System

Abstract

An object of the system according to the embodiment is to generate and share a moving image based on a profile of a user or a life event.SOLUTION: A system includes a profile input unit, an event input unit, a generation unit, an upload unit, and an evaluation unit. The profile input unit inputs profile information of a user. The event input unit inputs a life event of a user. The generation unit generates a moving image based on the information input by the profile input unit and the event input unit. The upload unit uploads the generated moving image. The evaluation unit receives an evaluation of the uploaded moving image from another user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not make it easy to generate and share videos based on a user's profile or life events, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and share videos based on a user's profile and life events. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile input unit, an event input unit, a generation unit, an upload unit, and an evaluation unit. The profile input unit inputs profile information of a user. The event input unit inputs events in the user's life. The generation unit generates a video based on the information input by the profile input unit and the event input unit. The upload unit uploads the generated video. The evaluation unit receives evaluations of the uploaded video from other users. [Effects of the Invention]

[0007] The system according to the embodiment can generate and share videos based on a user's profile and life events. [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) The video generation system according to an embodiment of the present invention is a system in which a generation AI creates videos based on a user's profile information and life events, and the videos can be uploaded and evaluated. This allows the user to visually express their own life and receive evaluations from other users.

[0029] A video generation system according to an embodiment includes a profile input unit, an event input unit, a generation unit, an upload unit, and an evaluation unit. The profile input unit inputs a user's profile information. For example, information such as name, age, gender, and occupation can be input. The event input unit inputs events in the user's life. For example, events such as marriage, graduation, and job change can be input. The generation unit generates a video based on the information input by the profile input unit and the event input unit. For example, the generation AI analyzes the input information and creates a video according to the user's wishes. The upload unit uploads the generated video. For example, a video created by a user can be uploaded as a private or public video. The evaluation unit receives evaluations from other users for the uploaded video. For example, other users can "like" or comment on the video. As a result, the video generation system according to an embodiment can generate, upload, and evaluate a video based on a user's profile information and life events.

[0030] The generation unit can automatically generate related questions based on the user's input and collect detailed information. For example, in the generation unit, the generation AI automatically generates related questions for an event entered by the user and requests additional information from the user. For example, if the user enters "I was moved at the graduation ceremony," the generation unit will ask, "Which moment was particularly moving?" The generation unit also automatically generates detailed questions for the event entered by the user to collect the background of the event and specific episodes. For example, if the user enters "I was moved at the graduation ceremony," the generation AI will ask, "Who did you share your emotions with?" The generation unit also automatically generates questions in chronological order for the event entered by the user to understand the flow of events in detail. For example, if the user enters "I was moved at the graduation ceremony," the generation AI will ask, "What events happened before and after the graduation ceremony?" This allows detailed information to be collected based on the user's input.

[0031] The generation unit can suggest similar past events based on the user's input and complement important events. For example, in the generation unit, the generation AI suggests similar past events for an event entered by the user, complementing important events that the user may have forgotten. For example, if the user enters "I was moved by my graduation ceremony," the generation AI suggests, "Would you like to add memories of your entrance ceremony as well?" The generation unit also suggests related past events for an event entered by the user, complementing important events that the user may have forgotten. For example, if the user enters "I was moved by my wedding," the generation AI suggests, "Would you like to add memories of the proposal moment as well?" The generation unit also suggests past events in chronological order for an event entered by the user, complementing important events that the user may have forgotten. For example, if the user enters "I was moved by my trip," the generation AI suggests, "Would you like to add memories of the planning stage of the trip as well?" This allows the generation unit to complement important events that the user may have forgotten.

[0032] The generation unit can automatically search for related images and audio data based on the user's input and complete the input. For example, when a user inputs an event, the generation AI automatically searches for related images and completes the input. For example, if the user inputs "I was moved by my graduation ceremony," photos of the graduation ceremony are automatically searched for and added. The generation unit also automatically searches for related audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by my wedding," an audio clip of the wedding is automatically searched for and added. The generation unit also automatically searches for related images and audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by my trip," scenic photos of the travel destination and audio guides are automatically searched for and added. This allows the generation unit to automatically search for related images and audio data to complete the user's input.

[0033] The generation unit can suggest similar events of other users based on the user's input, encouraging empathy. For example, in the generation unit, the generation AI suggests similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my graduation ceremony," it will suggest other users' memories of graduation ceremonies. In addition, in the generation unit, the generation AI suggests similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my wedding," it will suggest other users' memories of weddings. In addition, in the generation unit, the generation AI suggests similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my trip," it will suggest other users' travel memories. In this way, it is possible to suggest similar events of other users and encourage empathy.

[0034] The generation unit can analyze the user's profile information and provide a customized video template based on the user's hobbies and interests. For example, the generation AI in the generation unit analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a travel-themed template is provided for a user whose hobby is traveling. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's interests. For example, a music-themed template is provided for a user who likes music. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a sports-themed template is provided for a user who likes sports. This makes it possible to provide a customized video template based on the user's hobbies and interests.

[0035] The generation unit can analyze the user's input content and automatically generate a storyline that combines past events and future goals. For example, the generation AI in the generation unit analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past graduation ceremony and a future career goal is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past wedding and a future family plan is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past trip and a future travel plan is created. This makes it possible to automatically generate a storyline that combines past events and future goals.

[0036] The generation unit can analyze the user's profile information and customize videos from the perspectives of different cultures and regions. In the generation unit, for example, the generation AI analyzes the user's profile information and customizes videos from the perspectives of different cultures. For example, if the user is from Japan, a video that reflects Japanese culture is created. In addition, the generation unit uses the generation AI to analyze the user's profile information and customize videos from the perspectives of different regions. For example, if the user is from the United States, a video that reflects the regional characteristics of the United States is created. In addition, the generation unit uses the generation AI to analyze the user's profile information and customize videos from the perspectives of different cultures and regions. For example, if the user has a multicultural background, a video that reflects multiple cultures is created. This allows videos to be customized from the perspectives of different cultures and regions.

[0037] The generation unit can analyze the user's input content and automatically generate videos of different genres. For example, the generation AI in the generation unit analyzes the user's input content and automatically generates documentary-style videos. For example, it creates a video that introduces the events of the user's life in chronological order. The generation AI in the generation unit also analyzes the user's input content and automatically generates comedy-style videos. For example, it creates a video that humorously depicts the events of the user's life. The generation AI in the generation unit also analyzes the user's input content and automatically generates drama-style videos. For example, it creates a video that movingly depicts the events of the user's life. This makes it possible to automatically generate videos of different genres.

