System

Abstract

An object of the system according to the embodiment is to automate a process from collection to posting of a game video.SOLUTION: A system according to an embodiment includes a collection unit, an upload unit, an editing unit, and a posting unit. The collection unit collects data of the game video. The upload unit uploads the data collected by the collection unit to the cloud storage. The editing unit edits the data uploaded by the uploading unit. The posting unit posts the data edited by the editing unit to the video distribution site.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, the process of collecting, editing, uploading, and posting game videos was done manually, which was time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to automate the process from collecting to posting game videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an upload unit, an editing unit, and a posting unit. The collection unit collects game video data. The upload unit uploads the data collected by the collection unit to cloud storage. The editing unit edits the data uploaded by the upload unit. The posting unit posts the data edited by the editing unit to a video distribution site. [Effects of the Invention]

[0007] The system according to the embodiment can automate the process from collecting game videos to posting them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically uploads game videos that users regularly play, automatically edits the videos, and posts them to various video distribution sites. This system automatically collects data on game videos that users play on their PCs, game consoles, smartphones, etc., automatically uploads the collected video data, automatically edits the uploaded video data, and posts the video data to various video distribution sites. For example, the system automatically collects game video data while users are playing games. For example, it collects gameplay screen and audio data in real time. Next, the system automatically uploads the collected game video data. For example, uploading the data to a cloud storage service facilitates data storage and management. Furthermore, the system automatically edits the uploaded game video data. For example, it can cut out unnecessary parts or add subtitles and effects. Finally, the system automatically posts the edited game video to various video distribution sites. For example, by posting the video to a platform such as YouTube (registered trademark), the video can be shared with a large audience. This allows users to share game videos without any effort. This allows the system to automate the process of collecting, uploading, editing, and posting game videos. For example, users can share game videos without much effort, and viewers can also enjoy videos that are easy to watch.

[0029] A game video sharing system according to an embodiment includes a collection unit, an upload unit, an editing unit, and a posting unit. The collection unit collects game video data. For example, the collection unit can collect game play screen and audio data in real time. The collection unit can also detect a specific event during game play and start collection based on the event. The collection unit can also estimate a user's emotions and adjust the timing of collection based on the estimated emotions. For example, if the user is excited, the collection unit can start collection just before a boss battle or an important event. The upload unit uploads the data collected by the collection unit to cloud storage. The upload unit can, for example, upload the data to a cloud storage service. The upload unit can also automatically adjust the data compression rate during upload to optimize uploading. The upload unit can also estimate a user's emotions and adjust the timing of uploading based on the estimated emotions. For example, if the user is relaxed, the upload unit can start uploading immediately. The editing unit edits the data uploaded by the upload unit. For example, the editing unit can cut out unnecessary parts and add subtitles and effects. The editing department can also estimate the user's emotions and adjust the editing style based on the estimated emotions. Furthermore, the editing department can apply different editing algorithms depending on the game genre. For example, the editing department can apply a speedy editing algorithm to action games. The posting department posts the data edited by the editing department to a video distribution site. The posting department can post the data to a video distribution site such as YouTube. Furthermore, the posting department can estimate the user's emotions and adjust the timing of posting based on the estimated emotions. Furthermore, the posting department can analyze trends on the video distribution site and suggest optimal content to post. For example, the posting department can suggest titles and tags that match current trends.As a result, the game video sharing system according to the embodiment can automate the series of tasks of collecting, uploading, editing, and posting game videos.

[0030] The collection unit can collect game play screens and audio data in real time. For example, the collection unit can capture game play screens and collect audio data in real time. For example, the collection unit can collect game screen captures and audio data when the game is being played on a PC. The collection unit can also collect game play screens and audio data when the game is being played on a game console or smartphone. This allows for more accurate game video to be obtained by collecting game play screens and audio data in real time. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for collecting game play screens and audio data in real time.

[0031] The upload unit can upload data to a cloud storage service. The upload unit can upload data to, for example, a cloud storage service. For example, the upload unit can upload data to a cloud storage service such as AWS (registered trademark) S3 or Google (registered trademark) Drive. The upload unit can also automatically adjust the data compression rate during upload to perform optimal uploading. For example, the upload unit can set a low compression rate when a high-speed Internet connection is used and a high compression rate when a low-speed Internet connection is used. This facilitates data storage and management by uploading data to a cloud storage service. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for uploading data to a cloud storage service.

[0032] The editing unit can cut out unnecessary parts and add subtitles and effects. The editing unit can, for example, cut out unnecessary parts and add subtitles and effects. For example, the editing unit can automatically detect and cut out unnecessary parts of a game video. The editing unit can also add subtitles and effects. For example, the editing unit can set the subtitle format and type of effect and add them to the video. This makes it possible to create a video that is easy for viewers to watch. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit the video using an AI model for cutting out unnecessary parts and adding subtitles and effects.

[0033] The posting unit can post videos to a video distribution site such as YouTube. The posting unit can post videos to a video distribution site such as YouTube. For example, the posting unit can post videos according to YouTube's uploading procedure. The posting unit can also estimate a user's emotions and adjust the timing of posting based on the estimated emotions. For example, the posting unit can post immediately if the user is excited. This allows the video to be shared with many viewers. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post videos using an AI model for posting videos to a video distribution site such as YouTube.

[0034] The collection unit can detect a specific event during game play and start collection based on that event. For example, the collection unit can detect a specific event during game play and start collection based on that event. For example, the collection unit can start collection just before a boss battle begins. The collection unit can also start collection the moment a rare item is obtained. Furthermore, the collection unit can start collection when a specific quest is completed. In this way, by starting collection based on a specific event, it is possible to collect without missing important scenes. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for detecting a specific event and starting collection based on that event.

[0035] The collection unit can automatically adjust the resolution and frame rate of the game play screen to perform appropriate collection. The collection unit can automatically adjust the resolution and frame rate of the game play screen to perform appropriate collection. For example, the collection unit can set a high frame rate for fast action scenes. The collection unit can also set a high resolution for scenes with many still images. Furthermore, the collection unit can adjust the resolution and frame rate according to network conditions. This enables optimal collection by automatically adjusting the resolution and frame rate. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for automatically adjusting the resolution and frame rate of the game play screen.

