system

Abstract

The system according to the embodiment aims to generate a storytelling-type video by effectively utilizing photos and videos taken by a user. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit receives photos or videos uploaded by a user. The analysis unit analyzes the photos or videos received by the reception unit. The generation unit generates a storytelling video based on the photos or videos analyzed by the analysis unit. The provision unit provides the video generated by the generation unit to the user. The storage unit saves the uploaded data.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to effectively utilize photos and videos taken by users to generate storytelling-style videos.

[0005] The system according to the embodiment aims to generate a storytelling-type video by effectively utilizing photos and videos taken by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit receives photos or videos uploaded by a user. The analysis unit analyzes the photos or videos received by the reception unit. The generation unit generates a storytelling video based on the photos or videos analyzed by the analysis unit. The provision unit provides the video generated by the generation unit to the user. The storage unit saves the uploaded data. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize photos and videos taken by a user to generate a storytelling-type video. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a free service available to users, utilizing a generation AI and a nationwide data center cloud. This system allows users to upload photos and videos taken with their smartphones via an application, and the generation AI automatically generates a storytelling video of a specific day. For example, users can upload photos and videos by specifying the date of a special event, such as a family trip or a birthday party. The generation AI analyzes the uploaded photos and videos and automatically generates a storytelling video of the specific day. The generated videos are designed for easy viewing by users and can also be shared on social media, allowing users to easily reminisce about memories of a specific day. This system allows users to easily reminisce about memories of a specific day. For example, users can reminisce about special events, such as family trips or birthday parties, through videos automatically generated by the generation AI. Users can also share the memories with others by sharing the generated videos on social media. Furthermore, because this system utilizes a nationwide data center cloud, users do not need to worry about storage, even when uploading a large number of photos and videos. This allows users to use the service with peace of mind.

[0029] A storytelling video generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit accepts uploads of photos or videos from users. For example, users can upload photos or videos taken with their smartphones via an application. The reception unit temporarily stores the uploaded data and passes it to the analysis unit. The analysis unit analyzes the uploaded photos or videos using a generation AI. For example, the generation AI can understand the content of the photos and analyze the content of the videos using image recognition technology. The analysis unit can also analyze the audio and text of the videos using natural language processing technology. The generation unit generates a storytelling video based on the photos and videos analyzed by the analysis unit. For example, the generation AI automatically generates a storytelling video for a specific day based on the analyzed information. The generation unit passes the generated video to the provision unit. The provision unit provides the generated video to a user. For example, the user can play the generated video within the app. The provision unit can also provide a link for sharing the generated video on social media or other platforms. The storage unit stores uploaded photos and videos and provides them to the analysis unit and generation unit as needed. For example, the storage unit can efficiently store large amounts of data using cloud storage. This allows the storytelling video generation system according to the embodiment to analyze photos and videos uploaded by users and automatically generate and provide storytelling videos.

[0030] The reception unit can accept a user's request to upload photos and videos by specifying a specific date. The reception unit, for example, accepts a user's request to specify a specific date using calendar input or text input. For example, a user can upload photos and videos by specifying the date of a special event such as a family trip or a birthday party. This allows the user to easily upload photos and videos related to a specific date. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input a user-specified date into AI, which can then automatically select photos and videos related to that date.

[0031] The analysis unit can understand the content of uploaded photos and videos and extract information for generating a storytelling-type video for a specific day. The analysis unit can understand the content of uploaded photos and videos, for example, using a generation AI. For example, the generation AI can analyze the content of photos and understand the content of videos using image recognition technology. The analysis unit can also analyze the audio and text of videos using natural language processing technology. Furthermore, the analysis unit can extract important scenes from videos using keyframe extraction technology. For example, the analysis unit can extract particularly important scenes from videos and extract information for generating a storytelling-type video based on the extracted scenes. This allows the analysis unit to understand the content of uploaded photos and videos and extract information for generating a storytelling-type video for a specific day. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input uploaded photos and videos into the generation AI, which can then analyze the content and extract information.

[0032] The generation unit can generate a storytelling-type video for a specific day based on the extracted information. The generation unit, for example, uses a generation AI to generate a storytelling-type video for a specific day based on the extracted information. For example, the generation AI can automatically generate a storytelling-type video for a specific day based on the information extracted by the analysis unit. The generation unit converts the generated video into a format that can be viewed by a user and passes it to the provision unit. This allows the generation unit to generate a storytelling-type video for a specific day based on the extracted information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the extracted information into the generation AI, and the generation AI can generate a video based on the information.

[0033] The providing unit can provide the generated video so that the user can view it. The providing unit, for example, provides a function for playing the generated video within an app. For example, the user can play the generated video within the app. The providing unit can also provide a link for sharing the generated video on a social networking site or the like. For example, the user can share the generated video on a social networking site. This allows the providing unit to provide the generated video so that the user can view it. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the generated video to AI, which can convert the video into an optimal format and provide it.

[0034] The storage unit can store uploaded photos or videos and provide them to the analysis unit or generation unit as needed. The storage unit can store uploaded photos or videos using, for example, cloud storage. For example, the storage unit can use cloud storage to efficiently store large amounts of data. The storage unit also has a function of providing the stored data to the analysis unit or generation unit. For example, the storage unit can quickly provide data required by the analysis unit and provide data required by the generation unit in an appropriate format. This allows the storage unit to store uploaded photos or videos and provide them to the analysis unit or generation unit as needed. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the stored data into AI, which can convert the data into an optimal format and provide it.

[0035] The reception unit can analyze the user's past upload history and select the optimal upload method. The reception unit, for example, stores and analyzes the user's past upload history in a database. For example, the reception unit can prioritize and suggest upload methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit analyzes the formats of photos and videos previously uploaded by the user and selects the optimal upload method. For example, the reception unit can suggest the optimal upload method based on the formats of photos and videos previously uploaded by the user. This allows the reception unit to select the optimal upload method based on the user's past upload history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history into AI, and the AI ​​can select the optimal upload method based on the data.

[0036] The reception unit can perform filtering based on the user's current events or areas of interest when uploading. The reception unit can, for example, refer to the user's calendar information or social media posts and perform filtering based on the user's current events or areas of interest. For example, the reception unit can prioritize uploading photos and videos related to events in which the user is currently participating. The reception unit can also filter and upload related photos and videos based on the user's areas of interest (travel, sports, etc.). Furthermore, the reception unit can refer to the user's calendar information and upload photos and videos related to specific events. This allows the reception unit to perform filtering based on the user's current events and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's calendar information and social media posts into AI, and the AI ​​can perform filtering based on that data.

