System

Abstract

An object of the system according to the embodiment is to allow anyone to easily create and transmit a Vlog.SOLUTION: A system includes an imaging unit, an analysis unit, an editing unit, and a transmission unit. The imaging unit captures an image. The analysis unit analyzes the video captured by the imaging unit. The editing unit edits the video analyzed by the analysis unit. The transmitter transmits the video edited by the editor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating and publishing a vlog required time and knowledge, making it difficult for everyone to do.

[0005] The system according to the embodiment aims to enable anyone to easily create and publish a Vlog. [Means for solving the problem]

[0006] The system according to the embodiment includes a shooting unit, an analysis unit, an editing unit, and a transmission unit. The shooting unit shoots video. The analysis unit analyzes the video shot by the shooting unit. The editing unit edits the video analyzed by the analysis unit. The transmission unit transmits the video edited by the editing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows anyone to easily create and publish a Vlog. [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 Vlog creation system according to an embodiment of the present invention utilizes a generation AI and a wearable device to enable anyone to easily create and share Vlogs. The Vlog creation system allows users to easily create Vlogs without requiring specialized knowledge or effort. The system automatically captures video footage, and the generation AI edits the footage. Furthermore, a Vlog-SNS is provided that allows users to easily share the Vlogs they create. For example, in this Vlog creation system, the user wears a wearable device. This device is a badge-shaped AI wearable device with no keyboard or display. While worn by the user, the device automatically captures video footage. For example, it can capture various everyday scenes, such as while the user is taking a walk or cooking. The captured video footage is then sent to the generation AI for editing. The generation AI analyzes the video and crops or highlights necessary portions. It can also add mosaics to protect privacy and captions to enhance visual information. Furthermore, adding background music to the video allows for the creation of compelling Vlogs. Vlogs created in this way can be easily shared on the Vlog-SNS. This allows the Vlog creation system to allow users to easily create and share Vlogs without requiring any special knowledge or effort. This is expected to make Vlog sharing mainstream and lead to it becoming the next major social networking site. This allows the Vlog creation system to allow users to easily create and share Vlogs without requiring any special knowledge or effort. For example, users can quickly and accurately shoot various scenes from their daily lives, and the generation AI can automatically edit them to create an appealing Vlog. Furthermore, sharing can be easily done through the Vlog-SNS, allowing users to share specific details about their lives. This is expected to make Vlog sharing mainstream and lead to it becoming the next major social networking site.

[0029] A Vlog creation system according to an embodiment includes a filming unit, an analysis unit, an editing unit, and a transmission unit. The filming unit captures video using a wearable device worn by a user. For example, the filming unit can automatically capture various everyday scenes, such as while the user is taking a walk or cooking. The filming unit can capture video footage, live footage, recorded footage, and the like, depending on the type and format of the video. The analysis unit analyzes the video captured by the filming unit. For example, the analysis unit can analyze the video using methods such as image analysis, audio analysis, and motion analysis. The analysis unit can also crop or select necessary portions based on the content of the video. The editing unit edits the video analyzed by the analysis unit. For example, the editing unit can edit the video using methods such as cut editing, adding effects, and audio editing. The editing unit can also add mosaics to protect privacy or add captions to supplement visual information. The editing unit can also add background music to the video. The transmission unit transmits the video edited by the editing unit. For example, the transmission unit can transmit the video by posting to social media, live streaming, uploading to the cloud, etc. As a result, the Vlog creation system according to the embodiment automates the process from shooting to analyzing, editing, and transmitting the video, allowing users to easily create Vlogs.

[0030] The analysis unit can analyze the video and cut out and select necessary portions. For example, the analysis unit can automatically detect important scenes from the video and cut out necessary portions. The analysis unit can also pick out portions in which specific people appear. Furthermore, the analysis unit can select and pick out particularly noteworthy scenes from the video. This improves editing efficiency by automatically cutting out and picking out necessary portions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data into a generation AI and have the generation AI cut out and pick out necessary portions.

[0031] The editorial department can apply mosaics to protect privacy. For example, the editorial department can automatically detect the face of a specific person in a video and apply a mosaic. The editorial department can also blur portions of audio that contain specific personal information. Furthermore, the editorial department can automatically detect portions of a video that require privacy protection and apply a mosaic. This allows the user's privacy to be protected by automatically applying a mosaic for privacy protection. Some or all of the above-mentioned processing by the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input video data into a generation AI and have the generation AI apply mosaics for privacy protection.

[0032] The editing department can add subtitles to supplement visual information. For example, the editing department can display important information in the video as subtitles. The editing department can also add annotations for specific scenes in the video as subtitles. Furthermore, the editing department can add graphics or subtitles to supplement visual information in the video. This allows for automatic addition of subtitles to supplement visual information, thereby deepening understanding of the video. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video data into a generation AI and have the generation AI add subtitles to supplement visual information.

[0033] The editing department can add background music to the video. For example, the editing department can select appropriate background music according to the content of the video and add it to the video. The editing department can also adjust the genre and tempo of the background music to match the atmosphere of the video. Furthermore, the editing department can improve the quality of the video by adding background music that is appealing to viewers of the video. This allows for the automatic addition of background music to the video, making it possible to create a Vlog that is appealing to viewers. Some or all of the above-mentioned processing by the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can input video data into a generation AI and have the generation AI add background music.

[0034] The sending unit can send video to a social networking site. For example, the sending unit can automatically post edited video to a social networking site. The sending unit can also live-stream the video. Furthermore, the sending unit can upload the video to a cloud and generate a sharing link. This allows users to easily share their Vlogs by automatically sending the video to a Vlog-SNS. Some or all of the above-described processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input edited video data into a generation AI and have the generation AI post the video to a social networking site or live-stream the video.