[0036] The collection unit can analyze the user's past play history and prioritize collecting scenes that are particularly noteworthy. The collection unit can, for example, analyze the user's past play history and prioritize collecting scenes that are particularly noteworthy. For example, the collection unit can prioritize collecting scenes that the user has viewed in the past. The collection unit can also prioritize collecting scenes that the user has received high ratings in the past. Furthermore, the collection unit can also prioritize collecting scenes that the user has received many comments in the past. In this way, by analyzing the past play history, it is possible to prioritize collecting scenes that are particularly noteworthy. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for analyzing the user's past play history and priority collecting scenes that are particularly noteworthy.

[0037] The collection unit can recognize a user's voice command while playing a game and start or stop collection based on the specific command. For example, the collection unit can recognize a user's voice command while playing a game and start or stop collection based on the specific command. For example, the collection unit can start collection when the user says "start recording." The collection unit can also stop collection when the user says "stop recording." Furthermore, the collection unit can collect specific scenes when the user says "highlight." This simplifies user operation by starting or stopping collection based on a voice command. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for recognizing a user's voice command and starting or stopping collection based on a specific command.

[0038] The collection unit can monitor the remaining battery level of the user's device while playing a game, and suspend collection when the battery becomes low. For example, the collection unit can monitor the remaining battery level of the user's device while playing a game, and suspend collection when the battery becomes low. For example, the collection unit can suspend collection when the remaining battery level falls to 20% or less. The collection unit can also stop collection when the remaining battery level falls to 10% or less. Furthermore, the collection unit can resume collection when the battery is charged. In this way, by monitoring the remaining battery level, it is possible to prevent the device from running out of battery. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for monitoring the remaining battery level of the user's device and suspending collection when the battery becomes low.

[0039] The collection unit can monitor the user's internet connection status while playing a game, and suspend collection if it determines that the connection is unstable. For example, the collection unit can monitor the user's internet connection status while playing a game, and suspend collection if it determines that the connection is unstable. For example, the collection unit can suspend collection if the internet connection is unstable. The collection unit can also resume collection if the internet connection is restored. Furthermore, the collection unit can stop collection if the internet connection is completely lost. In this way, by monitoring the internet connection status, collection can be suspended if the connection is unstable, preventing data loss. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that monitors the user's internet connection status and suspends collection if it determines that the connection is unstable.

[0040] The upload unit can automatically adjust the data compression rate during upload to optimize upload. The upload unit can, for example, automatically adjust the data compression rate during upload to optimize upload. For example, the upload unit can set a low compression rate during a high-speed Internet connection and a high compression rate during a low-speed Internet connection. The upload unit can also adjust the compression rate depending on the content of the data. This enables optimal upload by automatically adjusting the data compression rate. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for automatically adjusting the data compression rate.

[0041] The upload unit can monitor the usage status of cloud storage during uploading and select the optimal storage service. The upload unit can, for example, monitor the usage status of cloud storage during uploading and select the optimal storage service. For example, the upload unit can monitor the available space of cloud storage and select the optimal service. The upload unit can also select the optimal service taking into account the cloud storage usage fee. Furthermore, the upload unit can select the optimal service taking into account the security level of the cloud storage. In this way, the optimal storage service can be selected by monitoring the usage status of cloud storage. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can monitor the usage status of cloud storage and upload data using an AI model for selecting the optimal storage service.

[0042] The upload unit can automatically encrypt data when uploading, thereby enhancing security. The upload unit can, for example, automatically encrypt data when uploading, thereby enhancing security. For example, the upload unit can encrypt data before uploading. The upload unit can also encrypt data during uploading. Furthermore, the upload unit can also encrypt data after uploading. In this way, security can be enhanced by automatically encrypting data. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for automatically encrypting data.

[0043] The upload unit can monitor the user's Internet connection speed during uploading and adjust the upload speed according to the connection speed. For example, the upload unit can monitor the user's Internet connection speed during uploading and adjust the upload speed according to the connection speed. For example, the upload unit can set the upload speed to the maximum when the Internet connection is fast. The upload unit can also set the upload speed to a lower value when the Internet connection is slow. Furthermore, the upload unit can dynamically adjust the upload speed when the Internet connection speed fluctuates. This enables efficient uploading by adjusting the upload speed according to the Internet connection speed. Some or all of the above-described processing in the upload unit can be performed using, for example, AI, or can be performed without using AI. For example, the upload unit can upload data using an AI model for monitoring the user's Internet connection speed and adjusting the upload speed according to the connection speed.

[0044] The upload unit can monitor the storage capacity of the user's device during uploading and automatically delete old data when capacity is insufficient. For example, the upload unit can monitor the storage capacity of the user's device during uploading and automatically delete old data when capacity is insufficient. For example, the upload unit can delete old data when storage capacity exceeds 80%. The upload unit can also prioritize deleting old data when storage capacity exceeds 90%. Furthermore, the upload unit can delete the oldest data when storage capacity reaches 100%. This enables efficient storage utilization by monitoring storage capacity and automatically deleting old data when capacity is insufficient. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for monitoring the storage capacity of the user's device and automatically deleting old data when capacity is insufficient.

[0045] The upload unit can monitor the user's cloud storage usage fees and propose an optimal pricing plan during uploading. The upload unit can, for example, monitor the user's cloud storage usage fees during uploading and propose an optimal pricing plan. For example, the upload unit can propose an optimal pricing plan when the cloud storage usage fees increase. The upload unit can also propose an optimal pricing plan when the cloud storage usage fees decrease. Furthermore, the upload unit can dynamically propose an optimal pricing plan when the cloud storage usage fees fluctuate. This enables cost-effective storage usage by monitoring the cloud storage usage fees and proposing an optimal pricing plan. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for monitoring the user's cloud storage usage fees and proposing an optimal pricing plan.

[0046] The editing department can add effects to emphasize specific game events during editing. For example, the editing department can add effects to emphasize specific game events during editing. For example, the editing department can add special effects to victory scenes. The editing department can also add dramatic effects to defeat scenes. Furthermore, the editing department can add sparkling effects to scenes where rare items are obtained. In this way, adding effects to emphasize specific game events can create videos that are more impressive to viewers. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can edit videos using an AI model to add effects to emphasize specific game events.

[0047] The editing unit can analyze the user's past editing history during editing and propose an optimal editing pattern. For example, the editing unit can analyze the user's past editing history during editing and propose an optimal editing pattern. For example, the editing unit can prioritize and propose effects that the user has used in the past. The editing unit can also propose an editing pattern by referring to scenes that the user has previously cut. Furthermore, the editing unit can also propose the style of subtitles that the user has previously added. In this way, by analyzing the past editing history, an optimal editing pattern for the user can be proposed. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit a video using an AI model that analyzes the user's past editing history and proposes an optimal editing pattern.