[0037] The reception unit can select the optimal upload method according to the user's input method when uploading. For example, the reception unit provides a function that automatically selects and uploads related photos when the user simply voice-inputs, "Upload photos from my family trip." For example, the reception unit can use voice recognition technology to convert the user's voice input into text and automatically select and upload related photos and videos. The reception unit can also provide a function that automatically uploads photos and videos taken on a specific date when the user inputs a specific date. Furthermore, the reception unit provides a function that automatically selects and uploads the most suitable images when the user selects images. This allows the reception unit to select the optimal upload method according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without AI. For example, the reception unit can input the user's voice input or text input into AI, and the AI ​​can select the optimal upload method based on that data.

[0038] When uploading, the reception unit can prioritize uploading highly relevant data taking into account the user's geographical location information. The reception unit, for example, acquires the user's GPS data or IP address and prioritizes uploading highly relevant data based on the geographical location information. For example, if the user is traveling, relevant photos and videos can be prioritized to be uploaded based on the geographical location information of the travel destination. Also, if the user is participating in a specific event, relevant photos and videos can be prioritized to be uploaded based on the geographical location information of the event. Furthermore, if the user is at home, relevant photos and videos can be prioritized to be uploaded based on the geographical location information around the home. This allows the reception unit to prioritize uploading highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can select and upload highly relevant data based on that data.

[0039] The reception unit can analyze the user's social media activity and upload relevant data at the time of uploading. The reception unit, for example, accesses the user's social media account and analyzes the content of posts and check-in information. For example, the reception unit can prioritize uploading photos and videos related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and upload relevant photos and videos. Furthermore, the reception unit can upload relevant photos and videos based on the activities of the user's friends on social media. This allows the reception unit to upload relevant data based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can select and upload relevant data based on that data.

[0040] The reception unit can customize the upload method by reflecting the user's past feedback when uploading. The reception unit, for example, stores the user's past feedback in a database and analyzes it. For example, the reception unit can suggest an optimal upload method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific upload method based on the user's past feedback. Furthermore, the reception unit analyzes the user's past feedback and customizes the upload method. For example, the reception unit can change settings according to the user's preferences and customize the upload method. This allows the reception unit to customize the upload method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, and the AI ​​can customize the upload method based on the data.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo or video. The analysis unit, for example, performs a detailed analysis on important photos or videos designated by the user. For example, the analysis unit can perform a detailed analysis on photos or videos of important events. The analysis unit can also perform a concise analysis on everyday photos and videos. Furthermore, the analysis unit can perform a detailed analysis on important photos and videos designated by the user. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the photo or video. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input importance data of the photo or video into the generation AI, and the generation AI can adjust the level of detail of the analysis based on that data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the photo or video. For example, the analysis unit can apply a scenery analysis algorithm to travel photos. For example, the analysis unit can analyze the travel photos by applying a scenery analysis algorithm. The analysis unit can also apply a face recognition algorithm to a birthday party video. For example, the analysis unit can analyze the birthday party video by applying a face recognition algorithm. Furthermore, the analysis unit can apply a motion analysis algorithm to photos of a sporting event. For example, the analysis unit can analyze the sporting event photos by applying a motion analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the photo or video. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input category data of the photo or video into the generation AI, and the generation AI can apply an appropriate analysis algorithm based on that data.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, stores the user's past analysis results in a database and analyzes them. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. Furthermore, the analysis unit can analyze the user's past analysis results and improve the analysis accuracy. This allows the analysis unit to improve the analysis accuracy by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the analysis accuracy based on that data.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the photos and videos were taken. The analysis unit obtains the time when the photos and videos were taken based on, for example, a timestamp in the metadata or user input. For example, the analysis unit can prioritize analyzing recently taken photos and videos. The analysis unit can also prioritize analyzing photos and videos taken on the date of a specific event. Furthermore, the analysis unit can determine the analysis priority based on the time when the photos and videos were taken specified by the user. This allows the analysis unit to determine the analysis priority based on the time when the photos and videos were taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the photos and videos were taken into the generation AI, and the generation AI can determine the analysis priority based on that data.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of photos and videos. The analysis unit evaluates relevance, for example, based on photos and videos related to a common tag or the same event. For example, the analysis unit can prioritize analysis of highly relevant photos and videos. The analysis unit can also adjust the order of analysis based on relevance specified by a user. Furthermore, the analysis unit can automatically determine relevance using an analysis algorithm and adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of photos and videos. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input relevance data of photos and videos into a generation AI, and the generation AI can adjust the order of analysis based on that data.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the level of expertise based on, for example, the user's occupation and past usage history. For example, if the user has technical expertise, the analysis unit can provide analysis results using detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results using concise and easy-to-understand terms. Furthermore, the analysis unit can determine the user's level of expertise based on the user's past analysis results and use appropriate terms. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can select appropriate terms based on that data.

[0047] The generation unit can adjust the level of detail of the generation based on the importance of the photo or video during generation. For example, the generation unit generates a detailed video for an important photo or video designated by a user. For example, the generation unit can generate a detailed video for a photo or video of an important event. The generation unit can also generate a concise video for an everyday photo or video. Furthermore, the generation unit can generate a detailed video for an important photo or video designated by a user. This allows the generation unit to adjust the level of detail of the generation based on the importance of the photo or video. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the photo or video to the generation AI, and the generation AI can adjust the level of detail of the generation based on that data.

[0048] The generation unit can apply different generation algorithms depending on the category of the photo or video during generation. For example, the generation unit can generate a video by applying a scenery analysis algorithm to travel photos. For example, the generation unit can generate a video by applying a scenery analysis algorithm to travel photos. The generation unit can also generate a video by applying a face recognition algorithm to a birthday party video. For example, the generation unit can generate a video by applying a face recognition algorithm to a birthday party video. Furthermore, the generation unit can generate a video by applying a motion analysis algorithm to photos of a sporting event. For example, the generation unit can generate a video by applying a motion analysis algorithm to photos of a sporting event. This allows the generation unit to apply different generation algorithms depending on the category of the photo or video. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the photo or video into the generation AI, and the generation AI can apply an appropriate generation algorithm based on the data.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, stores the user's past generation results in a database and analyzes them. For example, the generation unit can adjust the generation algorithm based on the user's past generation results. The generation unit can also learn specific patterns from the user's past generation results and improve the generation accuracy. Furthermore, the generation unit can analyze the user's past generation results and improve the generation accuracy. In this way, the generation unit can improve the generation accuracy by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI, and the generation AI can improve the generation accuracy based on that data.

