system

Abstract

The system according to the embodiment aims to efficiently edit moving images. [Solution] A system according to an embodiment includes a collection unit, a learning unit, an input unit, an editing unit, and an output unit. The collection unit collects past editing data of a video poster. The learning unit learns the editing style of the video poster based on the editing data collected by the collection unit. The input unit inputs raw footage shot by the video poster. The editing unit automatically adds captions and sound effects based on the raw footage input by the input unit. The output unit outputs the video edited by the editing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that video editing requires a lot of time and effort, making it difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently edit moving images. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, an input unit, an editing unit, and an output unit. The collection unit collects past editing data of video posters. The learning unit learns the editing style of the video poster based on the editing data collected by the collection unit. The input unit inputs raw footage shot by the video poster. The editing unit automatically adds captions and sound effects based on the raw footage input by the input unit. The output unit outputs the video edited by the editing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently edit moving images. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A video editing system according to an embodiment of the present invention utilizes a generation AI to quickly complete video editing. In this video editing system, a video uploader shoots live video footage and inputs the footage into a generation AI. The generation AI uses past editing data to learn the uploader's editing style and automatically adds captions, sound effects, and other elements. This allows the video uploader to significantly reduce the time it takes to edit. This system can also be used for any video media, including television news flashes. For example, in this video editing system, a video uploader shoots live video footage. This footage is input into a generation AI. The generation AI automatically adds captions, sound effects, and other elements based on the uploader's editing style, which it has previously learned. For example, the generation AI automatically inserts captions and sound effects frequently used by the video uploader in a specific scene. The generation AI then outputs the video after the editing process. The video uploader can review this video and make any necessary modifications. For example, the video uploader can change the position and content of the captions inserted by the generation AI. This allows the video uploader to significantly reduce the time it takes to edit. For example, a task that would take several hours with conventional manual editing can be completed in just a few minutes using generative AI. This system can also be used with any video medium, such as television news flashes. For example, the generative AI can automatically add captions and sound effects to breaking news footage, enabling rapid information provision. This allows the video editing system to significantly reduce the labor required by video uploaders and enable rapid information provision. For example, a video uploader simply shoots live footage and inputs the footage into the generative AI, which then performs the editing process automatically. This allows video uploaders to significantly reduce the time required for editing. This system can also be used with any video medium, such as television news flashes. For example, the generative AI can automatically add captions and sound effects to breaking news footage, enabling rapid information provision.

[0029] A video editing system according to an embodiment includes a collection unit, a learning unit, an input unit, an editing unit, and an output unit. The collection unit collects past editing data of a video poster. The past editing data of a video poster includes, but is not limited to, examples of cutting videos, adding subtitles, and inserting sound effects. The collection unit, for example, collects data on videos previously edited by the video poster. The collection unit can also collect data on editing tools and editing techniques previously used by the video poster. For example, the collection unit collects log data of editing software used by the video poster. The learning unit learns the editing style of the video poster based on the editing data collected by the collection unit. For example, the learning unit analyzes the collected editing data to identify the editing style of the video poster. The learning unit can also model the editing style of the video poster based on the collected editing data. For example, the learning unit models the editing style of the video poster using a machine learning algorithm. The input unit inputs raw video footage shot by the video poster. The raw video may include, for example, resolution, frame rate, file format, etc., but is not limited to these examples. The input unit, for example, inputs raw video shot by a video poster in a digital format. The input unit can also input raw video shot by a video poster in real time. For example, the input unit inputs raw video shot by a video poster in a streaming format. The editing unit automatically adds captions and sound effects based on the raw video input by the input unit. The editing unit automatically adds captions and sound effects using a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the editing unit automatically adds captions to the raw video using the generation AI. The editing unit can also automatically add sound effects to the raw video using the generation AI. For example, the editing unit automatically inserts captions and sound effects that are frequently used by the video poster in a specific scene using the generation AI. The output unit outputs the video edited by the editing unit. For example, the output unit outputs the edited video in a digital format. The output unit can also output the edited video in real time.For example, the output unit outputs the edited video in a streaming format. This allows the video editing system according to the embodiment to improve the efficiency of the video uploader's editing work and quickly complete video editing. For example, the video uploader simply shoots raw footage and inputs the footage into the generation AI, and the editing work is performed automatically. This allows the video uploader to significantly reduce the time required for editing work.