[0048] The editing department can apply different editing algorithms depending on the game genre during editing. For example, the editing department can apply different editing algorithms depending on the game genre during editing. For example, the editing department can apply a speedy editing algorithm to an action game. The editing department can also apply a story-focused editing algorithm to an RPG game. Furthermore, the editing department can apply an explanation-focused editing algorithm to a puzzle game. This allows for more appropriate editing by applying an editing algorithm depending on the game genre. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can edit videos using an AI model for applying different editing algorithms depending on the game genre.

[0049] The editing unit can determine editing priorities based on game play time during editing. The editing unit can determine editing priorities based on game play time during editing, for example. For example, the editing unit can prioritize editing of scenes played for a long time. The editing unit can also prioritize editing of scenes in which important events occur in a short period of time. Furthermore, the editing unit can prioritize editing of scenes in which play time is concentrated in a specific time period. In this way, by determining editing priorities based on game play time, important scenes can be prioritized for editing. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit videos using an AI model for determining editing priorities based on game play time.

[0050] The editing department can improve the accuracy of editing by referring to game-related information during editing. The editing department can improve the accuracy of editing by referring to game-related information during editing, for example. For example, the editing department can refer to game walkthrough information to perform editing that emphasizes important scenes. The editing department can also refer to reviews to perform editing that emphasizes scenes that are likely to interest viewers. Furthermore, the editing department can refer to official game information to perform editing that reflects the latest update content. In this way, by referring to game-related information, the accuracy of editing can be improved. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can edit videos using an AI model that refers to game-related information to improve the accuracy of editing.

[0051] The editing unit can adjust the editing processing speed based on the performance of the user's device during editing. The editing unit can adjust the editing processing speed based on the performance of the user's device during editing, for example. For example, the editing unit can perform high-speed editing processing on a high-performance device. The editing unit can also adjust the editing speed to reduce the processing load on a low-performance device. Furthermore, the editing unit can adjust the editing processing taking into account the remaining battery level of the device. This enables efficient editing by adjusting the editing processing speed taking into account the performance of the device. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit a video using an AI model for adjusting the editing processing speed based on the performance of the user's device.

[0052] The posting unit can analyze trends on video streaming sites at the time of posting and suggest optimal content to post. The posting unit can, for example, analyze trends on video streaming sites at the time of posting and suggest optimal content to post. For example, the posting unit can suggest titles and tags that match current trends. The posting unit can also suggest content that viewers are likely to be interested in based on trends. Furthermore, the posting unit can also suggest optimal posting times based on trends. In this way, by analyzing trends on video streaming sites, it is possible to suggest content to post that is attractive to viewers. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post videos using an AI model that analyzes trends on video streaming sites and suggests optimal content to post.

[0053] The posting unit can analyze the user's past posting history at the time of posting and suggest an optimal posting pattern. The posting unit can, for example, analyze the user's past posting history at the time of posting and suggest an optimal posting pattern. For example, the posting unit can suggest a posting pattern in which the user has attracted many viewers in the past. The posting unit can also suggest a posting pattern in which the user has received high ratings in the past. Furthermore, the posting unit can suggest a posting pattern in which the user has received many comments in the past. In this way, by analyzing the past posting history, it is possible to suggest an optimal posting pattern for the user. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can analyze the user's past posting history and post a video using an AI model for suggesting an optimal posting pattern.

[0054] The posting unit can automatically generate metadata for a video when posting. The posting unit can automatically generate metadata for a video when posting, for example. For example, the posting unit can generate an optimal title based on the content of the video. The posting unit can also generate a detailed description based on the content of the video. Furthermore, the posting unit can also generate related tags based on the content of the video. By automatically generating metadata for a video, the effort required for posting can be reduced. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post a video using an AI model for automatically generating metadata for a video.

[0055] The posting unit can analyze the viewer demographics of the video distribution site at the time of posting and suggest the optimal posting time. The posting unit can, for example, analyze the viewer demographics of the video distribution site at the time of posting and suggest the optimal posting time. For example, the posting unit can suggest posting during a time period when the viewer demographics are high. The posting unit can also suggest posting during a time period when the viewer demographics are low. Furthermore, the posting unit can adjust the posting time to match the activity times of the viewer demographics. In this way, the optimal posting time can be suggested by analyzing the viewer demographics. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post a video using an AI model for analyzing the viewer demographics of the video distribution site and suggesting the optimal posting time.

[0056] The posting unit can analyze the algorithm of the video distribution site at the time of posting and suggest the optimal posting method. The posting unit can, for example, analyze the algorithm of the video distribution site at the time of posting and suggest the optimal posting method. For example, the posting unit can suggest the optimal tag based on the algorithm. The posting unit can also suggest the optimal title based on the algorithm. Furthermore, the posting unit can also suggest the optimal description based on the algorithm. In this way, the optimal posting method can be suggested by analyzing the algorithm of the video distribution site. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can analyze the algorithm of the video distribution site and post the video using an AI model for suggesting the optimal posting method.

[0057] The posting unit can cooperate with the user's social media account when posting a video to promote the spread of the video. The posting unit can cooperate with the user's social media account when posting a video to promote the spread of the video. For example, the posting unit can automatically share the posted video on social media. The posting unit can also send notifications to social media followers. Furthermore, the posting unit can analyze reactions on social media and suggest the optimal method of spreading the video. In this way, by coordinating with the social media account, the spread of the video can be promoted. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can cooperate with the user's social media account and post the video using an AI model for promoting the spread of the video.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The upload module can monitor the user's internet connection speed in real time and adjust upload priorities accordingly. For example, if the connection speed drops, it can pause uploading less important data and resume it when the connection speed improves. It can also prioritize uploading important scenes when the connection speed is stable. Furthermore, the upload module can analyze the user's internet connection speed history and suggest optimal upload times.

[0060] The editing department can analyze a user's past viewing history and suggest editing styles that the viewer prefers. For example, it can analyze the editing styles of videos the user has previously watched and apply a similar style. It can also perform editing that emphasizes scenes or effects that the viewer particularly likes. It can also suggest new editing styles that the viewer might be interested in based on the viewing history.