[0050] At the time of generation, the generation unit can determine the generation priority based on the shooting date of the photos and videos. The generation unit acquires the shooting date of the photos and videos based on, for example, a timestamp in the metadata or a user input. For example, the generation unit can prioritize generating photos and videos taken recently. The generation unit can also prioritize generating photos and videos taken on the date of a specific event. Furthermore, the generation unit can determine the generation priority based on the shooting date specified by the user. This allows the generation unit to determine the generation priority based on the shooting date of the photos and videos. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input shooting date data of the photos and videos into the generation AI, and the generation AI can determine the generation priority based on that data.

[0051] The generation unit can adjust the order of generation based on the relevance of photos and videos during generation. The generation unit evaluates the relevance, for example, based on photos and videos related to a common tag or the same event. For example, the generation unit can prioritize generating highly relevant photos and videos. The generation unit can also adjust the order of generation based on the relevance specified by the user. Furthermore, the generation unit can automatically determine the relevance using a generation algorithm and adjust the order of generation. This allows the generation unit to adjust the order of generation based on the relevance of photos and videos. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of photos and videos into the generation AI, and the generation AI can adjust the order of generation based on that data.

[0052] The generation unit can adjust the use of technical terminology in the generated video according to the user's level of expertise during generation. The generation unit evaluates the level of expertise based on, for example, the user's occupation or past usage history. For example, if the user has technical expertise, the generation unit can generate a video using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate a video using concise and easy-to-understand terminology. Furthermore, the generation unit can determine the user's level of expertise based on the user's past generation results and use appropriate terminology. This allows the generation unit to adjust the use of technical terminology in the generated video according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI, and the generation AI can select appropriate terminology based on that data.

[0053] The providing unit can select the optimal delivery method by referring to the user's past viewing history when providing the content. The providing unit, for example, stores and analyzes the user's past viewing history in a database. For example, the providing unit can select the optimal delivery method based on the format of videos the user has previously viewed. The providing unit can also predict and suggest the format of videos the user will view during a specific time period based on the user's past viewing history. Furthermore, the providing unit can analyze the content of videos the user has previously viewed and select the optimal delivery method. This allows the providing unit to select the optimal delivery method based on the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past viewing history data into AI, and the AI ​​can select the optimal delivery method based on the data.

[0054] The providing unit can customize the content provided according to the user's current task when providing the content. The providing unit, for example, stores and analyzes the user's current task in a database. For example, when the user is working, the providing unit can provide a short, to-the-point video. When the user is relaxing, the providing unit can provide a longer video with detailed explanations. Furthermore, when the user is exercising, the providing unit can provide a video with visually stimulating effects. This allows the providing unit to customize the content provided according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into AI, and the AI ​​can customize the content provided based on that data.

[0055] The providing unit can select the optimal delivery method based on the user's device information during delivery. The providing unit, for example, acquires the user's device information and selects the optimal delivery method based on the device type and OS version. For example, if the user is using a smartphone, a video tailored to the screen size can be provided. Also, if the user is using a tablet, a video optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a concise and highly visible video can be provided. This allows the providing unit to select the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal delivery method based on that data.

[0056] The providing unit can select the optimal delivery method by taking into account the user's device information when providing the information. For example, the providing unit acquires the user's device information and selects the optimal delivery method based on the device type and OS version. For example, if the user is using a smartphone, the providing unit can provide a delivery method tailored to the screen size. Also, if the user is using a tablet, the providing unit can provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a delivery method that is concise and highly visible. This allows the providing unit to select the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal delivery method based on that data.

[0057] The providing unit can make the provided content multilingual according to the user's language setting when providing the content. The providing unit, for example, acquires the language setting of the user's device and sets the provided content based on that language. For example, the providing unit can automatically set the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the provided content in that language. This allows the providing unit to make the provided content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI, and the AI ​​can make the provided content multilingual based on that data.

[0058] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, accesses the user's social media account and analyzes the posted content and check-in information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. This allows the providing unit to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, and the AI ​​can provide related information based on the data.

[0059] During storage management, the storage unit can select the optimal management method by referring to the user's past data storage history. The storage unit, for example, stores and analyzes the user's past data storage history in a database. For example, the storage unit can prioritize and suggest storage management methods that the user has frequently used in the past. The storage unit can also predict and suggest storage management methods to be used during specific time periods based on the user's past data storage history. Furthermore, the storage unit can analyze the format of data previously stored by the user and select the optimal storage management method. This allows the storage unit to select the optimal management method based on the user's past data storage history. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's past data storage history into AI, which can then select the optimal management method based on that data.

[0060] During storage management, the storage unit can adjust the storage capacity according to the user's current data volume. For example, the storage unit monitors the user's data volume in real time and adjusts the storage capacity as needed. For example, if the user is storing a large amount of data, the storage capacity can be automatically expanded. Also, if the user is storing a small amount of data, the storage capacity can be automatically reduced. Furthermore, the storage unit can analyze the user's data storage patterns and suggest the optimal storage capacity. This allows the storage unit to adjust the storage capacity according to the user's current data volume. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's data volume data into AI, and the AI ​​can adjust the storage capacity based on that data.

[0061] During storage management, the storage unit can determine storage priorities based on the importance of the user's data. The storage unit, for example, evaluates the importance of the user's data and prioritizes saving important data. For example, the storage unit can prioritize saving important data and back it up. The storage unit can also simply save everyday data. Furthermore, the storage unit can prioritize allocating storage capacity to important data designated by the user. This allows the storage unit to determine storage priorities based on the importance of the user's data. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input data on the importance of the user's data into AI, and the AI ​​can determine storage priorities based on that data.

[0062] During storage management, the storage unit can select the optimal storage method by taking into account the user's geographical location information. The storage unit, for example, acquires the user's GPS data or IP address and selects the optimal storage method based on the geographical location information. For example, if the user is traveling, the optimal storage method can be selected based on the geographical location information of the travel destination. Also, if the user is participating in a specific event, the optimal storage method can be selected based on the geographical location information of the event. Furthermore, if the user is at home, the optimal storage method can be selected based on the geographical location information around the home. This allows the storage unit to select the optimal storage method based on the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's geographical location information into AI, and the AI ​​can select the optimal storage method based on that data.

[0063] During storage management, the storage unit can analyze the user's social media activity and suggest storage options. For example, the storage unit can access the user's social media account and analyze posts and check-in information. For example, the storage unit can prioritize storing data related to locations where the user has checked in on social media. The storage unit can also analyze the user's social media posts and save related data. Furthermore, the storage unit can save related data based on the activities of the user's friends on social media. This allows the storage unit to suggest storage options based on the user's social media activity. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's social media activity data into AI, which can then suggest storage options based on that data.