[0035] The camera unit can automatically select an appropriate shooting angle based on the user's activity when shooting. For example, if the user is cooking, the camera unit can select an angle that emphasizes the movement of the user's hands. Furthermore, if the user is playing sports, the camera unit can select an angle that captures the dynamism of the movement. Furthermore, if the user is enjoying a landscape, the camera unit can select an angle that brings out the beauty of the background. This allows for more effective footage to be captured by selecting the optimal shooting angle based on the user's activity. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the user's activity data into a generation AI and have the generation AI select the optimal shooting angle.

[0036] When taking a photo, the photographing unit can appropriately adjust the photographing settings by referring to the user's past photographing history. For example, the photographing unit can automatically apply a filter that the user has used favorably in the past. The photographing unit can also set new photographing settings by referring to the composition of scenes photographed by the user in the past. Furthermore, the photographing unit can select optimal settings by taking into account the time of day and weather conditions when the user took photographs in the past. This allows optimal photographing settings to be automatically applied by referring to the user's past photographing history. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input past photographing data into a generation AI and have the generation AI adjust the optimal photographing settings.

[0037] The image capture unit can detect ambient light and audio levels in real time during shooting and automatically adjust appropriate shooting conditions. For example, when the ambient light is low, the image capture unit can automatically increase the ISO sensitivity to adjust brightness. Furthermore, when the ambient audio level is high, the image capture unit can adjust the microphone sensitivity to record clear audio. Furthermore, when the ambient light changes, the image capture unit can automatically adjust the white balance to maintain natural colors. This allows for real-time detection of ambient light and audio levels and automatic adjustment of optimal shooting conditions, thereby enabling higher quality video to be captured. Some or all of the above-described processing in the image capture unit may be performed using, or without, AI. For example, the image capture unit can input ambient light and audio data into a generation AI and have the generation AI adjust the optimal shooting conditions.

[0038] When taking a photo, the photographing unit can prioritize photographing highly relevant scenes by taking into account the user's geographical location information. For example, when the user is at a tourist spot, the photographing unit can prioritize photographing the famous places and scenery of that location. Furthermore, when the user is at an event venue, the photographing unit can prioritize photographing highlight scenes of the event. Furthermore, when the user is at home, the photographing unit can prioritize photographing scenes from everyday life. In this way, highly relevant scenes can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI. For example, the photographing unit can input geographical location data to a generation AI and cause the generation AI to select highly relevant scenes.

[0039] The photographing unit can analyze the user's social media activity and photograph related scenes when photographing. For example, the photographing unit can prioritize photographing locations that the user has shared on social media. The photographing unit can also photograph events or locations in which the user has expressed interest on social media. Furthermore, the photographing unit can analyze the content of the user's social media posts and photograph related scenes. In this way, by analyzing the user's social media activity, related scenes can be effectively photographed. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input social media data into a generation AI and cause the generation AI to photograph related scenes.

[0040] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. For example, the photographing unit can automatically apply a photographing style that the user previously preferred. The photographing unit can also adjust the photographing method by reflecting improvements that the user previously pointed out. Furthermore, the photographing unit can select optimal photographing settings based on the user's past feedback. This allows the photographing method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input past feedback data into a generation AI and have the generation AI customize the photographing method.

[0041] During analysis, the analysis unit can apply different analysis algorithms based on the content of the video. For example, if the video is a landscape, the analysis unit can apply a landscape analysis algorithm. Also, if the video is a person, the analysis unit can apply a face recognition algorithm. Furthermore, if the video is an event, the analysis unit can apply an event analysis algorithm. By applying different analysis algorithms based on the content of the video, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data to a generation AI and have the generation AI apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis according to the category of the video. For example, if the video is a landscape, the analysis unit can perform a detailed landscape analysis. Furthermore, if the video is of a person, the analysis unit can also analyze facial expressions and movements in detail. Furthermore, if the video is of an event, the analysis unit can analyze highlight scenes of the event in detail. This allows for more detailed analysis by adjusting the level of detail of the analysis according to the category of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can perform a similar analysis based on analysis results that the user previously preferred. The analysis unit can also improve the analysis accuracy by reflecting improvements that the user previously pointed out. Furthermore, the analysis unit can analyze the user's past analysis results and select an optimal analysis algorithm. This improves the accuracy of the analysis 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, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the video was shot. For example, if the video was shot recently, the analysis unit can prioritize analysis. Furthermore, if the video is related to a specific event, the analysis unit can also analyze based on the time of the event. Furthermore, if the video is from the past, the analysis unit can determine the analysis priority according to its importance. In this way, by determining the analysis priority based on the time when the video was shot, more important videos can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input shooting time data into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. For example, if the videos are related to a specific theme, the analysis unit can determine the order of analysis based on that theme. Furthermore, the analysis unit can prioritize analysis of videos related to the user's interests. Furthermore, if the videos are highly related to other videos, the analysis unit can adjust the order of analysis based on the relevance. Thus, by adjusting the order of analysis based on the relevance of the videos, highly related videos can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input video relevance data to a generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has expertise, the analysis unit can perform a detailed analysis. Furthermore, if the user is a beginner, the analysis unit can also perform a basic analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. This allows for more appropriate analysis by adjusting the level of detail of 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, AI, or may be performed without using AI. For example, the analysis unit can input the user's expertise data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0047] During editing, the editing department can adjust the level of editing detail based on the importance of the video. For example, the editing department can edit important scenes in detail and simplify unnecessary scenes. The editing department can also perform detailed editing to emphasize important events or occurrences. Furthermore, the editing department can adjust the level of editing detail according to the importance of the video. As a result, by adjusting the level of editing detail based on the importance of the video, more important scenes can be edited in detail. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video importance data into the generation AI and have the generation AI adjust the level of editing detail.