[0061] The posting unit can link with the user's social media accounts to promote the spread of videos. For example, the posting unit can automatically share posted videos on social media. It can also send notifications to social media followers. It can also analyze reactions on social media and suggest the optimal way to spread the video. In this way, linking with social media accounts can promote the spread of videos.

[0062] The editing department can analyze the user's past editing history and suggest the optimal editing pattern. For example, the editing department can prioritize and suggest effects that the user has used in the past. The editing department can also suggest editing patterns by referring to scenes that the user has previously cut. Furthermore, the editing department can also suggest styles of subtitles that the user has previously added. In this way, by analyzing the user's past editing history, the editing department can suggest the optimal editing pattern for the user.

[0063] The posting unit can analyze trends on video streaming sites at the time of posting and suggest optimal posting content. For example, the posting unit can suggest titles and tags that match current trends. The posting unit can also suggest content that viewers are likely to be interested in based on trends. Furthermore, the posting unit can also suggest optimal posting times based on trends. In this way, by analyzing trends on video streaming sites, it is possible to suggest posting content that will be attractive to viewers.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects game video data. For example, the collection unit can collect game play screen and audio data in real time. The collection unit can also detect a specific event during game play and start collection based on the event. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of collection based on the estimated emotions. For example, if the user is excited, the collection unit can start collection just before a boss battle or an important event. Step 2: The uploading unit uploads the data collected by the collecting unit to cloud storage. The uploading unit can upload data to a cloud storage service, for example. The uploading unit can also automatically adjust the data compression rate during uploading to perform optimal uploading. Furthermore, the uploading unit can estimate the user's emotions and adjust the timing of uploading based on the estimated emotions. For example, the uploading unit can start uploading immediately if the user is relaxed. Step 3: The editing department edits the data uploaded by the uploading department. For example, the editing department can cut out unnecessary parts and add subtitles and effects. The editing department can also estimate the user's emotions and adjust the editing style based on the estimated emotions. Furthermore, the editing department can apply different editing algorithms depending on the game genre. For example, the editing department can apply a speedy editing algorithm to an action game. Step 4: The posting unit posts the data edited by the editing unit to a video distribution site. The posting unit can post the data to a video distribution site such as YouTube, for example. The posting unit can also estimate the user's emotions and adjust the timing of posting based on the estimated emotions. Furthermore, the posting unit can analyze trends on the video distribution site and suggest optimal content to post. For example, the posting unit can suggest titles and tags that match current trends.

[0066] (Example 2) A system according to an embodiment of the present invention automatically uploads game videos played by users, automatically edits the videos, and posts them to various video streaming sites. This system automatically collects data on game videos played by users on their PCs, game consoles, smartphones, etc., automatically uploads the collected video data, automatically edits the uploaded video data, and posts the video data to various video streaming sites. For example, the system automatically collects game video data while users are playing games. For example, it collects gameplay screen and audio data in real time. Next, the system automatically uploads the collected game video data. For example, uploading the data to a cloud storage service facilitates data storage and management. Furthermore, the system automatically edits the uploaded game video data. For example, it can cut out unnecessary parts or add subtitles and effects. Finally, the system automatically posts the edited game video to various video streaming sites. For example, by posting the video to platforms such as YouTube, the video can be shared with a large audience. This allows users to share game videos without any effort. This allows the system to automate the process of collecting, uploading, editing, and posting game videos. For example, users can share game videos without much effort, and viewers can also enjoy videos that are easy to watch.

[0067] A game video sharing system according to an embodiment includes a collection unit, an upload unit, an editing unit, and a posting unit. The collection unit collects game video data. For example, the collection unit can collect game play screen and audio data in real time. The collection unit can also detect a specific event during game play and start collection based on the event. The collection unit can also estimate a user's emotions and adjust the timing of collection based on the estimated emotions. For example, if the user is excited, the collection unit can start collection just before a boss battle or an important event. The upload unit uploads the data collected by the collection unit to cloud storage. The upload unit can, for example, upload the data to a cloud storage service. The upload unit can also automatically adjust the data compression rate during upload to optimize uploading. The upload unit can also estimate a user's emotions and adjust the timing of uploading based on the estimated emotions. For example, if the user is relaxed, the upload unit can start uploading immediately. The editing unit edits the data uploaded by the upload unit. For example, the editing unit can cut out unnecessary parts and add subtitles and effects. The editing department can also estimate the user's emotions and adjust the editing style based on the estimated emotions. Furthermore, the editing department can apply different editing algorithms depending on the game genre. For example, the editing department can apply a speedy editing algorithm to action games. The posting department posts the data edited by the editing department to a video distribution site. The posting department can post the data to a video distribution site such as YouTube. Furthermore, the posting department can estimate the user's emotions and adjust the timing of posting based on the estimated emotions. Furthermore, the posting department can analyze trends on the video distribution site and suggest optimal content to post. For example, the posting department can suggest titles and tags that match current trends.As a result, the game video sharing system according to the embodiment can automate the series of tasks of collecting, uploading, editing, and posting game videos.

[0068] The collection unit can collect game play screens and audio data in real time. For example, the collection unit can capture game play screens and collect audio data in real time. For example, the collection unit can collect game screen captures and audio data when the game is being played on a PC. The collection unit can also collect game play screens and audio data when the game is being played on a game console or smartphone. This allows for more accurate game video to be obtained by collecting game play screens and audio data in real time. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for collecting game play screens and audio data in real time.

[0069] The upload unit can upload data to a cloud storage service. The upload unit can upload data to, for example, a cloud storage service. For example, the upload unit can upload data to a cloud storage service such as AWS S3 or Google Drive. The upload unit can also automatically adjust the data compression rate during upload to perform optimal uploading. For example, the upload unit can set a low compression rate when a high-speed Internet connection is used and a high compression rate when a low-speed Internet connection is used. This facilitates data storage and management by uploading data to a cloud storage service. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for uploading data to a cloud storage service.

[0070] The editing unit can cut out unnecessary parts and add subtitles and effects. The editing unit can, for example, cut out unnecessary parts and add subtitles and effects. For example, the editing unit can automatically detect and cut out unnecessary parts of a game video. The editing unit can also add subtitles and effects. For example, the editing unit can set the subtitle format and type of effect and add them to the video. This makes it possible to create a video that is easy for viewers to watch. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit the video using an AI model for cutting out unnecessary parts and adding subtitles and effects.