[0064] During storage management, the storage unit can propose storage means based on the importance of the user's data. The storage unit, for example, evaluates the importance of the user's data and prioritizes storing important data. For example, the storage unit can prioritize storing important data and back it up. The storage unit can also simply store everyday data. Furthermore, the storage unit can prioritize allocating storage capacity to important data designated by the user. This allows the storage unit to propose storage means based on the importance of the user's data. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input data on the importance of the user's data into AI, and the AI ​​can propose storage means based on that data.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, it can preferentially suggest upload methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest the upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can analyze the formats of photos and videos that the user has uploaded in the past and select the optimal upload method. This allows the reception unit to select the optimal upload method based on the user's past upload history.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photos and videos. For example, the analysis unit can perform a detailed analysis on important photos and videos designated by the user. The analysis unit can also perform a concise analysis on everyday photos and videos. Furthermore, the analysis unit can perform a detailed analysis on important photos and videos designated by the user. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the photos and videos.

[0068] The generation unit can apply different generation algorithms depending on the category of the photo or video during generation. For example, a landscape analysis algorithm can be applied to travel photos to generate a video. The generation unit can also apply a face recognition algorithm to a video of a birthday party to generate a video. Furthermore, the generation unit can apply a motion analysis algorithm to photos of a sporting event to generate a video. This allows the generation unit to apply different generation algorithms depending on the category of the photo or video.

[0069] The providing unit can select the optimal delivery method by referring to the user's past viewing history at the time of delivery. For example, the optimal delivery method can be selected based on the format of videos the user has viewed in the past. The providing unit can also predict and suggest the format of videos that the user will view in a specific time period based on the user's past viewing history. Furthermore, the providing unit can analyze the content of videos the user has viewed in the past and select the optimal delivery method. This allows the providing unit to select the optimal delivery method based on the user's past viewing history.

[0070] When managing storage, the storage unit can select the optimal management method by referring to the user's past data storage history. For example, the user's past data storage history can be stored in a database and analyzed. The storage unit can also preferentially suggest storage management methods that the user has frequently used in the past. Furthermore, the storage unit can predict and suggest storage management methods to be used during specific time periods based on the user's past data storage history. This allows the storage unit to select the optimal management method based on the user's past data storage history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit accepts photo or video uploads from users. For example, users can upload photos or videos taken with their smartphones via an application. The reception unit temporarily stores the uploaded data and passes it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the uploaded photos and videos. For example, the generation AI can use image recognition technology to understand the content of the photos and analyze the content of the videos. The analysis unit can also use natural language processing technology to analyze the audio and text of the videos. Step 3: The generation unit generates a storytelling video based on the photos and videos analyzed by the analysis unit. For example, the generation AI automatically generates a storytelling video for a specific day based on the analyzed information. The generation unit passes the generated video to the provision unit. Step 4: The providing unit provides the generated video to the user. For example, the user can play the generated video within the app. The providing unit can also provide a link to share the generated video on social media or the like. Step 5: The storage unit stores the uploaded photos and videos and provides them to the analysis and generation units as needed. For example, the storage unit can use cloud storage to efficiently store large amounts of data.

[0073] (Example 2) A system according to an embodiment of the present invention is a free service available to users, utilizing a generation AI and a nationwide data center cloud. This system allows users to upload photos and videos taken with their smartphones via an application, and the generation AI automatically generates a storytelling video of a specific day. For example, users can upload photos and videos by specifying the date of a special event, such as a family trip or a birthday party. The generation AI analyzes the uploaded photos and videos and automatically generates a storytelling video of the specific day. The generated videos are designed for easy viewing by users and can also be shared on social media, allowing users to easily reminisce about memories of a specific day. This system allows users to easily reminisce about memories of a specific day. For example, users can reminisce about special events, such as family trips or birthday parties, through videos automatically generated by the generation AI. Users can also share the memories with others by sharing the generated videos on social media. Furthermore, because this system utilizes a nationwide data center cloud, users do not need to worry about storage, even when uploading a large number of photos and videos. This allows users to use the service with peace of mind.

[0074] A storytelling video generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit accepts uploads of photos or videos from users. For example, users can upload photos or videos taken with their smartphones via an application. The reception unit temporarily stores the uploaded data and passes it to the analysis unit. The analysis unit analyzes the uploaded photos or videos using a generation AI. For example, the generation AI can understand the content of the photos and analyze the content of the videos using image recognition technology. The analysis unit can also analyze the audio and text of the videos using natural language processing technology. The generation unit generates a storytelling video based on the photos and videos analyzed by the analysis unit. For example, the generation AI automatically generates a storytelling video for a specific day based on the analyzed information. The generation unit passes the generated video to the provision unit. The provision unit provides the generated video to a user. For example, the user can play the generated video within the app. The provision unit can also provide a link for sharing the generated video on social media or other platforms. The storage unit stores uploaded photos and videos and provides them to the analysis unit and generation unit as needed. For example, the storage unit can efficiently store large amounts of data using cloud storage. This allows the storytelling video generation system according to the embodiment to analyze photos and videos uploaded by users and automatically generate and provide storytelling videos.

[0075] The reception unit can accept a user's request to upload photos and videos by specifying a specific date. The reception unit, for example, accepts a user's request to specify a specific date using calendar input or text input. For example, a user can upload photos and videos by specifying the date of a special event such as a family trip or a birthday party. This allows the user to easily upload photos and videos related to a specific date. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input a user-specified date into AI, which can then automatically select photos and videos related to that date.

[0076] The analysis unit can understand the content of uploaded photos and videos and extract information for generating a storytelling-type video for a specific day. The analysis unit can understand the content of uploaded photos and videos, for example, using a generation AI. For example, the generation AI can analyze the content of photos and understand the content of videos using image recognition technology. The analysis unit can also analyze the audio and text of videos using natural language processing technology. Furthermore, the analysis unit can extract important scenes from videos using keyframe extraction technology. For example, the analysis unit can extract particularly important scenes from videos and extract information for generating a storytelling-type video based on the extracted scenes. This allows the analysis unit to understand the content of uploaded photos and videos and extract information for generating a storytelling-type video for a specific day. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input uploaded photos and videos into the generation AI, which can then analyze the content and extract information.

[0077] The generation unit can generate a storytelling-type video for a specific day based on the extracted information. The generation unit, for example, uses a generation AI to generate a storytelling-type video for a specific day based on the extracted information. For example, the generation AI can automatically generate a storytelling-type video for a specific day based on the information extracted by the analysis unit. The generation unit converts the generated video into a format that can be viewed by a user and passes it to the provision unit. This allows the generation unit to generate a storytelling-type video for a specific day based on the extracted information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the extracted information into the generation AI, and the generation AI can generate a video based on the information.