[0048] During editing, the editing department can apply different editing algorithms depending on the category of the video. For example, if the video is a landscape, the editing department can apply a landscape editing algorithm. Furthermore, if the video is a person, the editing department can apply a face recognition editing algorithm. Furthermore, if the video is an event, the editing department can apply an event editing algorithm. In this way, by applying different editing algorithms depending on the category of the video, the accuracy of editing is improved. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video data into a generation AI and have the generation AI apply an appropriate editing algorithm.

[0049] During editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. For example, the editing department can automatically apply an editing style that the user previously preferred. The editing department can also improve the editing accuracy by reflecting improvements that the user previously pointed out. Furthermore, the editing department can analyze the user's past editing results and select the optimal editing algorithm. This improves the accuracy of editing by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input past editing data into a generation AI and have the generation AI improve the editing accuracy.

[0050] During editing, the editing department can determine the editing priority based on the shooting date of the video. For example, the editing department can prioritize editing of recently shot video. The editing department can also prioritize editing of video related to a specific event. Furthermore, the editing department can determine the editing priority even for past video according to its importance. In this way, by determining the editing priority based on the shooting date of the video, more important video can be prioritized for editing. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input shooting date data into the generation AI and have the generation AI determine the editing priority.

[0051] During editing, the editing department can adjust the editing order based on the relevance of the videos. For example, the editing department can prioritize editing videos related to a specific theme. The editing department can also prioritize editing videos related to the user's interests. Furthermore, the editing department can prioritize editing videos that are highly relevant to other videos. In this way, by adjusting the editing order based on the relevance of the videos, highly relevant videos can be prioritized for editing. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video relevance data into the generation AI and have the generation AI adjust the editing order.

[0052] During editing, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. For example, if the user has technical expertise, the editing department can perform editing that makes extensive use of technical terminology. Furthermore, if the user is a beginner, the editing department can perform editing that uses basic terminology. Furthermore, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. This allows for more appropriate editing by adjusting the use of technical terminology in the editing depending on the user's level of expertise. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's technical expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0053] The transmission unit can select an appropriate transmission platform based on the content of the video when transmitting. For example, if the video is short, the transmission unit can select a short video platform such as Instagram (registered trademark) or TikTok (registered trademark). Furthermore, if the video is long, the transmission unit can select a long video platform such as YouTube (registered trademark) or Facebook (registered trademark). Furthermore, the transmission unit can select the optimal transmission platform according to the content of the video. By selecting the optimal transmission platform based on the content of the video, the video can be transmitted more effectively. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input video data into a generation AI and have the generation AI select a transmission platform.

[0054] When making a call, the calling unit can appropriately adjust the call settings by referring to the user's past call history. The calling unit can, for example, automatically apply call settings that the user has previously preferred. The calling unit can also set new call settings by referring to the content of calls made by the user in the past. Furthermore, the calling unit can analyze the user's past call history and select optimal call settings. This makes it possible to automatically apply optimal call settings by referring to the user's past call history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input past call data into a generation AI and have the generation AI adjust the call settings.

[0055] The transmission unit can analyze the video viewing history at the time of transmission and suggest an appropriate transmission method. For example, if a video is viewed frequently, the transmission unit can suggest a new transmission method by referring to that transmission method. Furthermore, if a video is viewed infrequently, the transmission unit can analyze the cause and suggest improvements. Furthermore, the transmission unit can suggest the optimal transmission method based on the video viewing history. In this way, the optimal transmission method can be suggested by analyzing the video viewing history. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input viewing history data into a generation AI and have the generation AI suggest a transmission method.

[0056] When transmitting, the transmission unit can prioritize transmitting highly relevant video by taking into account the user's geographical location information. For example, when the user is at a tourist spot, the transmission unit can prioritize transmitting famous places and scenery of the location. Furthermore, when the user is at an event venue, the transmission unit can prioritize transmitting highlight scenes of the event. Furthermore, when the user is at home, the transmission unit can prioritize transmitting scenes from everyday life. In this way, highly relevant video can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input geographical location data to a generation AI and cause the generation AI to transmit highly relevant video.

[0057] At the time of transmission, the transmission unit can analyze the user's social media activity and transmit related videos. For example, the transmission unit can prioritize transmission of places that the user has shared on social media. The transmission unit can also transmit events or places that the user has shown interest in on social media. Furthermore, the transmission unit can analyze the content of the user's social media posts and transmit related videos. In this way, by analyzing the user's social media activity, related videos can be transmitted effectively. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input social media data into a generation AI and cause the generation AI to transmit related videos.

[0058] The sending unit can customize the sending method by reflecting the user's past feedback when making a call. The sending unit can, for example, automatically apply a sending style that the user previously preferred. The sending unit can also adjust the sending method by reflecting improvements that the user previously pointed out. Furthermore, the sending unit can select the optimal sending method based on the user's past feedback. In this way, the sending method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input past feedback data into a generation AI and have the generation AI customize the sending method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can detect specific objects in the video and automatically add information related to that object. For example, if a building is detected in the video, the history and characteristics of the building can be displayed as captions. If an animal is detected in the video, information about the animal's species and ecology can be added. Furthermore, if a specific event is detected in the video, the background and details of that event can be added. This enriches the content of the video and deepens the viewer's understanding.

[0061] The editorial department can detect specific sounds in a video and automatically generate subtitles based on those sounds. For example, if a conversation is taking place in a video, the conversation can be displayed as subtitles in real time. Also, if specific music is playing in the video, the lyrics of that music can be displayed as subtitles. Furthermore, if environmental sounds play an important role in the video, a description of the sound can be added as subtitles. This makes it easier for viewers to understand the content of the video.