[0071] The posting unit can post videos to a video distribution site such as YouTube. The posting unit can post videos to a video distribution site such as YouTube. For example, the posting unit can post videos according to YouTube's uploading procedure. The posting unit can also estimate a user's emotions and adjust the timing of posting based on the estimated emotions. For example, the posting unit can post immediately if the user is excited. This allows the video to be shared with many viewers. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post videos using an AI model for posting videos to a video distribution site such as YouTube.

[0072] The collection unit can estimate the user's emotions and adjust the timing of collecting game videos based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting game videos based on the estimated emotions. For example, if the user is excited, the collection unit can start collecting videos just before a boss battle or an important event. Also, if the user is relaxed, the collection unit can collect normal gameplay scenes. Furthermore, if the user is feeling stressed, the collection unit can pause collection and resume it after the user has relaxed. This allows game videos to be collected at a more appropriate time by adjusting the collection timing 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, 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0073] The collection unit can detect a specific event during game play and start collection based on that event. For example, the collection unit can detect a specific event during game play and start collection based on that event. For example, the collection unit can start collection just before a boss battle begins. The collection unit can also start collection the moment a rare item is obtained. Furthermore, the collection unit can start collection when a specific quest is completed. In this way, by starting collection based on a specific event, it is possible to collect without missing important scenes. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for detecting a specific event and starting collection based on that event.

[0074] The collection unit can automatically adjust the resolution and frame rate of the game play screen to perform appropriate collection. The collection unit can automatically adjust the resolution and frame rate of the game play screen to perform appropriate collection. For example, the collection unit can set a high frame rate for fast action scenes. The collection unit can also set a high resolution for scenes with many still images. Furthermore, the collection unit can adjust the resolution and frame rate according to network conditions. This enables optimal collection by automatically adjusting the resolution and frame rate. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for automatically adjusting the resolution and frame rate of the game play screen.

[0075] The collection unit can analyze the user's past play history and prioritize collecting scenes that are particularly noteworthy. The collection unit can, for example, analyze the user's past play history and prioritize collecting scenes that are particularly noteworthy. For example, the collection unit can prioritize collecting scenes that the user has viewed in the past. The collection unit can also prioritize collecting scenes that the user has received high ratings in the past. Furthermore, the collection unit can also prioritize collecting scenes that the user has received many comments in the past. In this way, by analyzing the past play history, it is possible to prioritize collecting scenes that are particularly noteworthy. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for analyzing the user's past play history and priority collecting scenes that are particularly noteworthy.

[0076] The collection unit can estimate the user's emotions and determine the priority of game videos to be collected based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and determine the priority of game videos to be collected based on the estimated emotions. For example, if the user is excited, the collection unit can prioritize collecting action scenes. Also, if the user is relaxed, the collection unit can prioritize collecting exploration scenes. Furthermore, if the user is stressed, the collection unit can pause collection and resume after the user has relaxed. In this way, by determining the priority of game videos to be collected based on the user's emotions, more important scenes can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0077] The collection unit can recognize a user's voice command while playing a game and start or stop collection based on the specific command. For example, the collection unit can recognize a user's voice command while playing a game and start or stop collection based on the specific command. For example, the collection unit can start collection when the user says "start recording." The collection unit can also stop collection when the user says "stop recording." Furthermore, the collection unit can collect specific scenes when the user says "highlight." This simplifies user operation by starting or stopping collection based on a voice command. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for recognizing a user's voice command and starting or stopping collection based on a specific command.

[0078] The collection unit can monitor the remaining battery level of the user's device while playing a game, and suspend collection when the battery becomes low. For example, the collection unit can monitor the remaining battery level of the user's device while playing a game, and suspend collection when the battery becomes low. For example, the collection unit can suspend collection when the remaining battery level falls to 20% or less. The collection unit can also stop collection when the remaining battery level falls to 10% or less. Furthermore, the collection unit can resume collection when the battery is charged. In this way, by monitoring the remaining battery level, it is possible to prevent the device from running out of battery. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model for monitoring the remaining battery level of the user's device and suspending collection when the battery becomes low.

[0079] The collection unit can monitor the user's internet connection status while playing a game, and suspend collection if it determines that the connection is unstable. For example, the collection unit can monitor the user's internet connection status while playing a game, and suspend collection if it determines that the connection is unstable. For example, the collection unit can suspend collection if the internet connection is unstable. The collection unit can also resume collection if the internet connection is restored. Furthermore, the collection unit can stop collection if the internet connection is completely lost. In this way, by monitoring the internet connection status, collection can be suspended if the connection is unstable, preventing data loss. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that monitors the user's internet connection status and suspends collection if it determines that the connection is unstable.

[0080] The upload unit can estimate the user's emotions and adjust the timing of uploading based on the estimated user emotions. The upload unit can, for example, estimate the user's emotions and adjust the timing of uploading based on the estimated emotions. For example, the upload unit can start uploading immediately if the user is relaxed. Furthermore, the upload unit can schedule uploading for later if the user is busy. Furthermore, the upload unit can prioritize uploading important scenes if the user is excited. This allows data to be uploaded at a more appropriate time by adjusting the upload timing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 upload unit can be performed using AI, for example, or without AI. For example, the upload unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The upload unit can automatically adjust the data compression rate during upload to optimize upload. The upload unit can, for example, automatically adjust the data compression rate during upload to optimize upload. For example, the upload unit can set a low compression rate during a high-speed Internet connection and a high compression rate during a low-speed Internet connection. The upload unit can also adjust the compression rate depending on the content of the data. This enables optimal upload by automatically adjusting the data compression rate. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for automatically adjusting the data compression rate.

[0082] The upload unit can monitor the usage status of cloud storage during uploading and select the optimal storage service. The upload unit can, for example, monitor the usage status of cloud storage during uploading and select the optimal storage service. For example, the upload unit can monitor the available space of cloud storage and select the optimal service. The upload unit can also select the optimal service taking into account the cloud storage usage fee. Furthermore, the upload unit can select the optimal service taking into account the security level of the cloud storage. In this way, the optimal storage service can be selected by monitoring the usage status of cloud storage. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can monitor the usage status of cloud storage and upload data using an AI model for selecting the optimal storage service.

[0083] The upload unit can automatically encrypt data when uploading, thereby enhancing security. The upload unit can, for example, automatically encrypt data when uploading, thereby enhancing security. For example, the upload unit can encrypt data before uploading. The upload unit can also encrypt data during uploading. Furthermore, the upload unit can also encrypt data after uploading. In this way, security can be enhanced by automatically encrypting data. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for automatically encrypting data.