[0038] The evaluation unit can use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, the evaluation unit uses the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it extracts moving scenes and fun scenes to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it extracts moving moments from a wedding or highlight scenes from a trip to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it extracts moving moments from a graduation ceremony or fun times with family to create a shortened version. This allows important scenes to be automatically highlighted to create a shortened version.

[0039] The evaluation unit uses the generation AI to analyze the user's videos and automatically link related events and people, thereby providing an interactive video experience. For example, the evaluation unit uses the generation AI to analyze the user's videos and automatically link related events and people. For example, videos of friends and family are linked to a graduation ceremony video. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link related events and people. For example, videos of a proposal and honeymoon are linked to a wedding video. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link related events and people. For example, videos of tourist spots and guides are linked to a travel video. This makes it possible to automatically link related events and people, thereby providing an interactive video experience.

[0040] The evaluation unit can use the generation AI to analyze the user's video and convert it into a different format to make it easier to share. For example, the evaluation unit can have the generation AI analyze the user's video and convert it into GIF format to make it easier to share. For example, touching or fun scenes can be converted into GIFs to share on social media. The evaluation unit can also have the generation AI analyze the user's video and convert it into a slideshow format to make it easier to share. For example, travel photos and videos can be converted into slideshows to share with family and friends. The evaluation unit can also have the generation AI analyze the user's video and convert it into a different format to make it easier to share. For example, a wedding video can be converted into a GIF or slideshow to share. This allows the video to be converted into a different format to make it easier to share.

[0041] The evaluation unit can use the generation AI to analyze the user's video and optimize it for different devices. For example, the evaluation unit uses the generation AI to analyze the user's video and optimize it for smartphones. For example, it converts the video into a portrait video format to make it easier to watch on a smartphone. The evaluation unit also uses the generation AI to analyze the user's video and optimize it for tablets. For example, it adjusts the video resolution and layout to match the tablet screen size. The evaluation unit also uses the generation AI to analyze the user's video and optimize it for VR headsets. For example, it converts the video into a 360-degree video format to make it easier to watch on a VR headset. This allows it to be optimized for different devices.

[0042] The evaluation unit can use the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends moving videos to a user who likes moving videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends travel videos to a user who likes travel videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends wedding videos to a user who likes wedding videos. This makes it possible to provide a recommendation algorithm based on the interests and concerns of other users.

[0043] The evaluation unit can use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, the evaluation unit uses the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of moving videos" or a "feature of fun videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of wedding videos" or a "feature of travel videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of family memories" or a "feature of memories with friends." This allows the generation AI to automatically pick out highly rated videos to create a feature page.

[0044] The uploading unit uses a generation AI to analyze a user's video and optimize it for different platforms before uploading it. For example, the generation AI in the uploading unit analyzes the user's video and optimizes it for YouTube before uploading it. For example, it converts it into YouTube's recommended format and automatically generates titles and tags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for Instagram before uploading it. For example, it converts it into Instagram's recommended format and automatically generates captions and hashtags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for different platforms before uploading it. For example, it converts it into YouTube and Instagram's recommended formats and automatically generates titles and tags appropriate for each platform. This allows the video to be optimized for different platforms before being uploaded.

[0045] The uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit translates the video into English, French, Chinese, etc., and uploads it. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically generates subtitles and displays them in different languages. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically translates audio and plays it in different languages. This allows the uploading unit to automatically translate the audio into different languages ​​and receive international evaluation.

[0046] The evaluation unit can use the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends moving videos to a user who likes moving videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends travel videos to a user who likes travel videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends wedding videos to a user who likes wedding videos. This makes it possible to provide a recommendation algorithm based on the interests and concerns of other users.

[0047] The evaluation unit can use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, the evaluation unit uses the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of moving videos" or a "feature of fun videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of wedding videos" or a "feature of travel videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of family memories" or a "feature of memories with friends." This allows the generation AI to automatically pick out highly rated videos to create a feature page.

[0048] The uploading unit uses a generation AI to analyze a user's video and optimize it for different platforms before uploading it. For example, the generation AI in the uploading unit analyzes the user's video and optimizes it for YouTube before uploading it. For example, it converts it into YouTube's recommended format and automatically generates titles and tags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for Instagram before uploading it. For example, it converts it into Instagram's recommended format and automatically generates captions and hashtags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for different platforms before uploading it. For example, it converts it into YouTube and Instagram's recommended formats and automatically generates titles and tags appropriate for each platform. This allows the video to be optimized for different platforms before being uploaded.

[0049] The uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit translates the video into English, French, Chinese, etc., and uploads it. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically generates subtitles and displays them in different languages. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically translates audio and plays it in different languages. This allows the uploading unit to automatically translate the audio into different languages ​​and receive international evaluation.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The generation unit can automatically generate related questions based on the user's input to collect detailed information. For example, the generation AI can automatically generate related questions for an event entered by the user and request additional information from the user. For example, if the user enters "I was moved at the graduation ceremony," the AI ​​will ask, "Which moment was particularly moving?" The generation unit also automatically generates detailed questions for the event entered by the user to collect the background of the event and specific episodes. For example, if the user enters "I was moved at the graduation ceremony," the AI ​​will ask, "Who did you share that emotion with?" The generation unit also automatically generates questions in chronological order for the event entered by the user to understand the flow of events in detail. For example, if the user enters "I was moved at the graduation ceremony," the AI ​​will ask, "What events happened before and after the graduation ceremony?" This allows detailed information to be collected based on the user's input.

[0052] The generation unit can suggest similar past events based on the user's input and complement important events. For example, when a user inputs an event, the generation AI can suggest similar past events and complement important events that the user may have forgotten. For example, if the user inputs "I was moved by my graduation ceremony," the generation AI will suggest, "Would you like to add memories of your entrance ceremony as well?" The generation unit can also suggest related past events based on the user input, complementing important events that the user may have forgotten. For example, if the user inputs "I was moved by my wedding," the generation AI will suggest, "Would you like to add memories of the proposal moment as well?" The generation unit can also suggest past events in chronological order based on the user input, complementing important events that the user may have forgotten. For example, if the user inputs "I was moved by my trip," the generation AI will suggest, "Would you like to add memories of the planning stage of the trip as well?" This allows the generation unit to complement important events that the user may have forgotten.