[0062] The transmitter can automatically generate hashtags based on the content of the video and add them when posting to social media. For example, if a specific location is shown in the video, a hashtag related to that location can be generated. Also, if a specific event is taking place in the video, a hashtag related to that event can be generated. Furthermore, if a specific theme is covered in the video, a hashtag related to that theme can be generated. This makes social media posting more effective and more likely to attract viewers' attention.

[0063] The camera unit can automatically switch the shooting mode based on the user's activity. For example, if the user is playing sports, the camera can switch to sports mode to capture fast-moving scenes. If the user is cooking, the camera can switch to close-up mode to emphasize the movements of the user's hands. Furthermore, if the user is enjoying the scenery, the camera can switch to wide-angle mode to capture the beauty of the background. This makes it possible to automatically select the optimal shooting mode according to the user's activity.

[0064] The shooting unit can appropriately adjust shooting settings by referring to the user's past shooting history. For example, it can automatically apply a filter that the user has used favorably in the past. It can also set new shooting settings by referring to the composition of scenes the user has photographed in the past. Furthermore, it can select optimal settings by taking into account the time of day and weather conditions when the user took photos in the past. In this way, optimal shooting settings can be automatically applied by referring to the user's past shooting history.

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

[0066] Step 1: The camera unit captures video using a wearable device worn by the user. For example, the camera unit can automatically capture various everyday scenes, such as while the user is taking a walk or cooking. The camera unit can also capture video footage, live footage, recorded footage, etc., depending on the type and format of the video. Step 2: The analysis unit analyzes the video captured by the shooting unit. For example, the analysis unit can analyze the video using methods such as image analysis, audio analysis, and motion analysis. The analysis unit can also cut out or select necessary parts based on the content of the video. Step 3: The editing department edits the video analyzed by the analysis department. For example, the editing department can edit the video using methods such as cutting, adding effects, and editing audio. The editing department can also add mosaics to protect privacy and captions to supplement visual information. Furthermore, the editing department can add background music to the video. Step 4: The transmission department transmits the video edited by the editing department. For example, the transmission department can transmit the video by posting it on social media, live streaming, uploading it to the cloud, or other methods.

[0067] (Example 2) A Vlog creation system according to an embodiment of the present invention utilizes a generation AI and a wearable device to enable anyone to easily create and share Vlogs. The Vlog creation system allows users to easily create Vlogs without requiring specialized knowledge or effort. The system automatically captures video footage, and the generation AI edits the footage. Furthermore, a Vlog-SNS is provided that allows users to easily share the Vlogs they create. For example, in this Vlog creation system, the user wears a wearable device. This device is a badge-shaped AI wearable device with no keyboard or display. While worn by the user, the device automatically captures video footage. For example, it can capture various everyday scenes, such as while the user is taking a walk or cooking. The captured video footage is then sent to the generation AI for editing. The generation AI analyzes the video and crops or highlights necessary portions. It can also add mosaics to protect privacy and captions to enhance visual information. Furthermore, adding background music to the video allows for the creation of compelling Vlogs. Vlogs created in this way can be easily shared on the Vlog-SNS. This allows the Vlog creation system to allow users to easily create and share Vlogs without requiring any special knowledge or effort. This is expected to make Vlog sharing mainstream and lead to it becoming the next major social networking site. This allows the Vlog creation system to allow users to easily create and share Vlogs without requiring any special knowledge or effort. For example, users can quickly and accurately shoot various scenes from their daily lives, and the generation AI can automatically edit them to create an appealing Vlog. Furthermore, sharing can be easily done through the Vlog-SNS, allowing users to share specific details about their lives. This is expected to make Vlog sharing mainstream and lead to it becoming the next major social networking site.

[0068] A Vlog creation system according to an embodiment includes a filming unit, an analysis unit, an editing unit, and a transmission unit. The filming unit captures video using a wearable device worn by a user. For example, the filming unit can automatically capture various everyday scenes, such as while the user is taking a walk or cooking. The filming unit can capture video footage, live footage, recorded footage, and the like, depending on the type and format of the video. The analysis unit analyzes the video captured by the filming unit. For example, the analysis unit can analyze the video using methods such as image analysis, audio analysis, and motion analysis. The analysis unit can also crop or select necessary portions based on the content of the video. The editing unit edits the video analyzed by the analysis unit. For example, the editing unit can edit the video using methods such as cut editing, adding effects, and audio editing. The editing unit can also add mosaics to protect privacy or add captions to supplement visual information. The editing unit can also add background music to the video. The transmission unit transmits the video edited by the editing unit. For example, the transmission unit can transmit the video by posting to social media, live streaming, uploading to the cloud, etc. As a result, the Vlog creation system according to the embodiment automates the process from shooting to analyzing, editing, and transmitting the video, allowing users to easily create Vlogs.

[0069] The analysis unit can analyze the video and cut out and select necessary portions. For example, the analysis unit can automatically detect important scenes from the video and cut out necessary portions. The analysis unit can also pick out portions in which specific people appear. Furthermore, the analysis unit can select and pick out particularly noteworthy scenes from the video. This improves editing efficiency by automatically cutting out and picking out necessary portions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data into a generation AI and have the generation AI cut out and pick out necessary portions.

[0070] The editorial department can apply mosaics to protect privacy. For example, the editorial department can automatically detect the face of a specific person in a video and apply a mosaic. The editorial department can also blur portions of audio that contain specific personal information. Furthermore, the editorial department can automatically detect portions of a video that require privacy protection and apply a mosaic. This allows the user's privacy to be protected by automatically applying a mosaic for privacy protection. Some or all of the above-mentioned processing by the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input video data into a generation AI and have the generation AI apply mosaics for privacy protection.