[0084] The upload unit can estimate the user's emotions and determine the priority of data to be uploaded based on the estimated user emotions. The upload unit can, for example, estimate the user's emotions and determine the priority of data to be uploaded based on the estimated emotions. For example, if the user is excited, the upload unit can prioritize uploading important scenes. Also, if the user is relaxed, the upload unit can prioritize uploading normal scenes. Furthermore, if the user is feeling stressed, the upload unit can pause uploading and resume it after the user has relaxed. In this way, by determining the priority of data to be uploaded based on the user's emotions, important data can be uploaded preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the upload unit may be performed using AI, or may be performed without AI. For example, the upload unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The upload unit can monitor the user's Internet connection speed during uploading and adjust the upload speed according to the connection speed. For example, the upload unit can monitor the user's Internet connection speed during uploading and adjust the upload speed according to the connection speed. For example, the upload unit can set the upload speed to the maximum when the Internet connection is fast. The upload unit can also set the upload speed to a lower value when the Internet connection is slow. Furthermore, the upload unit can dynamically adjust the upload speed when the Internet connection speed fluctuates. This enables efficient uploading by adjusting the upload speed according to the Internet connection speed. Some or all of the above-described processing in the upload unit can be performed using, for example, AI, or can be performed without using AI. For example, the upload unit can upload data using an AI model for monitoring the user's Internet connection speed and adjusting the upload speed according to the connection speed.

[0086] The upload unit can monitor the storage capacity of the user's device during uploading and automatically delete old data when capacity is insufficient. For example, the upload unit can monitor the storage capacity of the user's device during uploading and automatically delete old data when capacity is insufficient. For example, the upload unit can delete old data when storage capacity exceeds 80%. The upload unit can also prioritize deleting old data when storage capacity exceeds 90%. Furthermore, the upload unit can delete the oldest data when storage capacity reaches 100%. This enables efficient storage utilization by monitoring storage capacity and automatically deleting old data when capacity is insufficient. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for monitoring the storage capacity of the user's device and automatically deleting old data when capacity is insufficient.

[0087] The upload unit can monitor the user's cloud storage usage fees and propose an optimal pricing plan during uploading. The upload unit can, for example, monitor the user's cloud storage usage fees during uploading and propose an optimal pricing plan. For example, the upload unit can propose an optimal pricing plan when the cloud storage usage fees increase. The upload unit can also propose an optimal pricing plan when the cloud storage usage fees decrease. Furthermore, the upload unit can dynamically propose an optimal pricing plan when the cloud storage usage fees fluctuate. This enables cost-effective storage usage by monitoring the cloud storage usage fees and proposing an optimal pricing plan. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can upload data using an AI model for monitoring the user's cloud storage usage fees and proposing an optimal pricing plan.

[0088] The editing unit can estimate the user's emotions and adjust the editing style based on the estimated user emotions. For example, the editing unit can estimate the user's emotions and adjust the editing style based on the estimated emotions. For example, if the user is excited, the editing unit can apply an editing style that emphasizes action scenes. Furthermore, if the user is relaxed, the editing unit can apply an editing style that emphasizes calm scenes. Furthermore, if the user is stressed, the editing unit can apply an editing style that emphasizes relaxing scenes. By adjusting the editing style based on the user's emotions, a more appealing video can be created. 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-mentioned processing in the editing unit can be performed using AI, for example, or without AI. For example, the editing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] The editing department can add effects to emphasize specific game events during editing. For example, the editing department can add effects to emphasize specific game events during editing. For example, the editing department can add special effects to victory scenes. The editing department can also add dramatic effects to defeat scenes. Furthermore, the editing department can add sparkling effects to scenes where rare items are obtained. In this way, adding effects to emphasize specific game events can create videos that are more impressive to viewers. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can edit videos using an AI model to add effects to emphasize specific game events.

[0090] The editing unit can analyze the user's past editing history during editing and propose an optimal editing pattern. For example, the editing unit can analyze the user's past editing history during editing and propose an optimal editing pattern. For example, the editing unit can prioritize and propose effects that the user has used in the past. The editing unit can also propose an editing pattern by referring to scenes that the user has previously cut. Furthermore, the editing unit can also propose the style of subtitles that the user has previously added. In this way, by analyzing the past editing history, an optimal editing pattern for the user can be proposed. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit a video using an AI model that analyzes the user's past editing history and proposes an optimal editing pattern.

[0091] The editing department can apply different editing algorithms depending on the game genre during editing. For example, the editing department can apply different editing algorithms depending on the game genre during editing. For example, the editing department can apply a speedy editing algorithm to an action game. The editing department can also apply a story-focused editing algorithm to an RPG game. Furthermore, the editing department can apply an explanation-focused editing algorithm to a puzzle game. This allows for more appropriate editing by applying an editing algorithm depending on the game genre. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can edit videos using an AI model for applying different editing algorithms depending on the game genre.

[0092] The editing unit can estimate the user's emotions and adjust the length of the edited video based on the estimated user emotions. The editing unit can, for example, estimate the user's emotions and adjust the length of the edited video based on the estimated emotions. For example, if the user is excited, the editing unit can perform a short, impactful edit. Furthermore, if the user is relaxed, the editing unit can perform a longer, more detailed edit. Furthermore, if the user is stressed, the editing unit can perform an edit that includes many relaxing scenes. By adjusting the edited video length based on the user's emotions, the optimal video length for the viewer can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, 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 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 perform emotion estimation.

[0093] The editing unit can determine editing priorities based on game play time during editing. The editing unit can determine editing priorities based on game play time during editing, for example. For example, the editing unit can prioritize editing of scenes played for a long time. The editing unit can also prioritize editing of scenes in which important events occur in a short period of time. Furthermore, the editing unit can prioritize editing of scenes in which play time is concentrated in a specific time period. In this way, by determining editing priorities based on game play time, important scenes can be prioritized for editing. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit videos using an AI model for determining editing priorities based on game play time.

[0094] The editing department can improve the accuracy of editing by referring to game-related information during editing. The editing department can improve the accuracy of editing by referring to game-related information during editing, for example. For example, the editing department can refer to game walkthrough information to perform editing that emphasizes important scenes. The editing department can also refer to reviews to perform editing that emphasizes scenes that are likely to interest viewers. Furthermore, the editing department can refer to official game information to perform editing that reflects the latest update content. In this way, by referring to game-related information, the accuracy of editing can be improved. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can edit videos using an AI model that refers to game-related information to improve the accuracy of editing.