[0078] The providing unit can provide the generated video so that the user can view it. The providing unit, for example, provides a function for playing the generated video within an app. For example, the user can play the generated video within the app. The providing unit can also provide a link for sharing the generated video on a social networking site or the like. For example, the user can share the generated video on a social networking site. This allows the providing unit to provide the generated video so that the user can view it. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the generated video to AI, which can convert the video into an optimal format and provide it.

[0079] The storage unit can store uploaded photos or videos and provide them to the analysis unit or generation unit as needed. The storage unit can store uploaded photos or videos using, for example, cloud storage. For example, the storage unit can use cloud storage to efficiently store large amounts of data. The storage unit also has a function of providing the stored data to the analysis unit or generation unit. For example, the storage unit can quickly provide data required by the analysis unit and provide data required by the generation unit in an appropriate format. This allows the storage unit to store uploaded photos or videos and provide them to the analysis unit or generation unit as needed. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the stored data into AI, which can convert the data into an optimal format and provide it.

[0080] The reception unit can estimate the user's emotion and adjust the timing of uploading based on the estimated user's emotion. The reception unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. The reception unit can also adjust the timing of uploading based on the estimated user's emotion. For example, if the user is feeling stressed, the upload procedure can be simplified and completed quickly. Furthermore, if the user is relaxed, detailed upload options can be provided and a customizable upload method can be suggested. This allows the reception unit to adjust the timing of uploading based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data into AI, and the AI ​​may adjust the timing of uploading based on that data.

[0081] The reception unit can analyze the user's past upload history and select the optimal upload method. The reception unit, for example, stores and analyzes the user's past upload history in a database. For example, the reception unit can prioritize and suggest upload methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit analyzes the formats of photos and videos previously uploaded by the user and selects the optimal upload method. For example, the reception unit can suggest the optimal upload method based on the formats of photos and videos previously uploaded by the user. This allows the reception unit to select the optimal upload method based on the user's past upload history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history into AI, and the AI ​​can select the optimal upload method based on the data.

[0082] The reception unit can perform filtering based on the user's current events or areas of interest when uploading. The reception unit can, for example, refer to the user's calendar information or social media posts and perform filtering based on the user's current events or areas of interest. For example, the reception unit can prioritize uploading photos and videos related to events in which the user is currently participating. The reception unit can also filter and upload related photos and videos based on the user's areas of interest (travel, sports, etc.). Furthermore, the reception unit can refer to the user's calendar information and upload photos and videos related to specific events. This allows the reception unit to perform filtering based on the user's current events and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's calendar information and social media posts into AI, and the AI ​​can perform filtering based on that data.

[0083] The reception unit can select the optimal upload method according to the user's input method when uploading. For example, the reception unit provides a function that automatically selects and uploads related photos when the user simply voice-inputs, "Upload photos from my family trip." For example, the reception unit can use voice recognition technology to convert the user's voice input into text and automatically select and upload related photos and videos. The reception unit can also provide a function that automatically uploads photos and videos taken on a specific date when the user inputs a specific date. Furthermore, the reception unit provides a function that automatically selects and uploads the most suitable images when the user selects images. This allows the reception unit to select the optimal upload method according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without AI. For example, the reception unit can input the user's voice input or text input into AI, and the AI ​​can select the optimal upload method based on that data.

[0084] The reception unit can estimate the user's emotions and determine the priority of photos and videos to be uploaded based on the estimated user's emotions. The reception unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit can determine the priority of photos and videos to be uploaded based on the estimated user's emotions. For example, if the user is emotional, photos and videos capturing emotional moments can be prioritized for upload. Also, if the user is having fun, photos and videos capturing happy moments can be prioritized for upload. This allows the reception unit to determine the priority of photos and videos to be uploaded based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data into AI, and the AI ​​may determine the priority of photos and videos to be uploaded based on the data.

[0085] When uploading, the reception unit can prioritize uploading highly relevant data taking into account the user's geographical location information. The reception unit, for example, acquires the user's GPS data or IP address and prioritizes uploading highly relevant data based on the geographical location information. For example, if the user is traveling, relevant photos and videos can be prioritized to be uploaded based on the geographical location information of the travel destination. Also, if the user is participating in a specific event, relevant photos and videos can be prioritized to be uploaded based on the geographical location information of the event. Furthermore, if the user is at home, relevant photos and videos can be prioritized to be uploaded based on the geographical location information around the home. This allows the reception unit to prioritize uploading highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can select and upload highly relevant data based on that data.

[0086] The reception unit can analyze the user's social media activity and upload relevant data at the time of uploading. The reception unit, for example, accesses the user's social media account and analyzes the content of posts and check-in information. For example, the reception unit can prioritize uploading photos and videos related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and upload relevant photos and videos. Furthermore, the reception unit can upload relevant photos and videos based on the activities of the user's friends on social media. This allows the reception unit to upload relevant data based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can select and upload relevant data based on that data.

[0087] The reception unit can customize the upload method by reflecting the user's past feedback when uploading. The reception unit, for example, stores the user's past feedback in a database and analyzes it. For example, the reception unit can suggest an optimal upload method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific upload method based on the user's past feedback. Furthermore, the reception unit analyzes the user's past feedback and customizes the upload method. For example, the reception unit can change settings according to the user's preferences and customize the upload method. This allows the reception unit to customize the upload method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, and the AI ​​can customize the upload method based on the data.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide an analysis result that progresses at a leisurely pace. On the other hand, if the user is in a hurry, the generation AI can provide a concise analysis result that focuses on the main points. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit may input user emotion data into the generation AI, and the generation AI may adjust the method of analysis expression based on that data.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo or video. The analysis unit, for example, performs a detailed analysis on important photos or videos designated by the user. For example, the analysis unit can perform a detailed analysis on photos or videos of important events. The analysis unit can also perform a concise analysis on everyday photos and videos. Furthermore, the analysis unit can perform a detailed analysis on important photos and videos designated by the user. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the photo or video. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input importance data of the photo or video into the generation AI, and the generation AI can adjust the level of detail of the analysis based on that data.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the photo or video. For example, the analysis unit can apply a scenery analysis algorithm to travel photos. For example, the analysis unit can analyze the travel photos by applying a scenery analysis algorithm. The analysis unit can also apply a face recognition algorithm to a birthday party video. For example, the analysis unit can analyze the birthday party video by applying a face recognition algorithm. Furthermore, the analysis unit can apply a motion analysis algorithm to photos of a sporting event. For example, the analysis unit can analyze the sporting event photos by applying a motion analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the photo or video. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input category data of the photo or video into the generation AI, and the generation AI can apply an appropriate analysis algorithm based on that data.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, stores the user's past analysis results in a database and analyzes them. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. Furthermore, the analysis unit can analyze the user's past analysis results and improve the analysis accuracy. This allows the analysis unit to improve the analysis accuracy by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the analysis accuracy based on that data.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit can also adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the generation AI can provide a short, concise analysis result. On the other hand, if the user is relaxed, the generation AI can provide a longer analysis result including detailed explanations. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit may input user emotion data into the generation AI, and the generation AI may adjust the length of analysis based on that data.