[0071] The editing department can add subtitles to supplement visual information. For example, the editing department can display important information in the video as subtitles. The editing department can also add annotations for specific scenes in the video as subtitles. Furthermore, the editing department can add graphics or subtitles to supplement visual information in the video. This allows for automatic addition of subtitles to supplement visual information, thereby deepening understanding of the video. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video data into a generation AI and have the generation AI add subtitles to supplement visual information.

[0072] The editing department can add background music to the video. For example, the editing department can select appropriate background music according to the content of the video and add it to the video. The editing department can also adjust the genre and tempo of the background music to match the atmosphere of the video. Furthermore, the editing department can improve the quality of the video by adding background music that is appealing to viewers of the video. This allows for the automatic addition of background music to the video, making it possible to create a Vlog that is appealing to viewers. Some or all of the above-mentioned processing by the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can input video data into a generation AI and have the generation AI add background music.

[0073] The sending unit can send video to a social networking site. For example, the sending unit can automatically post edited video to a social networking site. The sending unit can also live-stream the video. Furthermore, the sending unit can upload the video to a cloud and generate a sharing link. This allows users to easily share their Vlogs by automatically sending the video to a Vlog-SNS. Some or all of the above-described processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input edited video data into a generation AI and have the generation AI post the video to a social networking site or live-stream the video.

[0074] The camera unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is having fun, the camera unit can prioritize capturing moments when the user is smiling. Furthermore, if the user is deeply moved, the camera unit can capture moments when the user's emotions are most clearly expressed. Furthermore, if the user is relaxed, the camera unit can capture natural facial expressions and movements. This allows for more natural video to be captured by adjusting the timing of capturing images based on 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the camera unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the camera unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The camera unit can automatically select an appropriate shooting angle based on the user's activity when shooting. For example, if the user is cooking, the camera unit can select an angle that emphasizes the movement of the user's hands. Furthermore, if the user is playing sports, the camera unit can select an angle that captures the dynamism of the movement. Furthermore, if the user is enjoying a landscape, the camera unit can select an angle that brings out the beauty of the background. This allows for more effective footage to be captured by selecting the optimal shooting angle based on the user's activity. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the user's activity data into a generation AI and have the generation AI select the optimal shooting angle.

[0076] When taking a photo, the photographing unit can appropriately adjust the photographing settings by referring to the user's past photographing history. For example, the photographing unit can automatically apply a filter that the user has used favorably in the past. The photographing unit can also set new photographing settings by referring to the composition of scenes photographed by the user in the past. Furthermore, the photographing unit can select optimal settings by taking into account the time of day and weather conditions when the user took photographs in the past. This allows optimal photographing settings to be automatically applied by referring to the user's past photographing history. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input past photographing data into a generation AI and have the generation AI adjust the optimal photographing settings.

[0077] The image capture unit can detect ambient light and audio levels in real time during shooting and automatically adjust appropriate shooting conditions. For example, when the ambient light is low, the image capture unit can automatically increase the ISO sensitivity to adjust brightness. Furthermore, when the ambient audio level is high, the image capture unit can adjust the microphone sensitivity to record clear audio. Furthermore, when the ambient light changes, the image capture unit can automatically adjust the white balance to maintain natural colors. This allows for real-time detection of ambient light and audio levels and automatic adjustment of optimal shooting conditions, thereby enabling higher quality video to be captured. Some or all of the above-described processing in the image capture unit may be performed using, or without, AI. For example, the image capture unit can input ambient light and audio data into a generation AI and have the generation AI adjust the optimal shooting conditions.

[0078] The image capture unit can estimate the user's emotions and determine the priority of scenes to be captured based on the estimated user emotions. For example, if the user is excited, the image capture unit can prioritize capturing scenes that best express the user's emotions. Furthermore, if the user is relaxed, the image capture unit can prioritize capturing natural landscapes or everyday scenes. Furthermore, if the user is moved, the image capture unit can capture the moment without missing it. By determining the priority of scenes to be captured based on the user's emotions, more emotionally rich footage can be captured. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the image capture unit may be performed using, for example, AI, or without AI. For example, the image capture unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of scenes to be captured.

[0079] When taking a photo, the photographing unit can prioritize photographing highly relevant scenes by taking into account the user's geographical location information. For example, when the user is at a tourist spot, the photographing unit can prioritize photographing the famous places and scenery of that location. Furthermore, when the user is at an event venue, the photographing unit can prioritize photographing highlight scenes of the event. Furthermore, when the user is at home, the photographing unit can prioritize photographing scenes from everyday life. In this way, highly relevant scenes can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI. For example, the photographing unit can input geographical location data to a generation AI and cause the generation AI to select highly relevant scenes.

[0080] The photographing unit can analyze the user's social media activity and photograph related scenes when photographing. For example, the photographing unit can prioritize photographing locations that the user has shared on social media. The photographing unit can also photograph events or locations in which the user has expressed interest on social media. Furthermore, the photographing unit can analyze the content of the user's social media posts and photograph related scenes. In this way, by analyzing the user's social media activity, related scenes can be effectively photographed. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input social media data into a generation AI and cause the generation AI to photograph related scenes.

[0081] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. For example, the photographing unit can automatically apply a photographing style that the user previously preferred. The photographing unit can also adjust the photographing method by reflecting improvements that the user previously pointed out. Furthermore, the photographing unit can select optimal photographing settings based on the user's past feedback. This allows the photographing method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input past feedback data into a generation AI and have the generation AI customize the photographing method.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing scenes that best express the user's emotions. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing natural scenes. Furthermore, if the user is emotional, the analysis unit can analyze the moment without missing it. By adjusting the analysis priority based on the user's emotions, more emotionally rich video can be analyzed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis priority.