[0053] The generation unit can automatically search for related images and audio data based on the user's input and complete the input. For example, when a user inputs an event, the generation AI automatically searches for related images and completes the input. For example, if the user inputs "I was moved by the graduation ceremony," it automatically searches for and adds photos of the graduation ceremony. The generation unit also automatically searches for related audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by the wedding," it automatically searches for and adds an audio clip of the wedding. The generation unit also automatically searches for related images and audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by the trip," it automatically searches for and adds scenic photos and audio guides from the travel destination. This allows the generation unit to automatically search for related images and audio data to complete the user's input.

[0054] The generation unit can suggest similar events of other users based on the user's input, encouraging empathy. For example, the generation AI can suggest similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my graduation ceremony," it will suggest other users' memories of graduation ceremonies. The generation unit can also suggest similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my wedding," it will suggest other users' memories of weddings. The generation unit can also suggest similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my trip," it will suggest other users' travel memories. This allows the generation AI to suggest similar events of other users, encouraging empathy.

[0055] The generation unit can analyze the user's profile information and provide a customized video template based on the user's hobbies and interests. For example, the generation AI analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a travel-themed template is provided for a user whose hobby is traveling. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's interests. For example, a music-themed template is provided for a user who likes music. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a sports-themed template is provided for a user who likes sports. This makes it possible to provide a customized video template based on the user's hobbies and interests.

[0056] The generation unit can analyze the user's input content and automatically generate a storyline that combines past events and future goals. For example, the generation AI analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past graduation ceremony and a future career goal is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past wedding and a future family plan is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past trip and a future travel plan is created. This makes it possible to automatically generate a storyline that combines past events and future goals.

[0057] The generation unit can analyze the user's profile information and customize videos from the perspectives of different cultures and regions. For example, the generation AI analyzes the user's profile information and customizes videos from the perspectives of different cultures. For example, if the user is from Japan, it creates a video that reflects Japanese culture. The generation unit also analyzes the user's profile information and customizes videos from the perspectives of different regions. For example, if the user is from the United States, it creates a video that reflects the regional characteristics of the United States. The generation unit also analyzes the user's profile information and customizes videos from the perspectives of different cultures and regions. For example, if the user has a multicultural background, it creates a video that reflects multiple cultures. This allows videos to be customized from the perspectives of different cultures and regions.

[0058] The generation unit can analyze the user's input and automatically generate videos of different genres. For example, the generation AI can analyze the user's input and automatically generate documentary-style videos. For example, it can create videos that introduce the events of the user's life in chronological order. The generation unit can also analyze the user's input and automatically generate comedy-style videos. For example, it can create videos that humorously depict the events of the user's life. The generation unit can also analyze the user's input and automatically generate drama-style videos. For example, it can create videos that movingly depict the events of the user's life. This makes it possible to automatically generate videos of different genres.

[0059] The evaluation unit can use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, the generation AI can analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it can extract moving scenes and fun scenes to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it can extract moving moments from a wedding or highlight scenes from a trip to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it can extract moving moments from a graduation ceremony or fun times with family to create a shortened version. This allows important scenes to be automatically highlighted to create a shortened version.

[0060] The evaluation unit uses the generation AI to analyze the user's videos and automatically link them to related events and people, thereby providing an interactive video experience. For example, the generation AI analyzes the user's videos and automatically links them to related events and people. For example, it links videos of friends and family to related videos of a graduation ceremony. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link them to related events and people. For example, it links videos of a proposal and honeymoon to related videos of a wedding. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link them to related events and people. For example, it links videos of tourist spots and guides to related videos of a travel video. This makes it possible to automatically link related events and people, thereby providing an interactive video experience.

[0061] The evaluation unit can use the generation AI to analyze the user's video and convert it into a different format to make it easier to share. For example, the generation AI can analyze the user's video and convert it into GIF format to make it easier to share. For example, touching or fun scenes can be converted into GIFs to share on social media. The evaluation unit can also use the generation AI to analyze the user's video and convert it into a slideshow format to make it easier to share. For example, travel photos and videos can be converted into slideshows to share with family and friends. The evaluation unit can also use the generation AI to analyze the user's video and convert it into a different format to make it easier to share. For example, a wedding video can be converted into a GIF or slideshow to share. This allows conversion into a different format to make it easier to share.

[0062] The evaluation unit can use the generation AI to analyze the user's video and optimize it for different devices. For example, the generation AI can analyze the user's video and optimize it for smartphones. For example, it can convert it into a portrait video format to make it easier to watch on a smartphone. The evaluation unit can also use the generation AI to analyze the user's video and optimize it for tablets. For example, it can adjust the video resolution and layout to match the tablet screen size. The evaluation unit can also use the generation AI to analyze the user's video and optimize it for VR headsets. For example, it can convert it into a 360-degree video format to make it easier to watch on a VR headset. This allows it to be optimized for different devices.

[0063] The evaluation unit can use the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the generation AI analyzes the user's videos and provides a recommendation algorithm based on the interests and concerns of other users. For example, it recommends inspiring videos to a user who likes inspiring videos. The evaluation unit also uses the generation AI to analyze the user's videos and provides a recommendation algorithm based on the interests and concerns of other users. For example, it recommends travel videos to a user who likes travel videos. The evaluation unit also uses the generation AI to analyze the user's videos and provides a recommendation algorithm based on the interests and concerns of other users. For example, it recommends wedding videos to a user who likes wedding videos. This makes it possible to provide a recommendation algorithm based on the interests and concerns of other users.

[0064] The evaluation unit can use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, the generation AI can analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it can create a "feature of moving videos" or a "feature of fun videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it can create a "feature of wedding videos" or a "feature of travel videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it can create a "feature of family memories" or a "feature of memories with friends." This allows it to automatically pick out highly rated videos to create a feature page.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The profile input unit inputs user profile information, such as name, age, gender, and occupation. Step 2: The event input section inputs events in the user's life, such as marriage, graduation, and job change. Step 3: The generator generates a video based on the information entered by the profile input unit and the event input unit. For example, the generator AI analyzes the entered information and creates a video that meets the user's wishes. Step 4: The upload unit uploads the created video. For example, a user can upload a video they created as a private or public video. Step 5: The rating unit receives ratings from other users for the uploaded video. For example, other users can "like" or comment on the video.