[0095] The editing unit can adjust the editing processing speed based on the performance of the user's device during editing. The editing unit can adjust the editing processing speed based on the performance of the user's device during editing, for example. For example, the editing unit can perform high-speed editing processing on a high-performance device. The editing unit can also adjust the editing speed to reduce the processing load on a low-performance device. Furthermore, the editing unit can adjust the editing processing taking into account the remaining battery level of the device. This enables efficient editing by adjusting the editing processing speed taking into account the performance of the device. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can edit a video using an AI model for adjusting the editing processing speed based on the performance of the user's device.

[0096] The posting unit can estimate the user's emotions and adjust the timing of posting based on the estimated user emotions. The posting unit can, for example, estimate the user's emotions and adjust the timing of posting based on the estimated emotions. For example, if the user is excited, the posting unit can post immediately. Also, if the user is relaxed, the posting unit can suggest the optimal posting time. Furthermore, if the user is feeling stressed, the posting unit can pause posting and resume after the user has relaxed. In this way, by adjusting the timing of posting based on the user's emotions, videos can be posted at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the posting unit may be performed using an AI, for example, or without an AI. For example, the posting unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The posting unit can analyze trends on video streaming sites at the time of posting and suggest optimal content to post. The posting unit can, for example, analyze trends on video streaming sites at the time of posting and suggest optimal content to post. For example, the posting unit can suggest titles and tags that match current trends. The posting unit can also suggest content that viewers are likely to be interested in based on trends. Furthermore, the posting unit can also suggest optimal posting times based on trends. In this way, by analyzing trends on video streaming sites, it is possible to suggest content to post that is attractive to viewers. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post videos using an AI model that analyzes trends on video streaming sites and suggests optimal content to post.

[0098] The posting unit can analyze the user's past posting history at the time of posting and suggest an optimal posting pattern. The posting unit can, for example, analyze the user's past posting history at the time of posting and suggest an optimal posting pattern. For example, the posting unit can suggest a posting pattern in which the user has attracted many viewers in the past. The posting unit can also suggest a posting pattern in which the user has received high ratings in the past. Furthermore, the posting unit can suggest a posting pattern in which the user has received many comments in the past. In this way, by analyzing the past posting history, it is possible to suggest an optimal posting pattern for the user. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can analyze the user's past posting history and post a video using an AI model for suggesting an optimal posting pattern.

[0099] The posting unit can automatically generate metadata for a video when posting. The posting unit can automatically generate metadata for a video when posting, for example. For example, the posting unit can generate an optimal title based on the content of the video. The posting unit can also generate a detailed description based on the content of the video. Furthermore, the posting unit can also generate related tags based on the content of the video. By automatically generating metadata for a video, the effort required for posting can be reduced. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post a video using an AI model for automatically generating metadata for a video.

[0100] The posting unit can estimate the user's emotions and determine the priority of videos to be posted based on the estimated user emotions. The posting unit can, for example, estimate the user's emotions and determine the priority of videos to be posted based on the estimated emotions. For example, if the user is excited, the posting unit can prioritize posting videos containing important scenes. Also, if the user is relaxed, the posting unit can prioritize posting videos containing normal scenes. Furthermore, if the user is feeling stressed, the posting unit can pause posting and resume after the user has relaxed. In this way, by determining the priority of videos to be posted based on the user's emotions, important videos can be posted preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, an AI. For example, the posting unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0101] The posting unit can analyze the viewer demographics of the video distribution site at the time of posting and suggest the optimal posting time. The posting unit can, for example, analyze the viewer demographics of the video distribution site at the time of posting and suggest the optimal posting time. For example, the posting unit can suggest posting during a time period when the viewer demographics are high. The posting unit can also suggest posting during a time period when the viewer demographics are low. Furthermore, the posting unit can adjust the posting time to match the activity times of the viewer demographics. In this way, the optimal posting time can be suggested by analyzing the viewer demographics. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can post a video using an AI model for analyzing the viewer demographics of the video distribution site and suggesting the optimal posting time.

[0102] The posting unit can analyze the algorithm of the video distribution site at the time of posting and suggest the optimal posting method. The posting unit can, for example, analyze the algorithm of the video distribution site at the time of posting and suggest the optimal posting method. For example, the posting unit can suggest the optimal tag based on the algorithm. The posting unit can also suggest the optimal title based on the algorithm. Furthermore, the posting unit can also suggest the optimal description based on the algorithm. In this way, the optimal posting method can be suggested by analyzing the algorithm of the video distribution site. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can analyze the algorithm of the video distribution site and post the video using an AI model for suggesting the optimal posting method.

[0103] The posting unit can cooperate with the user's social media account when posting a video to promote the spread of the video. The posting unit can cooperate with the user's social media account when posting a video to promote the spread of the video. For example, the posting unit can automatically share the posted video on social media. The posting unit can also send notifications to social media followers. Furthermore, the posting unit can analyze reactions on social media and suggest the optimal method of spreading the video. In this way, by coordinating with the social media account, the spread of the video can be promoted. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can cooperate with the user's social media account and post the video using an AI model for promoting the spread of the video. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, upload unit, editing unit, and posting unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects game video data in real time using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A starts the collection. The upload unit, realized, for example, by the specific processing unit 290 of the data processing device 12, uploads the collected data to cloud storage. The editing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, cuts out unnecessary parts and adds subtitles and effects. The posting unit, realized, for example, by the control unit 46A of the smart device 14, posts the edited data to a video distribution site such as YouTube. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, upload unit, editing unit, and posting unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects game video data in real time using the camera 42 and microphone 238 of the smart glasses 214, and the collection is initiated by the control unit 46A. The upload unit, realized, for example, by the specific processing unit 290 of the data processing device 12, uploads the collected data to cloud storage. The editing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, cuts out unnecessary parts and adds subtitles and effects. The posting unit, realized, for example, by the control unit 46A of the smart glasses 214, posts the edited data to a video distribution site such as YouTube. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, upload unit, editing unit, and posting unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects game video data in real time using the camera 42 and microphone 238 of the headset-type terminal 314, and the collection is started by the control unit 46A. The upload unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uploads the collected data to cloud storage. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cuts out unnecessary parts and adds subtitles and effects. The posting unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and posts the edited data to a video distribution site such as YouTube. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, upload unit, editing unit, and posting unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects game video data in real time using the camera 42 and microphone 238 of the robot 414, and the collection is started by the control unit 46A. The upload unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uploads the collected data to cloud storage. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cuts out unnecessary parts and adds subtitles and effects. The posting unit is realized, for example, by the control unit 46A of the robot 414, and posts the edited data to a video distribution site such as YouTube.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can also collect biometric data of the user while playing a game and adjust the collection timing based on this data. For example, the collection unit can monitor the user's heart rate and galvanic skin response and estimate the user's excitement level and stress level from this data. If the user's heart rate suddenly increases, the collection unit can determine that an important scene is occurring and start collection. Also, if the galvanic skin response increases, the collection unit can determine that the user is nervous and pause collection. Furthermore, the collection unit can analyze the user's biometric data over the long term and learn the optimal collection timing for each individual user.

