo.

[0111] When generating a story scene, the scene generation unit can estimate the user's emotions and adjust the tempo and atmosphere of the scene based on the estimated emotions. For example, if the user is relaxed, a slow-paced scene can be generated. On the other hand, if the user is excited, a fast-paced scene can be generated. This makes it possible to provide a scene that matches the user's emotions. Furthermore, when generating a story scene, the scene generation unit can adjust the level of detail and style of the scene by taking into account the user's past viewing history. For example, the scene generation unit can generate a preferred scene based on the style of a scene that the user has viewed in the past. When generating a story scene, the scene generation unit can also customize the scene by taking into account the user's geographical location information. For example, if the user is in an urban area, the scene generation unit can generate a scene that reflects the urban landscape.

[0112] When providing the generated video to the user, the providing unit can estimate the user's emotions and adjust the video providing method based on the estimated emotions. For example, if the user is relaxed, the video can be provided at a leisurely pace. On the other hand, if the user is in a hurry, a shortened version of the video can be provided. This makes it possible to provide the optimal video according to the user's emotions. Furthermore, when providing the generated video to the user, the providing unit can select the optimal providing method by taking into account the user's past viewing history. For example, the providing unit can prioritize providing videos that the user prefers based on the style of videos the user has viewed in the past. When providing the generated video to the user, the providing unit can also automatically select the optimal resolution and file format by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can automatically select the optimal resolution and file format.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit uploads a facial photograph of the user. For example, the resolution and file format of the facial photograph of the user are specified, but are not limited to these examples. For example, the reception unit allows the user to upload their facial photograph to the service using a smartphone or a personal computer. Step 2: The analysis unit analyzes the uploaded facial photo. For example, the analysis unit analyzes the user's facial features using a facial recognition algorithm. The analysis unit can also extract facial features using a feature extraction method. For example, the analysis unit analyzes the facial contours and the positions of the eyes, nose, and mouth to extract features. Step 3: The synthesis unit synthesizes the facial photo analyzed by the analysis unit onto the face of the protagonist of the story. The synthesis unit synthesizes the user's facial photo onto the face of the protagonist of the story using, for example, image processing technology. The synthesis unit can also synthesize faces using a synthesis algorithm. For example, the synthesis unit uses deep learning technology to naturally synthesize the user's facial photo onto the face of the protagonist of the story. Step 4: The scene generation unit generates story scenes based on the faces synthesized by the synthesis unit. The scene generation unit, for example, analyzes scenario data and generates scenes in accordance with the progress of the story. The scene generation unit can also generate scenes based on scene components. For example, the scene generation unit generates a scene in which the user is the protagonist of an adventure story based on the story scenario.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 for uploading a user's face photo; an analysis unit that analyzes the facial photograph uploaded by the reception unit; a synthesis unit that synthesizes the face photograph analyzed by the analysis unit with the face of the protagonist of the story; a scene generation unit that generates a story scene based on the face synthesized by the synthesis unit; Equipped with A system characterized by:

2. The reception unit Specify the resolution or file format of the user's face photo 2. The system of claim 1.

3. The scene generation unit Analyzes the scenario data created by the scriptwriter and generates scenes according to the progression of the story 2. The system of claim 1.

4. The synthesis unit Superimposing a user's face onto the face of the protagonist in a story 2. The system of claim 1.

5. The scene generation unit Generate a scene where the user becomes the protagonist of an adventure story 2. The system of claim 1.

6. The scene generation unit Generate scenes in which users play multiple roles in a story 2. The system of claim 1.

7. A providing unit that provides the generated video to the user 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the timing of uploading face photos based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Analyze the user's past face photo upload history and select the optimal upload method 2. The system of claim 1.

Images