[0067] (Example 2) The video generation system according to an embodiment of the present invention is a system in which a generation AI creates videos based on a user's profile information and life events, and the videos can be uploaded and evaluated. This allows the user to visually express their own life and receive evaluations from other users.

[0068] A video generation system according to an embodiment includes a profile input unit, an event input unit, a generation unit, an upload unit, and an evaluation unit. The profile input unit inputs a user's profile information. For example, information such as name, age, gender, and occupation can be input. The event input unit inputs events in the user's life. For example, events such as marriage, graduation, and job change can be input. The generation unit generates a video based on the information input by the profile input unit and the event input unit. For example, the generation AI analyzes the input information and creates a video according to the user's wishes. The upload unit uploads the generated video. For example, a video created by a user can be uploaded as a private or public video. The evaluation unit receives evaluations from other users for the uploaded video. For example, other users can "like" or comment on the video. As a result, the video generation system according to an embodiment can generate, upload, and evaluate a video based on a user's profile information and life events.

[0069] The generation unit can estimate the user's emotions and automatically generate a scenario based on those emotions. For example, the generation unit uses a generation AI to perform emotional analysis of an event entered by the user and automatically generate a video scenario based on that emotion. For example, if a user enters "I was moved by the graduation ceremony," the generation AI creates a scenario that reflects the emotion. The generation unit also analyzes the intensity of the emotion entered by the user and automatically generates a scenario based on the intensity of the emotion. For example, if the emotion is high, a scenario containing more moving scenes is created. The generation unit also analyzes multiple emotions entered by the user and automatically generates a scenario based on each emotion. For example, if the emotion is mixed with joy, a scenario that reflects both emotions is created. This allows for automatic generation of a scenario based on the user's emotions.

[0070] The generation unit can automatically generate related questions based on the user's input and collect detailed information. For example, in the generation unit, the generation AI automatically generates related questions for an event entered by the user and requests additional information from the user. For example, if the user enters "I was moved at the graduation ceremony," the generation unit will ask, "Which moment was particularly moving?" The generation unit also automatically generates detailed questions for the event entered by the user to collect the background of the event and specific episodes. For example, if the user enters "I was moved at the graduation ceremony," the generation AI will ask, "Who did you share your emotions with?" The generation unit also automatically generates questions in chronological order for the event entered by the user to understand the flow of events in detail. For example, if the user enters "I was moved at the graduation ceremony," the generation AI will ask, "What events happened before and after the graduation ceremony?" This allows detailed information to be collected based on the user's input.

[0071] The generation unit can suggest similar past events based on the user's input and complement important events. For example, in the generation unit, the generation AI suggests similar past events for an event entered by the user, complementing important events that the user may have forgotten. For example, if the user enters "I was moved by my graduation ceremony," the generation AI suggests, "Would you like to add memories of your entrance ceremony as well?" The generation unit also suggests related past events for an event entered by the user, complementing important events that the user may have forgotten. For example, if the user enters "I was moved by my wedding," the generation AI suggests, "Would you like to add memories of the proposal moment as well?" The generation unit also suggests past events in chronological order for an event entered by the user, complementing important events that the user may have forgotten. For example, if the user enters "I was moved by my trip," the generation AI suggests, "Would you like to add memories of the planning stage of the trip as well?" This allows the generation unit to complement important events that the user may have forgotten.

[0072] The generation unit can automatically search for related images and audio data based on the user's input and complete the input. For example, when a user inputs an event, the generation AI automatically searches for related images and completes the input. For example, if the user inputs "I was moved by my graduation ceremony," photos of the graduation ceremony are automatically searched for and added. The generation unit also automatically searches for related audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by my wedding," an audio clip of the wedding is automatically searched for and added. The generation unit also automatically searches for related images and audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by my trip," scenic photos of the travel destination and audio guides are automatically searched for and added. This allows the generation unit to automatically search for related images and audio data to complete the user's input.

[0073] The generation unit can suggest similar events of other users based on the user's input, encouraging empathy. For example, in the generation unit, the generation AI suggests similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my graduation ceremony," it will suggest other users' memories of graduation ceremonies. In addition, in the generation unit, the generation AI suggests similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my wedding," it will suggest other users' memories of weddings. In addition, in the generation unit, the generation AI suggests similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my trip," it will suggest other users' travel memories. In this way, it is possible to suggest similar events of other users and encourage empathy.

[0074] The generation unit can use the emotion estimation function to analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, the generation unit can analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, when a user inputs "I was moved by the graduation ceremony," the generation unit can suggest an episode to further deepen the emotion. The generation unit can also analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, when a user inputs "I was moved by the wedding," the generation unit can suggest music or effects to further deepen the emotion. The generation unit can also analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, when a user inputs "I was moved by the trip," the generation unit can suggest landscape photos or audio guides to further deepen the emotion. In this way, the generation unit can analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions.

[0075] The generation unit can estimate the user's emotions and automatically select music and effects based on the emotions to add to the video. For example, the generation AI of the generation unit estimates the user's emotions and automatically selects music based on the emotions to add to the video. For example, moving music is added to moving events, and happy music is added to happy events. The generation unit can also estimate the user's emotions and automatically select effects based on the emotions to add to the video. For example, a slow-motion effect is added to moving events, and a colorful effect is added to happy events. The generation unit can also estimate the user's emotions and automatically select music and effects based on the emotions to add to the video. For example, moving music and a slow-motion effect are added to moving events, and happy music and a colorful effect are added to happy events. In this way, music and effects based on the user's emotions can be automatically selected and added to the video.

[0076] The generation unit can analyze the user's profile information and provide a customized video template based on the user's hobbies and interests. For example, the generation AI in the generation unit analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a travel-themed template is provided for a user whose hobby is traveling. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's interests. For example, a music-themed template is provided for a user who likes music. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a sports-themed template is provided for a user who likes sports. This makes it possible to provide a customized video template based on the user's hobbies and interests.

[0077] The generation unit can analyze the user's input content and automatically generate a storyline that combines past events and future goals. For example, the generation AI in the generation unit analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past graduation ceremony and a future career goal is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past wedding and a future family plan is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past trip and a future travel plan is created. This makes it possible to automatically generate a storyline that combines past events and future goals.