[0093] During analysis, the analysis unit can determine the analysis priority based on the time when the photos and videos were taken. The analysis unit obtains the time when the photos and videos were taken based on, for example, a timestamp in the metadata or user input. For example, the analysis unit can prioritize analyzing recently taken photos and videos. The analysis unit can also prioritize analyzing photos and videos taken on the date of a specific event. Furthermore, the analysis unit can determine the analysis priority based on the time when the photos and videos were taken specified by the user. This allows the analysis unit to determine the analysis priority based on the time when the photos and videos were taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the photos and videos were taken into the generation AI, and the generation AI can determine the analysis priority based on that data.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of photos and videos. The analysis unit evaluates relevance, for example, based on photos and videos related to a common tag or the same event. For example, the analysis unit can prioritize analysis of highly relevant photos and videos. The analysis unit can also adjust the order of analysis based on relevance specified by a user. Furthermore, the analysis unit can automatically determine relevance using an analysis algorithm and adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of photos and videos. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input relevance data of photos and videos into a generation AI, and the generation AI can adjust the order of analysis based on that data.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the level of expertise based on, for example, the user's occupation and past usage history. For example, if the user has technical expertise, the analysis unit can provide analysis results using detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results using concise and easy-to-understand terms. Furthermore, the analysis unit can determine the user's level of expertise based on the user's past analysis results and use appropriate terms. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can select appropriate terms based on that data.

[0096] The generation unit can estimate the user's emotions and adjust the expression style of the generated video based on the estimated user's emotions. The generation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit adjusts the expression style of the generated video based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate a video that progresses at a leisurely pace. On the other hand, if the user is in a hurry, the generation AI can generate a video that emphasizes the shortest route. This allows the generation unit to adjust the expression style of the generated video according to the user's emotions. Emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, which may then adjust the video's expression method based on that data.

[0097] The generation unit can adjust the level of detail of the generation based on the importance of the photo or video during generation. For example, the generation unit generates a detailed video for an important photo or video designated by a user. For example, the generation unit can generate a detailed video for a photo or video of an important event. The generation unit can also generate a concise video for an everyday photo or video. Furthermore, the generation unit can generate a detailed video for an important photo or video designated by a user. This allows the generation unit to adjust the level of detail of the generation based on the importance of the photo or video. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the photo or video to the generation AI, and the generation AI can adjust the level of detail of the generation based on that data.

[0098] The generation unit can apply different generation algorithms depending on the category of the photo or video during generation. For example, the generation unit can generate a video by applying a scenery analysis algorithm to travel photos. For example, the generation unit can generate a video by applying a scenery analysis algorithm to travel photos. The generation unit can also generate a video by applying a face recognition algorithm to a birthday party video. For example, the generation unit can generate a video by applying a face recognition algorithm to a birthday party video. Furthermore, the generation unit can generate a video by applying a motion analysis algorithm to photos of a sporting event. For example, the generation unit can generate a video by applying a motion analysis algorithm to photos of a sporting event. This allows the generation unit to apply different generation algorithms depending on the category of the photo or video. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the photo or video into the generation AI, and the generation AI can apply an appropriate generation algorithm based on the data.

[0099] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, stores the user's past generation results in a database and analyzes them. For example, the generation unit can adjust the generation algorithm based on the user's past generation results. The generation unit can also learn specific patterns from the user's past generation results and improve the generation accuracy. Furthermore, the generation unit can analyze the user's past generation results and improve the generation accuracy. In this way, the generation unit can improve the generation accuracy by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI, and the generation AI can improve the generation accuracy based on that data.

[0100] The generation unit can estimate the user's emotion and adjust the length of the generated video based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit adjusts the length of the generated video based on the estimated user's emotion. For example, if the user is in a hurry, the generation AI can generate a short, to-the-point video. On the other hand, if the user is relaxed, the generation AI can generate a longer video with detailed explanations. This allows the generation unit to adjust the length of the generated video based on the user's emotion. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, and the generation AI may adjust the length of the video based on that data.

[0101] At the time of generation, the generation unit can determine the generation priority based on the shooting date of the photos and videos. The generation unit acquires the shooting date of the photos and videos based on, for example, a timestamp in the metadata or a user input. For example, the generation unit can prioritize generating photos and videos taken recently. The generation unit can also prioritize generating photos and videos taken on the date of a specific event. Furthermore, the generation unit can determine the generation priority based on the shooting date specified by the user. This allows the generation unit to determine the generation priority based on the shooting date of the photos and videos. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input shooting date data of the photos and videos into the generation AI, and the generation AI can determine the generation priority based on that data.

[0102] The generation unit can adjust the order of generation based on the relevance of photos and videos during generation. The generation unit evaluates the relevance, for example, based on photos and videos related to a common tag or the same event. For example, the generation unit can prioritize generating highly relevant photos and videos. The generation unit can also adjust the order of generation based on the relevance specified by the user. Furthermore, the generation unit can automatically determine the relevance using a generation algorithm and adjust the order of generation. This allows the generation unit to adjust the order of generation based on the relevance of photos and videos. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of photos and videos into the generation AI, and the generation AI can adjust the order of generation based on that data.

[0103] The generation unit can adjust the use of technical terminology in the generated video according to the user's level of expertise during generation. The generation unit evaluates the level of expertise based on, for example, the user's occupation or past usage history. For example, if the user has technical expertise, the generation unit can generate a video using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate a video using concise and easy-to-understand terminology. Furthermore, the generation unit can determine the user's level of expertise based on the user's past generation results and use appropriate terminology. This allows the generation unit to adjust the use of technical terminology in the generated video according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI, and the generation AI can select appropriate terminology based on that data.

[0104] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit adjusts the video presentation method based on the estimated user's emotions. For example, if the user is relaxed, a video progressing at a leisurely pace can be provided. On the other hand, if the user is in a hurry, a concise video that focuses on the main points can be provided. This allows the providing unit to adjust the video presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input user emotional data into the AI, which can then adjust how the video is provided based on that data.