[0030] When collecting past edited data, the collection unit can weight the data based on the user's edit frequency. For example, the collection unit can weight edited data that the user uses frequently, and can weight edited data that the user uses infrequently. The collection unit can also weight edited data that the user uses frequently in a specific period, and can weight edited data that the user uses frequently. In this way, by weighting the data based on the user's edit frequency, important edited data can be collected preferentially. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's edit frequency data to a generation AI and have the generation AI weight the data.

[0031] When collecting editing data, the collection unit can adjust the collection range taking into account changes in the user's editing style. For example, if the user's editing style changes, the collection unit expands the collection range to match the new style. Furthermore, if the user's editing style remains constant, the collection unit can narrow the collection range. Furthermore, if the user's editing style is diverse, the collection unit can adjust the collection range to accommodate multiple styles. In this way, by adjusting the collection range taking into account changes in the user's editing style, data corresponding to the latest editing style can be collected. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's editing style data to a generation AI and cause the generation AI to adjust the collection range.

[0032] When collecting editing data, the collection unit can apply a different collection method for each user's project. For example, for project A, the collection unit prioritizes collecting editing data related to a specific theme. The collection unit can also customize the collection method for project B based on the user's past feedback. The collection unit can also adjust the collection method for project C according to the user's current needs. In this way, by applying a different collection method for each project, it is possible to collect optimal editing data for each project. 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 input the collection method for each project to the generation AI and have the generation AI apply the collection method.

[0033] During learning, the learning unit can improve the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also select an algorithm suitable for a specific editing style from the past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. This improves the accuracy of learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to improve the learning algorithm.

[0034] During learning, the learning unit can analyze variations in the user's editing style and adjust the update frequency of the learning data. For example, if the user's editing style changes frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's editing style remains constant, the learning unit can also decrease the update frequency of the learning data. Furthermore, if the user's editing style changes with the seasons, the learning unit can adjust the update frequency of the learning data according to the seasons. In this way, by analyzing variations in the user's editing style and adjusting the update frequency of the learning data, it is possible to respond to the latest editing styles. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's editing style data into the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0035] During learning, the learning unit can update the learning data to reflect user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also prioritize learning data suitable for a specific editing style based on user feedback. The learning unit can also reflect user feedback in real time and optimize the learning data. This improves the accuracy of learning by updating the learning data to reflect user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0036] When inputting raw video, the input unit can filter the raw video based on the user's current project or area of ​​interest. For example, the input unit prioritizes input of raw video related to a project currently underway by the user. The input unit can also prioritize input of raw video related to the user's area of ​​interest. The input unit can also filter the raw video based on the user's past project history. This allows highly relevant raw video to be input by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's project data and area of ​​interest data to the generation AI and cause the generation AI to perform filtering.

[0037] When inputting raw video, the input unit can select an appropriate input means according to the user's input method. For example, if the user prefers voice input, the input unit can prioritize voice input. Also, if the user prefers text input, the input unit can prioritize text input. Also, if the user prefers image input, the input unit can prioritize image input. This enables efficient input of raw video by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0038] When inputting raw video, the input unit can optimize the input method by referring to the user's past input history. The input unit, for example, suggests an optimal input method based on input methods used by the user in the past. The input unit can also preferentially use a specific input method based on the user's past input history. The input unit can also analyze the user's past input history and suggest the most efficient input method. This enables efficient input of raw video by optimizing the input method by referring to the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and cause the generation AI to optimize the input method.

[0039] During editing, the editing department can add captions and sound effects by referring to the user's past editing style for a particular scene. For example, the editing department automatically adds captions that the user used in a particular scene in the past. The editing department can also automatically add sound effects that the user used in a particular scene in the past. The editing department can also analyze the user's past editing style and suggest optimal captions and sound effects. This allows more appropriate captions and sound effects to be added by referring to the user's past editing style for a particular scene. Some or all of the above-described processing by the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's past editing style data into a generation AI and have the generation AI add captions and sound effects.

[0040] During editing, the editing department can improve the editing method based on changes in the user's editing style. For example, if the user's editing style changes, the editing department adjusts the editing method to match the new style. The editing department can also fix the editing method if the user's editing style is constant. The editing department can also optimize the editing method to accommodate multiple styles if the user's editing style is diverse. This makes it possible to accommodate the latest editing style by optimizing the editing method in consideration of changes in the user's editing style. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's editing style data into a generation AI and have the generation AI improve the editing method.