[0078] The generation unit can analyze the user's profile information and customize videos from the perspectives of different cultures and regions. In the generation unit, for example, the generation AI analyzes the user's profile information and customizes videos from the perspectives of different cultures. For example, if the user is from Japan, a video that reflects Japanese culture is created. In addition, the generation unit uses the generation AI to analyze the user's profile information and customize videos from the perspectives of different regions. For example, if the user is from the United States, a video that reflects the regional characteristics of the United States is created. In addition, the generation unit uses the generation AI to analyze the user's profile information and customize videos from the perspectives of different cultures and regions. For example, if the user has a multicultural background, a video that reflects multiple cultures is created. This allows videos to be customized from the perspectives of different cultures and regions.

[0079] The generation unit can analyze the user's input content and automatically generate videos of different genres. For example, the generation AI in the generation unit analyzes the user's input content and automatically generates documentary-style videos. For example, it creates a video that introduces the events of the user's life in chronological order. The generation AI in the generation unit also analyzes the user's input content and automatically generates comedy-style videos. For example, it creates a video that humorously depicts the events of the user's life. The generation AI in the generation unit also analyzes the user's input content and automatically generates drama-style videos. For example, it creates a video that movingly depicts the events of the user's life. This makes it possible to automatically generate videos of different genres.

[0080] The generation unit can use the emotion estimation function to adjust scene transitions and effects of a video in real time based on the user's emotions. The generation unit, for example, uses the emotion estimation function to adjust scene transitions of a video in real time based on the user's emotions. For example, slow motion is used in emotional scenes, and fast-paced transitions are used in happy scenes. The generation unit also uses the emotion estimation function to adjust effects based on the user's emotions in real time. For example, a warm color filter is used in emotional scenes, and colorful effects are added in happy scenes. The generation unit also uses the emotion estimation function to adjust scene transitions and effects based on the user's emotions in real time. For example, slow motion and a warm color filter are used in emotional scenes, and fast-paced transitions and colorful effects are added in happy scenes. In this way, scene transitions and effects of a video can be adjusted in real time based on the user's emotions.

[0081] The evaluation unit uses the generation AI to estimate the user's emotions and tag videos based on those emotions, thereby improving the searchability of videos. For example, the generation AI estimates the user's emotions and tags videos based on those emotions. For example, it tags moving scenes with "moving" and fun scenes with "fun." The evaluation unit also improves the searchability of videos by having the generation AI estimate the user's emotions and tag videos based on those emotions. For example, if you search for "I want to watch moving scenes," scenes tagged with "moving" will be displayed. The evaluation unit also makes it easier to organize videos by having the generation AI estimate the user's emotions and tag videos based on those emotions. For example, it automatically generates a playlist that plays moving scenes together. This allows tagging based on the user's emotions and improves the searchability of videos.

[0082] The evaluation unit can use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, the evaluation unit uses the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it extracts moving scenes and fun scenes to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it extracts moving moments from a wedding or highlight scenes from a trip to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it extracts moving moments from a graduation ceremony or fun times with family to create a shortened version. This allows important scenes to be automatically highlighted to create a shortened version.

[0083] The evaluation unit uses the generation AI to analyze the user's videos and automatically link related events and people, thereby providing an interactive video experience. For example, the evaluation unit uses the generation AI to analyze the user's videos and automatically link related events and people. For example, videos of friends and family are linked to a graduation ceremony video. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link related events and people. For example, videos of a proposal and honeymoon are linked to a wedding video. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link related events and people. For example, videos of tourist spots and guides are linked to a travel video. This makes it possible to automatically link related events and people, thereby providing an interactive video experience.

[0084] The evaluation unit can use the generation AI to analyze the user's video and convert it into a different format to make it easier to share. For example, the evaluation unit can have the generation AI analyze the user's video and convert it into GIF format to make it easier to share. For example, touching or fun scenes can be converted into GIFs to share on social media. The evaluation unit can also have the generation AI analyze the user's video and convert it into a slideshow format to make it easier to share. For example, travel photos and videos can be converted into slideshows to share with family and friends. The evaluation unit can also have the generation AI analyze the user's video and convert it into a different format to make it easier to share. For example, a wedding video can be converted into a GIF or slideshow to share. This allows the video to be converted into a different format to make it easier to share.

[0085] The evaluation unit can use the generation AI to analyze the user's video and optimize it for different devices. For example, the evaluation unit uses the generation AI to analyze the user's video and optimize it for smartphones. For example, it converts the video into a portrait video format to make it easier to watch on a smartphone. The evaluation unit also uses the generation AI to analyze the user's video and optimize it for tablets. For example, it adjusts the video resolution and layout to match the tablet screen size. The evaluation unit also uses the generation AI to analyze the user's video and optimize it for VR headsets. For example, it converts the video into a 360-degree video format to make it easier to watch on a VR headset. This allows it to be optimized for different devices.

[0086] The evaluation unit can use the emotion estimation function to automatically adjust the playback order and playback speed of the videos based on the user's emotion. The evaluation unit, for example, uses the emotion estimation function to automatically adjust the playback order of the videos based on the user's emotion. For example, emotional scenes are played first, and happy scenes are played later. The evaluation unit also uses the emotion estimation function to automatically adjust the playback speed of the videos based on the user's emotion. For example, emotional scenes are played in slow motion, and happy scenes are played at normal speed. The evaluation unit also uses the emotion estimation function to automatically adjust the playback order and playback speed of the videos based on the user's emotion. For example, emotional scenes are played first in slow motion, and happy scenes are played later at normal speed. In this way, the playback order and playback speed of the videos can be automatically adjusted based on the user's emotion.

[0087] The evaluation unit can use the generation AI to estimate the user's emotions and automatically generate comments and feedback based on the emotions. For example, the evaluation unit uses the generation AI to estimate the user's emotions and automatically generate comments based on the emotions. For example, a comment such as "I was moved!" is generated for an emotional video. The evaluation unit also uses the generation AI to estimate the user's emotions and automatically generate feedback based on the emotions. For example, a comment such as "I had so much fun!" is generated for an enjoyable video. The evaluation unit also uses the generation AI to estimate the user's emotions and automatically generate comments and feedback based on the emotions. For example, a comment such as "I was moved!" is generated for an emotional video, and a comment such as "I had so much fun!" is generated for an enjoyable video. In this way, comments and feedback based on the user's emotions can be automatically generated.