[0105] The providing unit can select the optimal delivery method by referring to the user's past viewing history when providing the content. The providing unit, for example, stores and analyzes the user's past viewing history in a database. For example, the providing unit can select the optimal delivery method based on the format of videos the user has previously viewed. The providing unit can also predict and suggest the format of videos the user will view during a specific time period based on the user's past viewing history. Furthermore, the providing unit can analyze the content of videos the user has previously viewed and select the optimal delivery method. This allows the providing unit to select the optimal delivery method based on the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past viewing history data into AI, and the AI ​​can select the optimal delivery method based on the data.

[0106] The providing unit can customize the content provided according to the user's current task when providing the content. The providing unit, for example, stores and analyzes the user's current task in a database. For example, when the user is working, the providing unit can provide a short, to-the-point video. When the user is relaxing, the providing unit can provide a longer video with detailed explanations. Furthermore, when the user is exercising, the providing unit can provide a video with visually stimulating effects. This allows the providing unit to customize the content provided according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into AI, and the AI ​​can customize the content provided based on that data.

[0107] The providing unit can select the optimal delivery method based on the user's device information during delivery. The providing unit, for example, acquires the user's device information and selects the optimal delivery method based on the device type and OS version. For example, if the user is using a smartphone, a video tailored to the screen size can be provided. Also, if the user is using a tablet, a video optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a concise and highly visible video can be provided. This allows the providing unit to select the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal delivery method based on that data.

[0108] The providing unit can estimate the user's emotion and adjust the video presentation procedure based on the estimated user's emotion. The providing unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the providing unit adjusts the video presentation procedure based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide simple, highly visible presentation procedures. On the other hand, if the user is relaxed, the providing unit can provide presentation procedures including detailed information. This allows the providing unit to adjust the video presentation procedure according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input user emotional data into the AI, which can then adjust the video providing procedure based on that data.

[0109] The providing unit can select the optimal delivery method by taking into account the user's device information when providing the information. For example, the providing unit acquires the user's device information and selects the optimal delivery method based on the device type and OS version. For example, if the user is using a smartphone, the providing unit can provide a delivery method tailored to the screen size. Also, if the user is using a tablet, the providing unit can provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a delivery method that is concise and highly visible. This allows the providing unit to select the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal delivery method based on that data.

[0110] The providing unit can make the provided content multilingual according to the user's language setting when providing the content. The providing unit, for example, acquires the language setting of the user's device and sets the provided content based on that language. For example, the providing unit can automatically set the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the provided content in that language. This allows the providing unit to make the provided content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI, and the AI ​​can make the provided content multilingual based on that data.

[0111] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, accesses the user's social media account and analyzes the posted content and check-in information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. This allows the providing unit to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, and the AI ​​can provide related information based on the data.

[0112] The storage unit can estimate a user's emotion and adjust the storage management method based on the estimated user's emotion. The storage unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the storage unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The storage unit can also estimate the user's emotion using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the storage unit can adjust the storage management method based on the estimated user's emotion. For example, if the user is stressed, a simple and intuitive storage management method can be provided. On the other hand, if the user is relaxed, detailed storage management options can be provided. This allows the storage unit to adjust the storage management method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input user emotional data into the AI, which can then adjust the storage management method based on that data.

[0113] During storage management, the storage unit can select the optimal management method by referring to the user's past data storage history. The storage unit, for example, stores and analyzes the user's past data storage history in a database. For example, the storage unit can prioritize and suggest storage management methods that the user has frequently used in the past. The storage unit can also predict and suggest storage management methods to be used during specific time periods based on the user's past data storage history. Furthermore, the storage unit can analyze the format of data previously stored by the user and select the optimal storage management method. This allows the storage unit to select the optimal management method based on the user's past data storage history. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's past data storage history into AI, which can then select the optimal management method based on that data.

[0114] During storage management, the storage unit can adjust the storage capacity according to the user's current data volume. For example, the storage unit monitors the user's data volume in real time and adjusts the storage capacity as needed. For example, if the user is storing a large amount of data, the storage capacity can be automatically expanded. Also, if the user is storing a small amount of data, the storage capacity can be automatically reduced. Furthermore, the storage unit can analyze the user's data storage patterns and suggest the optimal storage capacity. This allows the storage unit to adjust the storage capacity according to the user's current data volume. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's data volume data into AI, and the AI ​​can adjust the storage capacity based on that data.

[0115] During storage management, the storage unit can determine storage priorities based on the importance of the user's data. The storage unit, for example, evaluates the importance of the user's data and prioritizes saving important data. For example, the storage unit can prioritize saving important data and back it up. The storage unit can also simply save everyday data. Furthermore, the storage unit can prioritize allocating storage capacity to important data designated by the user. This allows the storage unit to determine storage priorities based on the importance of the user's data. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input data on the importance of the user's data into AI, and the AI ​​can determine storage priorities based on that data.

[0116] The storage unit can estimate a user's emotion and determine storage priorities based on the estimated user's emotion. The storage unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the storage unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The storage unit can also estimate the user's emotion using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the storage unit determines storage priorities based on the estimated user's emotion. For example, if the user is emotional, data capturing emotional moments can be preferentially saved. Also, if the user is having fun, data capturing enjoyable moments can be preferentially saved. This allows the storage unit to determine storage priorities according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input user emotion data into AI, which may then determine storage priorities based on that data.

[0117] During storage management, the storage unit can select the optimal storage method by taking into account the user's geographical location information. The storage unit, for example, acquires the user's GPS data or IP address and selects the optimal storage method based on the geographical location information. For example, if the user is traveling, the optimal storage method can be selected based on the geographical location information of the travel destination. Also, if the user is participating in a specific event, the optimal storage method can be selected based on the geographical location information of the event. Furthermore, if the user is at home, the optimal storage method can be selected based on the geographical location information around the home. This allows the storage unit to select the optimal storage method based on the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's geographical location information into AI, and the AI ​​can select the optimal storage method based on that data.

[0118] During storage management, the storage unit can analyze the user's social media activity and suggest storage options. For example, the storage unit can access the user's social media account and analyze posts and check-in information. For example, the storage unit can prioritize storing data related to locations where the user has checked in on social media. The storage unit can also analyze the user's social media posts and save related data. Furthermore, the storage unit can save related data based on the activities of the user's friends on social media. This allows the storage unit to suggest storage options based on the user's social media activity. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's social media activity data into AI, which can then suggest storage options based on that data.