[0041] During editing, the editing department can improve the method of adding captions and sound effects by reflecting user feedback. For example, the editing department can adjust the method of adding captions and sound effects based on feedback provided by the user. The editing department can also prioritize adding specific captions and sound effects based on user feedback. The editing department can also reflect user feedback in real time and optimize the method of adding captions and sound effects. This improves the accuracy of editing by improving the method of adding captions and sound effects by reflecting user feedback. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input user feedback data into a generation AI and have the generation AI improve the method of adding captions and sound effects.

[0042] The output unit can optimize the output method by referring to the user's past output history at the time of output. For example, the output unit can suggest an optimal output method based on output methods used by the user in the past. The output unit can also preferentially use a specific output method based on the user's past output history. The output unit can also analyze the user's past output history and suggest the most efficient output method. This enables efficient video output by optimizing the output method by referring to the user's past output history. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's past output history data into a generation AI and cause the generation AI to optimize the output method.

[0043] The output unit can customize the output content based on the user's current project or area of ​​interest at the time of output. For example, the output unit can prioritize providing output content related to the user's current project. The output unit can also prioritize providing output content related to the user's area of ​​interest. The output unit can also customize the output content based on the user's past project history. This allows for customizing the output content based on the user's current project or area of ​​interest, thereby outputting highly relevant videos. Some or all of the above-described processing in the output unit can be performed using, for example, AI, or can be performed without using AI. For example, the output unit can input the user's project data and area of ​​interest data into a generation AI and have the generation AI customize the output content.

[0044] The output unit can improve the output method by reflecting user feedback at the time of output. The output unit can adjust the output method based on, for example, feedback provided by the user. The output unit can also preferentially use a specific output method based on the user feedback. The output unit can also reflect user feedback in real time and optimize the output method. This improves the accuracy of the output by improving the output method by reflecting user feedback. Some or all of the above-mentioned processing in the output unit can be performed, for example, using AI or without AI. For example, the output unit can input user feedback data to a generation AI and have the generation AI improve the output method.

[0045] At the time of output, the output unit can prioritize outputting highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, the output unit can prioritize outputting videos related to that area. Furthermore, if the user is traveling, the output unit can prioritize outputting videos related to the travel destination. Furthermore, if the user is at home, the output unit can prioritize outputting videos related to the area around the user's home. In this way, by outputting highly relevant videos by taking into account the user's geographical location information, more appropriate videos can be provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant videos.

[0046] At the time of output, the output unit can analyze the user's social media activity and output related videos. For example, the output unit outputs videos related to content shared by the user on social media. The output unit can also analyze the content posted by the user on social media and output related videos. The output unit can also output related videos by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and outputting related videos, more appropriate videos can be provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media activity data to a generation AI and cause the generation AI to select related videos.

[0047] The output unit can customize the output method by reflecting the user's past feedback at the time of output. The output unit can adjust the output method based on, for example, feedback provided by the user in the past. The output unit can also preferentially use a specific output method based on the user's past feedback. The output unit can also reflect the user's feedback in real time and optimize the output method. This enables more appropriate video output by customizing the output method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the output unit can be performed using, for example, AI, or can be performed without using AI. For example, the output unit can input the user's past feedback data into a generation AI and have the generation AI customize the output method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] In addition to the user's editing style, the collection unit can also collect the user's viewing history and determine the priority of editing data based on the viewing history. For example, if the user frequently watches videos of a specific genre, the collection unit can prioritize collecting editing data related to that genre. Also, if the user watches many videos by a specific creator, the collection unit can prioritize collecting data similar to the creator's editing style. Furthermore, the collection unit can analyze the ratings and comments on videos watched by the user and prioritize collecting editing styles that have high ratings. This allows for more appropriate editing data to be collected based on the user's viewing history.

[0050] The collection unit can weight data based on the user's editing time period in addition to the user's editing frequency. For example, if the user often edits at night, a high weight can be assigned to edited data suitable for nighttime. Also, if the user often edits on weekends, a high weight can be assigned to edited data suitable for weekends. Furthermore, if the user edits frequently in a particular season, a high weight can be assigned to edited data suitable for that season. In this way, by weighting data based on the user's editing time period, more appropriate edited data can be collected.

[0051] The collection unit can adjust the collection range based on the user's editing purpose in addition to changes in the user's editing style. For example, if the user is creating a promotional video, it can prioritize collecting edited data suitable for promotion. Also, if the user is creating an educational video, it can prioritize collecting edited data suitable for education. Furthermore, if the user is creating an entertainment video, it can prioritize collecting edited data suitable for entertainment. In this way, by adjusting the collection range based on the user's editing purpose, more appropriate edited data can be collected.