[0088] The evaluation unit can use the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends moving videos to a user who likes moving videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends travel videos to a user who likes travel videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends wedding videos to a user who likes wedding videos. This makes it possible to provide a recommendation algorithm based on the interests and concerns of other users.

[0089] The evaluation unit can use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, the evaluation unit uses the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of moving videos" or a "feature of fun videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of wedding videos" or a "feature of travel videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of family memories" or a "feature of memories with friends." This allows the generation AI to automatically pick out highly rated videos to create a feature page.

[0090] The uploading unit uses a generation AI to analyze a user's video and optimize it for different platforms before uploading it. For example, the generation AI in the uploading unit analyzes the user's video and optimizes it for YouTube before uploading it. For example, it converts it into YouTube's recommended format and automatically generates titles and tags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for Instagram before uploading it. For example, it converts it into Instagram's recommended format and automatically generates captions and hashtags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for different platforms before uploading it. For example, it converts it into YouTube and Instagram's recommended formats and automatically generates titles and tags appropriate for each platform. This allows the video to be optimized for different platforms before being uploaded.

[0091] The uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit translates the video into English, French, Chinese, etc., and uploads it. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically generates subtitles and displays them in different languages. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically translates audio and plays it in different languages. This allows the uploading unit to automatically translate the audio into different languages ​​and receive international evaluation.

[0092] The evaluation unit uses the emotion estimation function to set evaluation criteria based on the user's emotions and can identify videos that are likely to resonate emotionally. The evaluation unit, for example, uses the emotion estimation function to set evaluation criteria based on the user's emotions and identify videos that are likely to resonate emotionally. For example, a criterion for giving a high rating to moving videos is set. The evaluation unit also uses the emotion estimation function to set evaluation criteria based on the user's emotions and identify videos that are likely to resonate emotionally. For example, a criterion for giving a high rating to fun videos is set. The evaluation unit also uses the emotion estimation function to set evaluation criteria based on the user's emotions and identify videos that are likely to resonate emotionally. For example, a criterion for giving a high rating to moving videos and fun videos is set. This makes it possible to identify videos that are likely to resonate emotionally.

[0093] The evaluation unit can use the generation AI to estimate the user's emotions and automatically generate comments and feedback based on the emotions. For example, the evaluation unit uses the generation AI to estimate the user's emotions and automatically generate comments based on the emotions. For example, a comment such as "I was moved!" is generated for an emotional video. The evaluation unit also uses the generation AI to estimate the user's emotions and automatically generate feedback based on the emotions. For example, a comment such as "I had so much fun!" is generated for an enjoyable video. The evaluation unit also uses the generation AI to estimate the user's emotions and automatically generate comments and feedback based on the emotions. For example, a comment such as "I was moved!" is generated for an emotional video, and a comment such as "I had so much fun!" is generated for an enjoyable video. In this way, comments and feedback based on the user's emotions can be automatically generated.

[0094] The evaluation unit can use the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends moving videos to a user who likes moving videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends travel videos to a user who likes travel videos. The evaluation unit also uses the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the evaluation unit recommends wedding videos to a user who likes wedding videos. This makes it possible to provide a recommendation algorithm based on the interests and concerns of other users.

[0095] The evaluation unit can use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, the evaluation unit uses the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of moving videos" or a "feature of fun videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of wedding videos" or a "feature of travel videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it creates a "feature of family memories" or a "feature of memories with friends." This allows the generation AI to automatically pick out highly rated videos to create a feature page.

[0096] The uploading unit uses a generation AI to analyze a user's video and optimize it for different platforms before uploading it. For example, the generation AI in the uploading unit analyzes the user's video and optimizes it for YouTube before uploading it. For example, it converts it into YouTube's recommended format and automatically generates titles and tags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for Instagram before uploading it. For example, it converts it into Instagram's recommended format and automatically generates captions and hashtags. The uploading unit also uses a generation AI to analyze the user's video and optimizes it for different platforms before uploading it. For example, it converts it into YouTube and Instagram's recommended formats and automatically generates titles and tags appropriate for each platform. This allows the video to be optimized for different platforms before being uploaded.

[0097] The uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit translates the video into English, French, Chinese, etc., and uploads it. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically generates subtitles and displays them in different languages. The uploading unit also uses a generation AI to analyze a user's video and automatically translate it into different languages ​​to receive international evaluation. For example, the uploading unit automatically translates audio and plays it in different languages. This allows the uploading unit to automatically translate the audio into different languages ​​and receive international evaluation.

[0098] The evaluation unit uses the emotion estimation function to set evaluation criteria based on the user's emotions and can identify videos that are likely to resonate emotionally. The evaluation unit, for example, uses the emotion estimation function to set evaluation criteria based on the user's emotions and identify videos that are likely to resonate emotionally. For example, a criterion for giving a high rating to moving videos is set. The evaluation unit also uses the emotion estimation function to set evaluation criteria based on the user's emotions and identify videos that are likely to resonate emotionally. For example, a criterion for giving a high rating to fun videos is set. The evaluation unit also uses the emotion estimation function to set evaluation criteria based on the user's emotions and identify videos that are likely to resonate emotionally. For example, a criterion for giving a high rating to moving videos and fun videos is set. This makes it possible to identify videos that are likely to resonate emotionally.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The generation unit can automatically generate related questions based on the user's input to collect detailed information. For example, the generation AI can automatically generate related questions for an event entered by the user and request additional information from the user. For example, if the user enters "I was moved at the graduation ceremony," the AI ​​will ask, "Which moment was particularly moving?" The generation unit also automatically generates detailed questions for the event entered by the user to collect the background of the event and specific episodes. For example, if the user enters "I was moved at the graduation ceremony," the AI ​​will ask, "Who did you share that emotion with?" The generation unit also automatically generates questions in chronological order for the event entered by the user to understand the flow of events in detail. For example, if the user enters "I was moved at the graduation ceremony," the AI ​​will ask, "What events happened before and after the graduation ceremony?" This allows detailed information to be collected based on the user's input.