[0106] The upload module can monitor the user's internet connection speed in real time and adjust upload priorities accordingly. For example, if the connection speed drops, it can pause uploading less important data and resume it when the connection speed improves. It can also prioritize uploading important scenes when the connection speed is stable. Furthermore, the upload module can analyze the user's internet connection speed history and suggest optimal upload times.

[0107] The editing department can analyze a user's past viewing history and suggest editing styles that the viewer prefers. For example, it can analyze the editing styles of videos the user has previously watched and apply a similar style. It can also perform editing that emphasizes scenes or effects that the viewer particularly likes. It can also suggest new editing styles that the viewer might be interested in based on the viewing history.

[0108] The posting unit can link with the user's social media accounts to promote the spread of videos. For example, the posting unit can automatically share posted videos on social media. It can also send notifications to social media followers. It can also analyze reactions on social media and suggest the optimal way to spread the video. In this way, linking with social media accounts can promote the spread of videos.

[0109] The collection unit can estimate the user's emotions and determine the priority of game videos to be collected based on the estimated emotions. For example, if the user is excited, the collection unit can prioritize collecting action scenes. Also, if the user is relaxed, the collection unit can prioritize collecting exploration scenes. Furthermore, if the user is feeling stressed, the collection unit can pause collection and resume it after the user has relaxed. In this way, by determining the priority of game videos to be collected based on the user's emotions, more important scenes can be collected with priority.

[0110] The editing department can estimate the user's emotions and adjust the length of the edited video based on the estimated emotions. For example, if the user is excited, the editing department can make a short, impactful edit. If the user is relaxed, the editing department can make a longer, more detailed edit. Furthermore, if the user is feeling stressed, the editing department can make an edit that includes many relaxing scenes. In this way, by adjusting the edited length based on the user's emotions, it is possible to achieve a video length that is optimal for the viewer.

[0111] The posting unit can estimate the user's emotions and adjust the timing of posting based on the estimated emotions. For example, if the user is excited, the posting unit can post immediately. Also, if the user is relaxed, the posting unit can suggest the optimal time to post. Furthermore, if the user is feeling stressed, the posting unit can pause posting and resume it after the user has relaxed. In this way, by adjusting the timing of posting based on the user's emotions, videos can be posted at more appropriate times.

[0112] The upload unit can estimate the user's emotions and adjust the timing of uploading based on the estimated emotions. For example, if the user is relaxed, the upload unit can start uploading immediately. If the user is busy, the upload unit can also schedule uploading for later. Furthermore, if the user is excited, the upload unit can prioritize uploading important scenes. In this way, by adjusting the upload timing based on the user's emotions, data can be uploaded at a more appropriate time.

[0113] The editing department can analyze the user's past editing history and suggest the optimal editing pattern. For example, the editing department can prioritize and suggest effects that the user has used in the past. The editing department can also suggest editing patterns by referring to scenes that the user has previously cut. Furthermore, the editing department can also suggest styles of subtitles that the user has previously added. In this way, by analyzing the user's past editing history, the editing department can suggest the optimal editing pattern for the user.

[0114] The posting unit can analyze trends on video streaming sites at the time of posting and suggest optimal posting content. For example, the posting unit can suggest titles and tags that match current trends. The posting unit can also suggest content that viewers are likely to be interested in based on trends. Furthermore, the posting unit can also suggest optimal posting times based on trends. In this way, by analyzing trends on video streaming sites, it is possible to suggest posting content that will be attractive to viewers.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects game video data. For example, the collection unit can collect game play screen and audio data in real time. The collection unit can also detect a specific event during game play and start collection based on the event. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of collection based on the estimated emotions. For example, if the user is excited, the collection unit can start collection just before a boss battle or an important event. Step 2: The uploading unit uploads the data collected by the collecting unit to cloud storage. The uploading unit can upload data to a cloud storage service, for example. The uploading unit can also automatically adjust the data compression rate during uploading to perform optimal uploading. Furthermore, the uploading unit can estimate the user's emotions and adjust the timing of uploading based on the estimated emotions. For example, the uploading unit can start uploading immediately if the user is relaxed. Step 3: The editing department edits the data uploaded by the uploading department. For example, the editing department can cut out unnecessary parts and add subtitles and effects. The editing department can also estimate the user's emotions and adjust the editing style based on the estimated emotions. Furthermore, the editing department can apply different editing algorithms depending on the game genre. For example, the editing department can apply a speedy editing algorithm to an action game. Step 4: The posting unit posts the data edited by the editing unit to a video distribution site. The posting unit can post the data to a video distribution site such as YouTube, for example. The posting unit can also estimate the user's emotions and adjust the timing of posting based on the estimated emotions. Furthermore, the posting unit can analyze trends on the video distribution site and suggest optimal content to post. For example, the posting unit can suggest titles and tags that match current trends.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

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

[0135] 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.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0147] In the 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.

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

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

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

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects game video data; an upload unit that uploads the data collected by the collection unit to a cloud storage; an editing unit that edits the data uploaded by the upload unit; a posting unit that posts the data edited by the editing unit to a video distribution site; Equipped with A system characterized by:

2. The collecting unit Instantly collect gameplay screen and audio data 2. The system of claim 1.

3. The upload unit Uploading data to a cloud storage service 2. The system of claim 1.

4. The editorial department Cut out unwanted parts and add subtitles and effects 2. The system of claim 1.

5. The collecting unit Estimate user emotions and adjust the timing of game video collection based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Detecting specific events during gameplay and triggering collection based on those events 2. The system of claim 1.

7. The collecting unit Automatically adjusts the gameplay screen resolution and frame rate for optimal collection 2. The system of claim 1.

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