[0083] During analysis, the analysis unit can apply different analysis algorithms based on the content of the video. For example, if the video is a landscape, the analysis unit can apply a landscape analysis algorithm. Also, if the video is a person, the analysis unit can apply a face recognition algorithm. Furthermore, if the video is an event, the analysis unit can apply an event analysis algorithm. By applying different analysis algorithms based on the content of the video, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data to a generation AI and have the generation AI apply an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis according to the category of the video. For example, if the video is a landscape, the analysis unit can perform a detailed landscape analysis. Furthermore, if the video is of a person, the analysis unit can also analyze facial expressions and movements in detail. Furthermore, if the video is of an event, the analysis unit can analyze highlight scenes of the event in detail. This allows for more detailed analysis by adjusting the level of detail of the analysis according to the category of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can perform a similar analysis based on analysis results that the user previously preferred. The analysis unit can also improve the analysis accuracy by reflecting improvements that the user previously pointed out. Furthermore, the analysis unit can analyze the user's past analysis results and select an optimal analysis algorithm. This improves the accuracy of the analysis 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, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI improve the analysis accuracy.

[0086] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing scenes that best express the user's emotions. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing natural scenes. Furthermore, if the user is moved, the analysis unit can analyze the moment without missing it. By adjusting the order of analysis based on the user's emotions, more emotionally rich video can be analyzed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the order of analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time when the video was shot. For example, if the video was shot recently, the analysis unit can prioritize analysis. Furthermore, if the video is related to a specific event, the analysis unit can also analyze based on the time of the event. Furthermore, if the video is from the past, the analysis unit can determine the analysis priority according to its importance. In this way, by determining the analysis priority based on the time when the video was shot, more important videos can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input shooting time data into the generation AI and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. For example, if the videos are related to a specific theme, the analysis unit can determine the order of analysis based on that theme. Furthermore, the analysis unit can prioritize analysis of videos related to the user's interests. Furthermore, if the videos are highly related to other videos, the analysis unit can adjust the order of analysis based on the relevance. Thus, by adjusting the order of analysis based on the relevance of the videos, highly related videos can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input video relevance data to a generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has expertise, the analysis unit can perform a detailed analysis. Furthermore, if the user is a beginner, the analysis unit can also perform a basic analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. This allows for more appropriate analysis by adjusting the level of detail of 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, AI, or may be performed without using AI. For example, the analysis unit can input the user's expertise data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0090] The editing department can estimate the user's emotions and adjust the editing expression method based on the estimated user's emotions. For example, if the user is having fun, the editing department can use bright colors and cheerful background music. Furthermore, if the user is feeling emotional, the editing department can use moving music and effects. Furthermore, if the user is feeling relaxed, the editing department can use calm colors and relaxing background music. This allows for editing of more emotionally rich video by adjusting the editing expression method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing department can be performed using, for example, AI, or without AI. For example, the editing department can input the user's emotion data into the generation AI and have the generation AI adjust the editing expression method.

[0091] During editing, the editing department can adjust the level of editing detail based on the importance of the video. For example, the editing department can edit important scenes in detail and simplify unnecessary scenes. The editing department can also perform detailed editing to emphasize important events or occurrences. Furthermore, the editing department can adjust the level of editing detail according to the importance of the video. As a result, by adjusting the level of editing detail based on the importance of the video, more important scenes can be edited in detail. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video importance data into the generation AI and have the generation AI adjust the level of editing detail.

[0092] During editing, the editing department can apply different editing algorithms depending on the category of the video. For example, if the video is a landscape, the editing department can apply a landscape editing algorithm. Furthermore, if the video is a person, the editing department can apply a face recognition editing algorithm. Furthermore, if the video is an event, the editing department can apply an event editing algorithm. In this way, by applying different editing algorithms depending on the category of the video, the accuracy of editing is improved. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video data into a generation AI and have the generation AI apply an appropriate editing algorithm.

[0093] During editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. For example, the editing department can automatically apply an editing style that the user previously preferred. The editing department can also improve the editing accuracy by reflecting improvements that the user previously pointed out. Furthermore, the editing department can analyze the user's past editing results and select the optimal editing algorithm. This improves the accuracy of editing by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input past editing data into a generation AI and have the generation AI improve the editing accuracy.

[0094] The editing unit can estimate the user's emotions and adjust the length of the edited video based on the estimated user emotions. For example, if the user is in a hurry, the editing unit can edit the video to be short and to the point. If the user is relaxed, the editing unit can edit the video to be longer and include detailed explanations. If the user is excited, the editing unit can edit the video with more visually stimulating effects. By adjusting the length of the edited video based on the user's emotions, more emotional video can be edited. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing unit can be performed using, for example, AI, or without AI. For example, the editing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the edited video.

[0095] During editing, the editing department can determine the editing priority based on the shooting date of the video. For example, the editing department can prioritize editing of recently shot video. The editing department can also prioritize editing of video related to a specific event. Furthermore, the editing department can determine the editing priority even for past video according to its importance. In this way, by determining the editing priority based on the shooting date of the video, more important video can be prioritized for editing. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input shooting date data into the generation AI and have the generation AI determine the editing priority.

[0096] During editing, the editing department can adjust the editing order based on the relevance of the videos. For example, the editing department can prioritize editing videos related to a specific theme. The editing department can also prioritize editing videos related to the user's interests. Furthermore, the editing department can prioritize editing videos that are highly relevant to other videos. In this way, by adjusting the editing order based on the relevance of the videos, highly relevant videos can be prioritized for editing. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video relevance data into the generation AI and have the generation AI adjust the editing order.