[0101] The generation unit can suggest similar past events based on the user's input and complement important events. For example, when a user inputs an event, the generation AI can suggest similar past events and complement important events that the user may have forgotten. For example, if the user inputs "I was moved by my graduation ceremony," the generation AI will suggest, "Would you like to add memories of your entrance ceremony as well?" The generation unit can also suggest related past events based on the user input, complementing important events that the user may have forgotten. For example, if the user inputs "I was moved by my wedding," the generation AI will suggest, "Would you like to add memories of the proposal moment as well?" The generation unit can also suggest past events in chronological order based on the user input, complementing important events that the user may have forgotten. For example, if the user inputs "I was moved by my trip," the generation AI will suggest, "Would you like to add memories of the planning stage of the trip as well?" This allows the generation unit to complement important events that the user may have forgotten.

[0102] The generation unit can automatically search for related images and audio data based on the user's input and complete the input. For example, when a user inputs an event, the generation AI automatically searches for related images and completes the input. For example, if the user inputs "I was moved by the graduation ceremony," it automatically searches for and adds photos of the graduation ceremony. The generation unit also automatically searches for related audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by the wedding," it automatically searches for and adds an audio clip of the wedding. The generation unit also automatically searches for related images and audio data when a user inputs an event and completes the input. For example, if the user inputs "I was moved by the trip," it automatically searches for and adds scenic photos and audio guides from the travel destination. This allows the generation unit to automatically search for related images and audio data to complete the user's input.

[0103] The generation unit can suggest similar events of other users based on the user's input, encouraging empathy. For example, the generation AI can suggest similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my graduation ceremony," it will suggest other users' memories of graduation ceremonies. The generation unit can also suggest similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my wedding," it will suggest other users' memories of weddings. The generation unit can also suggest similar events of other users for an event input by the user, encouraging empathy. For example, if the user inputs "I was moved by my trip," it will suggest other users' travel memories. This allows the generation AI to suggest similar events of other users, encouraging empathy.

[0104] The generation unit can use the emotion estimation function to analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, the generation unit can analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, when a user inputs "I was moved by the graduation ceremony," the generation unit can suggest an episode to further deepen the emotion. The generation unit can also analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, when a user inputs "I was moved by the wedding," the generation unit can suggest music or effects to further deepen the emotion. The generation unit can also analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions. For example, when a user inputs "I was moved by the trip," the generation unit can suggest landscape photos or audio guides to further deepen the emotion. In this way, the generation unit can analyze the emotion of the user when inputting in real time and make suggestions to draw out positive emotions.

[0105] The generation unit can estimate the user's emotions and automatically select music and effects based on the emotions to add to the video. For example, the generation AI can estimate the user's emotions and automatically select music based on the emotions to add to the video. For example, moving music is added to moving events, and happy music is added to happy events. The generation unit can also estimate the user's emotions and automatically select effects based on the emotions to add to the video. For example, a slow-motion effect is added to moving events, and a colorful effect is added to happy events. The generation unit can also estimate the user's emotions and automatically select music and effects based on the emotions to add to the video. For example, moving music and a slow-motion effect are added to moving events, and happy music and a colorful effect are added to happy events. This allows music and effects based on the user's emotions to be automatically selected and added to the video.

[0106] The generation unit can analyze the user's profile information and provide a customized video template based on the user's hobbies and interests. For example, the generation AI analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a travel-themed template is provided for a user whose hobby is traveling. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's interests. For example, a music-themed template is provided for a user who likes music. The generation unit also analyzes the user's profile information and provides a customized video template based on the user's hobbies and interests. For example, a sports-themed template is provided for a user who likes sports. This makes it possible to provide a customized video template based on the user's hobbies and interests.

[0107] The generation unit can analyze the user's input content and automatically generate a storyline that combines past events and future goals. For example, the generation AI analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past graduation ceremony and a future career goal is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past wedding and a future family plan is created. The generation unit also analyzes the user's input content and automatically generates a storyline that combines past events and future goals. For example, a storyline that combines a past trip and a future travel plan is created. This makes it possible to automatically generate a storyline that combines past events and future goals.

[0108] The generation unit can analyze the user's profile information and customize videos from the perspectives of different cultures and regions. For example, the generation AI analyzes the user's profile information and customizes videos from the perspectives of different cultures. For example, if the user is from Japan, it creates a video that reflects Japanese culture. The generation unit also analyzes the user's profile information and customizes videos from the perspectives of different regions. For example, if the user is from the United States, it creates a video that reflects the regional characteristics of the United States. The generation unit also analyzes the user's profile information and customizes videos from the perspectives of different cultures and regions. For example, if the user has a multicultural background, it creates a video that reflects multiple cultures. This allows videos to be customized from the perspectives of different cultures and regions.

[0109] The generation unit can analyze the user's input and automatically generate videos of different genres. For example, the generation AI can analyze the user's input and automatically generate documentary-style videos. For example, it can create videos that introduce the events of the user's life in chronological order. The generation unit can also analyze the user's input and automatically generate comedy-style videos. For example, it can create videos that humorously depict the events of the user's life. The generation unit can also analyze the user's input and automatically generate drama-style videos. For example, it can create videos that movingly depict the events of the user's life. This makes it possible to automatically generate videos of different genres.

[0110] The generation unit can use the emotion estimation function to adjust scene transitions and effects of a video in real time based on the user's emotions. For example, the emotion estimation function is used to adjust scene transitions of a video in real time based on the user's emotions. For example, slow motion is used in emotional scenes, and fast-paced transitions are used in happy scenes. The generation unit also uses the emotion estimation function to adjust effects based on the user's emotions in real time. For example, a warm color filter is used in emotional scenes, and colorful effects are added in happy scenes. The generation unit also uses the emotion estimation function to adjust scene transitions and effects based on the user's emotions in real time. For example, slow motion and a warm color filter are used in emotional scenes, and fast-paced transitions and colorful effects are added in happy scenes. In this way, scene transitions and effects of a video can be adjusted in real time based on the user's emotions.

[0111] The evaluation unit uses the generation AI to estimate the user's emotions and tag videos based on those emotions, improving video searchability. For example, the generation AI estimates the user's emotions and tags videos based on those emotions. For example, it tags moving scenes with "moving" and fun scenes with "fun." The evaluation unit also improves video searchability by having the generation AI estimate the user's emotions and tag videos based on those emotions. For example, if you search for "I want to watch moving scenes," scenes tagged with "moving" will be displayed. The evaluation unit also makes it easier to organize videos by having the generation AI estimate the user's emotions and tag videos based on those emotions. For example, it automatically generates a playlist that plays moving scenes together. This allows tagging based on the user's emotions and improves video searchability.