[0052] In addition to using different collection methods for each project, the collection department can also adjust the collection method based on the progress of the user's project. For example, in the early stages of a project, a wide range of editing data is collected, and in the middle stages, editing data related to a specific theme is collected. In addition, in the final stages of a project, the collection method can be customized based on user feedback. Furthermore, the priority of collected data can be adjusted depending on the progress of the project. This allows more appropriate editing data to be collected by adjusting the collection method based on the progress of the project.

[0053] The learning unit can improve the learning algorithm by referring to the learning data of other users in addition to past learning data. For example, the learning unit can analyze the editing styles used by other users and select the optimal learning algorithm. It can also select an algorithm suitable for a specific editing style from the learning data of other users. It can also analyze the learning data of other users and adjust the parameters of the learning algorithm. In this way, the learning algorithm can be optimized by referring to the learning data of other users, thereby improving the accuracy of learning.

[0054] In addition to variations in the user's editing style, the learning unit can also adjust the update frequency of the learning data based on the user's editing environment. For example, if the user edits using different devices, the update frequency of the learning data can be adjusted depending on the device. Also, if the user edits using different software, the update frequency of the learning data can be adjusted depending on the software. Furthermore, if the user edits in different locations, the update frequency of the learning data can be adjusted depending on the location. In this way, by adjusting the update frequency of the learning data based on the user's editing environment, it is possible to respond to the latest editing styles.

[0055] In addition to user feedback, the learning unit can also update the learning data to reflect the user's social media activity. For example, data related to content shared by the user on social media can be prioritized for learning. The learning unit can also analyze the content posted by the user on social media and prioritize learning related data. Furthermore, the learning unit can also prioritize learning related data by referring to the activities of the user's friends on social media. In this way, updating the learning data to reflect the user's social media activity improves the accuracy of learning.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects the video uploader's past editing data. This includes cutting videos, adding subtitles, and inserting sound effects. The collection unit also collects data on the editing tools and techniques used by the video uploader in the past. For example, it collects log data from editing software. Step 2: The learning unit learns the editing style of the video poster based on the editing data collected by the collection unit. The learning unit analyzes the collected editing data, identifies the editing style of the video poster, and models it using a machine learning algorithm. Step 3: The input unit inputs the raw video captured by the video uploader. The raw video includes the resolution, frame rate, file format, etc. The input unit inputs the raw video captured by the video uploader in digital format, and can also input it in real-time streaming format. Step 4: The editing department automatically adds captions and sound effects based on the raw footage input by the input department. Using generative AI, captions and sound effects are automatically added, and captions and sound effects that are frequently used by video uploaders in specific scenes are inserted. Step 5: The output unit outputs the video edited by the editing unit. The edited video can be output in a digital format and can also be output in a streaming format in real time.

[0058] (Example 2) A video editing system according to an embodiment of the present invention utilizes a generation AI to quickly complete video editing. In this video editing system, a video uploader shoots live video footage and inputs the footage into a generation AI. The generation AI uses past editing data to learn the uploader's editing style and automatically adds captions, sound effects, and other elements. This allows the video uploader to significantly reduce the time it takes to edit. This system can also be used for any video media, including television news flashes. For example, in this video editing system, a video uploader shoots live video footage. This footage is input into a generation AI. The generation AI automatically adds captions, sound effects, and other elements based on the uploader's editing style, which it has previously learned. For example, the generation AI automatically inserts captions and sound effects frequently used by the video uploader in a specific scene. The generation AI then outputs the video after the editing process. The video uploader can review this video and make any necessary modifications. For example, the video uploader can change the position and content of the captions inserted by the generation AI. This allows the video uploader to significantly reduce the time it takes to edit. For example, a task that would take several hours with conventional manual editing can be completed in just a few minutes using generative AI. This system can also be used with any video medium, such as television news flashes. For example, the generative AI can automatically add captions and sound effects to breaking news footage, enabling rapid information provision. This allows the video editing system to significantly reduce the labor required by video uploaders and enable rapid information provision. For example, a video uploader simply shoots live footage and inputs the footage into the generative AI, which then performs the editing process automatically. This allows video uploaders to significantly reduce the time required for editing. This system can also be used with any video medium, such as television news flashes. For example, the generative AI can automatically add captions and sound effects to breaking news footage, enabling rapid information provision.