[0097] During editing, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. For example, if the user has technical expertise, the editing department can perform editing that makes extensive use of technical terminology. Furthermore, if the user is a beginner, the editing department can perform editing that uses basic terminology. Furthermore, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. This allows for more appropriate editing by adjusting the use of technical terminology in the editing depending on the user's level of expertise. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's technical expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0098] The transmission unit can estimate the user's emotions and adjust the timing of transmission based on the estimated user emotions. For example, if the user is excited, the transmission unit can transmit immediately. Furthermore, if the user is relaxed, the transmission unit can transmit at an appropriate timing. Furthermore, if the user is moved, the transmission unit can transmit at a timing that best expresses the user's emotions. By adjusting the timing of transmission based on the user's emotions, the video can be transmitted at a more effective timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of transmission.

[0099] The transmission unit can select an appropriate transmission platform based on the content of the video when transmitting. For example, if the video is short, the transmission unit can select a short video platform such as Instagram or TikTok. Alternatively, if the video is long, the transmission unit can select a long video platform such as YouTube or Facebook. Furthermore, the transmission unit can select the optimal transmission platform depending on the content of the video. This allows the video to be transmitted more effectively by selecting the optimal transmission platform based on the content of the video. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input video data into a generation AI and have the generation AI select a transmission platform.

[0100] When making a call, the calling unit can appropriately adjust the call settings by referring to the user's past call history. The calling unit can, for example, automatically apply call settings that the user has previously preferred. The calling unit can also set new call settings by referring to the content of calls made by the user in the past. Furthermore, the calling unit can analyze the user's past call history and select optimal call settings. This makes it possible to automatically apply optimal call settings by referring to the user's past call history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input past call data into a generation AI and have the generation AI adjust the call settings.

[0101] The transmission unit can analyze the video viewing history at the time of transmission and suggest an appropriate transmission method. For example, if a video is viewed frequently, the transmission unit can suggest a new transmission method by referring to that transmission method. Furthermore, if a video is viewed infrequently, the transmission unit can analyze the cause and suggest improvements. Furthermore, the transmission unit can suggest the optimal transmission method based on the video viewing history. In this way, the optimal transmission method can be suggested by analyzing the video viewing history. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input viewing history data into a generation AI and have the generation AI suggest a transmission method.

[0102] The transmission unit can estimate the user's emotions and determine the priority of videos to be transmitted based on the estimated user emotions. For example, if the user is excited, the transmission unit can prioritize transmitting videos that best express the user's emotions. Furthermore, if the user is relaxed, the transmission unit can prioritize transmitting natural scenes. Furthermore, if the user is moved, the transmission unit can transmit videos without missing the moment. By determining the priority of videos to be transmitted based on the user's emotions, more emotional videos can be transmitted. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of videos to be transmitted.

[0103] When transmitting, the transmission unit can prioritize transmitting highly relevant video by taking into account the user's geographical location information. For example, when the user is at a tourist spot, the transmission unit can prioritize transmitting famous places and scenery of the location. Furthermore, when the user is at an event venue, the transmission unit can prioritize transmitting highlight scenes of the event. Furthermore, when the user is at home, the transmission unit can prioritize transmitting scenes from everyday life. In this way, highly relevant video can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input geographical location data to a generation AI and cause the generation AI to transmit highly relevant video.

[0104] At the time of transmission, the transmission unit can analyze the user's social media activity and transmit related videos. For example, the transmission unit can prioritize transmission of places that the user has shared on social media. The transmission unit can also transmit events or places that the user has shown interest in on social media. Furthermore, the transmission unit can analyze the content of the user's social media posts and transmit related videos. In this way, by analyzing the user's social media activity, related videos can be transmitted effectively. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input social media data into a generation AI and cause the generation AI to transmit related videos.

[0105] The sending unit can customize the sending method by reflecting the user's past feedback when making a call. The sending unit can, for example, automatically apply a sending style that the user previously preferred. The sending unit can also adjust the sending method by reflecting improvements that the user previously pointed out. Furthermore, the sending unit can select the optimal sending method based on the user's past feedback. In this way, the sending method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input past feedback data into a generation AI and have the generation AI customize the sending method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described shooting unit, analysis unit, editing unit, and transmission unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the shooting unit is realized by the camera 42 of the smart device 14 and automatically captures video while the user is taking a walk or cooking. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the captured video and cuts out or selects necessary portions. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs video cut editing, effect addition, and audio editing. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 and posts the edited video to social networking sites or uploads it to the cloud. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described photographing unit, analysis unit, editing unit, and transmission unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214, and automatically captures various scenes in the user's daily life. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured video and cuts out or selects necessary portions. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs video cut editing, effect addition, and audio editing. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214, and posts the edited video to a social networking site or uploads it to the cloud. === Hard Collateral 1-3 === Each of the multiple elements including the above-described shooting unit, analysis unit, editing unit, and transmission unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the shooting unit is realized by the camera 42 of the headset-type terminal 314, and the user automatically captures various scenes in their daily lives. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured video and cuts out or selects necessary portions. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs cut editing of the video, adding effects, and audio editing. The transmission unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and posts the edited video to SNS or uploads it to the cloud. === Hard Collateral 1-4 === Each of the multiple elements including the above-described shooting unit, analysis unit, editing unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the shooting unit is realized by the camera 42 of the robot 414, and automatically captures various scenes in the user's daily life. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured video and cuts out or selects necessary portions. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs cut editing of the video, adding effects, and audio editing. The transmission unit is realized, for example, by the control unit 46A of the robot 414, and posts the edited video to SNS or uploads it to the cloud.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The camera monitors the user's biometric information, such as heart rate and body temperature, in real time and can adjust the timing and angle of the shot based on this data. For example, if the user's heart rate rises, it can start shooting to capture that moment. Also, if the user's body temperature rises, it can capture a relaxed scene. Furthermore, it can automatically set the optimal shooting conditions based on the user's biometric information. This makes it possible to capture more natural and realistic images by utilizing the user's biometric information.