[0112] The evaluation unit can use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, the generation AI can analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it can extract moving scenes and fun scenes to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it can extract moving moments from a wedding or highlight scenes from a trip to create a shortened version. The evaluation unit can also use the generation AI to analyze the user's video and automatically highlight important scenes to create a shortened version. For example, it can extract moving moments from a graduation ceremony or fun times with family to create a shortened version. This allows important scenes to be automatically highlighted to create a shortened version.

[0113] The evaluation unit uses the generation AI to analyze the user's videos and automatically link them to related events and people, thereby providing an interactive video experience. For example, the generation AI analyzes the user's videos and automatically links them to related events and people. For example, it links videos of friends and family to related videos of a graduation ceremony. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link them to related events and people. For example, it links videos of a proposal and honeymoon to related videos of a wedding. The evaluation unit also uses the generation AI to analyze the user's videos and automatically link them to related events and people. For example, it links videos of tourist spots and guides to related videos of a travel video. This makes it possible to automatically link related events and people, thereby providing an interactive video experience.

[0114] The evaluation unit can use the generation AI to analyze the user's video and convert it into a different format to make it easier to share. For example, the generation AI can analyze the user's video and convert it into GIF format to make it easier to share. For example, touching or fun scenes can be converted into GIFs to share on social media. The evaluation unit can also use the generation AI to analyze the user's video and convert it into a slideshow format to make it easier to share. For example, travel photos and videos can be converted into slideshows to share with family and friends. The evaluation unit can also use the generation AI to analyze the user's video and convert it into a different format to make it easier to share. For example, a wedding video can be converted into a GIF or slideshow to share. This allows conversion into a different format to make it easier to share.

[0115] The evaluation unit can use the generation AI to analyze the user's video and optimize it for different devices. For example, the generation AI can analyze the user's video and optimize it for smartphones. For example, it can convert it into a portrait video format to make it easier to watch on a smartphone. The evaluation unit can also use the generation AI to analyze the user's video and optimize it for tablets. For example, it can adjust the video resolution and layout to match the tablet screen size. The evaluation unit can also use the generation AI to analyze the user's video and optimize it for VR headsets. For example, it can convert it into a 360-degree video format to make it easier to watch on a VR headset. This allows it to be optimized for different devices.

[0116] The evaluation unit can use the emotion estimation function to automatically adjust the playback order and playback speed of videos based on the user's emotions. For example, the emotion estimation function is used to automatically adjust the playback order of videos based on the user's emotions. For example, emotional scenes are played first, and happy scenes are played later. The evaluation unit also uses the emotion estimation function to automatically adjust the playback speed of videos based on the user's emotions. For example, emotional scenes are played in slow motion, and happy scenes are played at normal speed. The evaluation unit also uses the emotion estimation function to automatically adjust the playback order and playback speed of videos based on the user's emotions. For example, emotional scenes are played first in slow motion, and happy scenes are played later at normal speed. In this way, the playback order and playback speed of videos can be automatically adjusted based on the user's emotions.

[0117] The evaluation unit uses the generation AI to estimate the user's emotions and automatically generate comments and feedback based on the emotions. For example, the generation AI estimates the user's emotions and automatically generates comments based on the emotions. For example, it generates a comment such as "I was moved!" for an emotional video. The evaluation unit also uses the generation AI to estimate the user's emotions and automatically generate feedback based on the emotions. For example, it generates feedback such as "I had so much fun!" for an enjoyable video. The evaluation unit also uses the generation AI to estimate the user's emotions and automatically generate comments and feedback based on the emotions. For example, it generates comments and feedback such as "I was moved!" for an emotional video and "I had so much fun!" for an enjoyable video. This makes it possible to automatically generate comments and feedback based on the user's emotions.

[0118] The evaluation unit can use the generation AI to analyze the user's videos and provide a recommendation algorithm based on the interests and concerns of other users. For example, the generation AI analyzes the user's videos and provides a recommendation algorithm based on the interests and concerns of other users. For example, it recommends inspiring videos to a user who likes inspiring videos. The evaluation unit also uses the generation AI to analyze the user's videos and provides a recommendation algorithm based on the interests and concerns of other users. For example, it recommends travel videos to a user who likes travel videos. The evaluation unit also uses the generation AI to analyze the user's videos and provides a recommendation algorithm based on the interests and concerns of other users. For example, it recommends wedding videos to a user who likes wedding videos. This makes it possible to provide a recommendation algorithm based on the interests and concerns of other users.

[0119] The evaluation unit can use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, the generation AI can analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it can create a "feature of moving videos" or a "feature of fun videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it can create a "feature of wedding videos" or a "feature of travel videos." The evaluation unit can also use the generation AI to analyze the user's videos and automatically pick out highly rated videos to create a feature page. For example, it can create a "feature of family memories" or a "feature of memories with friends." This allows it to automatically pick out highly rated videos to create a feature page.

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

[0121] Step 1: The profile input unit inputs user profile information, such as name, age, gender, and occupation. Step 2: The event input section inputs events in the user's life, such as marriage, graduation, and job change. Step 3: The generator generates a video based on the information entered by the profile input unit and the event input unit. For example, the generator AI analyzes the entered information and creates a video that meets the user's wishes. Step 4: The upload unit uploads the created video. For example, a user can upload a video they created as a private or public video. Step 5: The rating unit receives ratings from other users for the uploaded video. For example, other users can "like" or comment on the video.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0189] 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 profile input section for inputting user profile information; an event input unit for inputting events in the user's life; a generation unit that generates a video based on the information input by the profile input unit and the event input unit; an uploading unit that uploads the generated video; An evaluation unit that receives evaluations from other users for the uploaded video. A system characterized by:

2. The generation unit Estimate the user's emotions and automatically generate scenarios based on those emotions 2. The system of claim 1.

3. The generation unit Auto-generate relevant questions based on user input to gather more information 2. The system of claim 1.

4. The generation unit Based on user input, it suggests similar events from the past and completes important events.

2. The system of claim 1.

5. The generation unit Automatically search for related images and audio data based on user input to complete the input 2. The system of claim 1.

Images