[0119] During storage management, the storage unit can propose storage means based on the importance of the user's data. The storage unit, for example, evaluates the importance of the user's data and prioritizes storing important data. For example, the storage unit can prioritize storing important data and back it up. The storage unit can also simply store everyday data. Furthermore, the storage unit can prioritize allocating storage capacity to important data designated by the user. This allows the storage unit to propose storage means based on the importance of the user's data. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input data on the importance of the user's data into AI, and the AI ​​can propose storage means based on that data. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and storage unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing a user to upload photos and videos taken with their smartphone via an application. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photos and videos using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a storytelling-type video based on the analyzed information. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the generated video to the user. The storage unit is implemented, for example, by the database 24 of the data processing device 12, and stores the uploaded photos and videos. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and storage unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and allows a user to upload photos and videos taken with a smartphone via an application. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos and videos using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a storytelling-type video based on the analyzed information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated video to the user. The storage unit is implemented, for example, by the database 24 of the data processing device 12 and stores the uploaded photos and videos. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, provision unit, and storage unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows a user to upload photos and videos taken with a smartphone via an application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photos and videos using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a storytelling-type video based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated video to the user. The storage unit is realized, for example, by the database 24 of the data processing device 12, and stores the uploaded photos and videos. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and storage unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, and allows a user to upload photos and videos taken with a smartphone via an application. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photos and videos using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a storytelling-type video based on the analyzed information. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated video to the user. The storage unit is implemented, for example, by the database 24 of the data processing device 12, and stores the uploaded photos and videos.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, it can preferentially suggest upload methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest the upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can analyze the formats of photos and videos that the user has uploaded in the past and select the optimal upload method. This allows the reception unit to select the optimal upload method based on the user's past upload history.

[0122] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photos and videos. For example, the analysis unit can perform a detailed analysis on important photos and videos designated by the user. The analysis unit can also perform a concise analysis on everyday photos and videos. Furthermore, the analysis unit can perform a detailed analysis on important photos and videos designated by the user. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the photos and videos.

[0123] The generation unit can apply different generation algorithms depending on the category of the photo or video during generation. For example, a landscape analysis algorithm can be applied to travel photos to generate a video. The generation unit can also apply a face recognition algorithm to a video of a birthday party to generate a video. Furthermore, the generation unit can apply a motion analysis algorithm to photos of a sporting event to generate a video. This allows the generation unit to apply different generation algorithms depending on the category of the photo or video.

[0124] The providing unit can select the optimal delivery method by referring to the user's past viewing history at the time of delivery. For example, the optimal delivery method can be selected based on the format of videos the user has viewed in the past. The providing unit can also predict and suggest the format of videos that the user will view in a specific time period based on the user's past viewing history. Furthermore, the providing unit can analyze the content of videos the user has viewed in the past and select the optimal delivery method. This allows the providing unit to select the optimal delivery method based on the user's past viewing history.

[0125] When managing storage, the storage unit can select the optimal management method by referring to the user's past data storage history. For example, the user's past data storage history can be stored in a database and analyzed. The storage unit can also preferentially suggest storage management methods that the user has frequently used in the past. Furthermore, the storage unit can predict and suggest storage management methods to be used during specific time periods based on the user's past data storage history. This allows the storage unit to select the optimal management method based on the user's past data storage history.

[0126] The reception unit can estimate the user's emotions and adjust the timing of uploading based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology. Alternatively, the user's emotions can be estimated using voice analysis technology. Furthermore, the timing of uploading can be adjusted based on the estimated user's emotions. For example, if the user is feeling stressed, the uploading procedure can be simplified and completed quickly. Alternatively, if the user is relaxed, detailed upload options can be provided and a customizable upload method can be suggested. This allows the reception unit to adjust the timing of uploading according to the user's emotions.

[0127] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology. The user's emotions can also be estimated using voice analysis technology. Furthermore, the way the analysis is presented can be adjusted based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide analysis results that proceed at a leisurely pace. On the other hand, if the user is in a hurry, the generation AI can provide concise analysis results that focus on the main points. This allows the analysis unit to adjust the way the analysis is presented depending on the user's emotions.

[0128] The generation unit can estimate the user's emotions and adjust the expression method of the generated video based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology. The user's emotions can also be estimated using voice analysis technology. Furthermore, the expression method of the generated video can be adjusted based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate a video that progresses at a leisurely pace. On the other hand, if the user is in a hurry, the generation AI can generate a video that emphasizes the shortest route. This allows the generation unit to adjust the expression method of the generated video according to the user's emotions.

[0129] The providing unit can estimate the user's emotions and adjust the video providing method based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology. The user's emotions can also be estimated using voice analysis technology. Furthermore, the video providing method can be adjusted based on the estimated user's emotions. For example, if the user is relaxed, a video that progresses at a leisurely pace can be provided. On the other hand, if the user is in a hurry, a concise video that hits the main points can be provided. This allows the providing unit to adjust the video providing method according to the user's emotions.

[0130] The storage unit can estimate a user's emotion and adjust the storage management method based on the estimated user's emotion. For example, the user's emotion can be estimated using facial expression recognition technology. Alternatively, the user's emotion can be estimated using voice analysis technology. Furthermore, the storage management method can be adjusted based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and intuitive storage management method can be provided. Alternatively, if the user is relaxed, detailed storage management options can be provided. This allows the storage unit to adjust the storage management method according to the user's emotion.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit accepts photo or video uploads from users. For example, users can upload photos or videos taken with their smartphones via an application. The reception unit temporarily stores the uploaded data and passes it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the uploaded photos and videos. For example, the generation AI can use image recognition technology to understand the content of the photos and analyze the content of the videos. The analysis unit can also use natural language processing technology to analyze the audio and text of the videos. Step 3: The generation unit generates a storytelling video based on the photos and videos analyzed by the analysis unit. For example, the generation AI automatically generates a storytelling video for a specific day based on the analyzed information. The generation unit passes the generated video to the provision unit. Step 4: The providing unit provides the generated video to the user. For example, the user can play the generated video within the app. The providing unit can also provide a link to share the generated video on social media or the like. Step 5: The storage unit stores the uploaded photos and videos and provides them to the analysis and generation units as needed. For example, the storage unit can use cloud storage to efficiently store large amounts of data.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0147] In the 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0151] 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives uploads of photos or videos from users; an analysis unit that analyzes the photo or video accepted by the acceptance unit; a generation unit that generates a storytelling video based on the photos or videos analyzed by the analysis unit; a providing unit that provides the video generated by the generating unit to a user; a storage unit for storing the uploaded data; A system characterized by:

2. The reception unit Allows users to upload photos and videos for a specific date 2. The system of claim 1.

3. The analysis unit Understand the content of uploaded photos and videos and extract information to generate storytelling videos for a specific day 2. The system of claim 1.

4. The generation unit Generate a storytelling video for a specific day based on the extracted information 2. The system of claim 1.

5. The providing unit Providing the generated video for users to watch 2. The system of claim 1.

6. The storage unit Store uploaded photos or videos and provide them to the analysis or generation department as needed 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust upload timing based on the estimated user emotions 2. The system of claim 1.

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

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