[0108] The analysis unit can detect specific objects in the video and automatically add information related to that object. For example, if a building is detected in the video, the history and characteristics of the building can be displayed as captions. If an animal is detected in the video, information about the animal's species and ecology can be added. Furthermore, if a specific event is detected in the video, the background and details of that event can be added. This enriches the content of the video and deepens the viewer's understanding.

[0109] The editorial department can estimate the user's emotions and automatically adjust the color tone and filter of the video based on the estimated user's emotions. For example, if the user is feeling happy, bright and vivid colors can be applied. If the user is feeling depressed, muted colors and a monochrome filter can be applied. Furthermore, if the user is excited, dynamic effects can be added. This makes it possible to express the video in a way that matches the user's emotions, leaving a stronger impression on the viewer.

[0110] The editorial department can detect specific sounds in a video and automatically generate subtitles based on those sounds. For example, if a conversation is taking place in a video, the conversation can be displayed as subtitles in real time. Also, if specific music is playing in the video, the lyrics of that music can be displayed as subtitles. Furthermore, if environmental sounds play an important role in the video, a description of the sound can be added as subtitles. This makes it easier for viewers to understand the content of the video.

[0111] The editorial department can estimate the user's emotions and adjust the tempo and rhythm of the video based on the estimated user's emotions. For example, if the user is excited, the tempo of the video can be increased to emphasize the rhythm. Alternatively, if the user is relaxed, the tempo of the video can be decreased to make the rhythm gentler. Furthermore, if the user is moved, the tempo of the video can be changed to express the heightened emotion. In this way, by adjusting the rhythm and tempo of the video to match the user's emotions, a more emotional experience can be provided to the viewer.

[0112] The transmitter can automatically generate hashtags based on the content of the video and add them when posting to social media. For example, if a specific location is shown in the video, a hashtag related to that location can be generated. Also, if a specific event is taking place in the video, a hashtag related to that event can be generated. Furthermore, if a specific theme is covered in the video, a hashtag related to that theme can be generated. This makes social media posting more effective and more likely to attract viewers' attention.

[0113] The shooting unit can estimate the user's emotions and determine the priority of scenes to be shot based on the estimated user's emotions. For example, if the user is excited, it can prioritize shooting scenes that best express that emotion. Also, if the user is relaxed, it can prioritize shooting natural landscapes or everyday scenes. Furthermore, if the user is moved, it can capture that moment without missing a beat. In this way, by determining the priority of scenes to be shot based on the user's emotions, it is possible to shoot more emotionally rich video.

[0114] The camera unit can automatically switch the shooting mode based on the user's activity. For example, if the user is playing sports, the camera can switch to sports mode to capture fast-moving scenes. If the user is cooking, the camera can switch to close-up mode to emphasize the movements of the user's hands. Furthermore, if the user is enjoying the scenery, the camera can switch to wide-angle mode to capture the beauty of the background. This makes it possible to automatically select the optimal shooting mode according to the user's activity.

[0115] The shooting unit can appropriately adjust shooting settings by referring to the user's past shooting history. For example, it can automatically apply a filter that the user has used favorably in the past. It can also set new shooting settings by referring to the composition of scenes the user has photographed in the past. Furthermore, it can select optimal settings by taking into account the time of day and weather conditions when the user took photos in the past. In this way, optimal shooting settings can be automatically applied by referring to the user's past shooting history.

[0116] The shooting unit can estimate the user's emotions and determine the priority of scenes to be shot based on the estimated user's emotions. For example, if the user is excited, it can prioritize shooting scenes that best express that emotion. Also, if the user is relaxed, it can prioritize shooting natural landscapes or everyday scenes. Furthermore, if the user is moved, it can capture that moment without missing a beat. In this way, by determining the priority of scenes to be shot based on the user's emotions, it is possible to shoot more emotionally rich video.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The camera unit captures video using a wearable device worn by the user. For example, the camera unit can automatically capture various everyday scenes, such as while the user is taking a walk or cooking. The camera unit can also capture video footage, live footage, recorded footage, etc., depending on the type and format of the video. Step 2: The analysis unit analyzes the video captured by the shooting unit. For example, the analysis unit can analyze the video using methods such as image analysis, audio analysis, and motion analysis. The analysis unit can also cut out or select necessary parts based on the content of the video. Step 3: The editing department edits the video analyzed by the analysis department. For example, the editing department can edit the video using methods such as cutting, adding effects, and editing audio. The editing department can also add mosaics to protect privacy and captions to supplement visual information. Furthermore, the editing department can add background music to the video. Step 4: The transmission department transmits the video edited by the editing department. For example, the transmission department can transmit the video by posting it on social media, live streaming, uploading it to the cloud, or other methods.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the 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.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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, in order to avoid confusion and to 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 filming unit that shoots the video; an analysis unit that analyzes the video captured by the imaging unit; an editing unit that edits the video analyzed by the analysis unit; a transmission unit that transmits the video edited by the editing unit; Equipped with A system characterized by:

2. The analysis unit Analyze the video, cut out and select the necessary parts 2. The system of claim 1.

3. The editorial department Adding a mosaic to protect privacy 2. The system of claim 1.

4. The editorial department Add subtitles to supplement visual information 2. The system of claim 1.

5. The editorial department Add background music to video 2. The system of claim 1.

6. The transmitting unit Posting videos to social media 2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When taking a photo, the appropriate shooting angle is automatically selected based on the user's activity.

2. The system of claim 1.

Images