[0059] A video editing system according to an embodiment includes a collection unit, a learning unit, an input unit, an editing unit, and an output unit. The collection unit collects past editing data of a video poster. The past editing data of a video poster includes, but is not limited to, examples of cutting videos, adding subtitles, and inserting sound effects. The collection unit, for example, collects data on videos previously edited by the video poster. The collection unit can also collect data on editing tools and editing techniques previously used by the video poster. For example, the collection unit collects log data of editing software used by the video poster. The learning unit learns the editing style of the video poster based on the editing data collected by the collection unit. For example, the learning unit analyzes the collected editing data to identify the editing style of the video poster. The learning unit can also model the editing style of the video poster based on the collected editing data. For example, the learning unit models the editing style of the video poster using a machine learning algorithm. The input unit inputs raw video footage shot by the video poster. The raw video may include, for example, resolution, frame rate, file format, etc., but is not limited to these examples. The input unit, for example, inputs raw video shot by a video poster in a digital format. The input unit can also input raw video shot by a video poster in real time. For example, the input unit inputs raw video shot by a video poster in a streaming format. The editing unit automatically adds captions and sound effects based on the raw video input by the input unit. The editing unit automatically adds captions and sound effects using a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the editing unit automatically adds captions to the raw video using the generation AI. The editing unit can also automatically add sound effects to the raw video using the generation AI. For example, the editing unit automatically inserts captions and sound effects that are frequently used by the video poster in a specific scene using the generation AI. The output unit outputs the video edited by the editing unit. For example, the output unit outputs the edited video in a digital format. The output unit can also output the edited video in real time.For example, the output unit outputs the edited video in a streaming format. This allows the video editing system according to the embodiment to improve the efficiency of the video uploader's editing work and quickly complete video editing. For example, the video uploader simply shoots raw footage and inputs the footage into the generation AI, and the editing work is performed automatically. This allows the video uploader to significantly reduce the time required for editing work.

[0060] The collection unit can estimate the user's emotions and determine the priority of edited data to be collected based on the estimated user's emotions. For example, if the user is stressed, the collection unit can prioritize collecting relaxing edited data. Furthermore, if the user is excited, the collection unit can prioritize collecting energetic edited data. Furthermore, if the user is tired, the collection unit can prioritize collecting simple and intuitive edited data. This allows for more appropriate edited data to be collected by prioritizing edited data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of edited data based on emotions.

[0061] When collecting past edited data, the collection unit can weight the data based on the user's edit frequency. For example, the collection unit can weight edited data that the user uses frequently, and can weight edited data that the user uses infrequently. The collection unit can also weight edited data that the user uses frequently in a specific period, and can weight edited data that the user uses frequently. In this way, by weighting the data based on the user's edit frequency, important edited data can be collected preferentially. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's edit frequency data to a generation AI and have the generation AI weight the data.

[0062] When collecting editing data, the collection unit can adjust the collection range taking into account changes in the user's editing style. For example, if the user's editing style changes, the collection unit expands the collection range to match the new style. Furthermore, if the user's editing style remains constant, the collection unit can narrow the collection range. Furthermore, if the user's editing style is diverse, the collection unit can adjust the collection range to accommodate multiple styles. In this way, by adjusting the collection range taking into account changes in the user's editing style, data corresponding to the latest editing style can be collected. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's editing style data to a generation AI and cause the generation AI to adjust the collection range.

[0063] When collecting editing data, the collection unit can apply a different collection method for each user's project. For example, for project A, the collection unit prioritizes collecting editing data related to a specific theme. The collection unit can also customize the collection method for project B based on the user's past feedback. The collection unit can also adjust the collection method for project C according to the user's current needs. In this way, by applying a different collection method for each project, it is possible to collect optimal editing data for each project. 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 input the collection method for each project to the generation AI and have the generation AI apply the collection method.

[0064] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can prioritize learning data with a gentle editing style. Furthermore, if the user is excited, the learning unit can prioritize learning data with a dynamic editing style. Furthermore, if the user is tired, the learning unit can prioritize learning data with a simple and intuitive editing style. This allows for the selection of training data according to the user's emotions, thereby learning a more appropriate editing style. 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 learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to select training data based on the emotion.

[0065] During learning, the learning unit can improve the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also select an algorithm suitable for a specific editing style from the past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. This improves the accuracy of learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to improve the learning algorithm.

[0066] During learning, the learning unit can analyze variations in the user's editing style and adjust the update frequency of the learning data. For example, if the user's editing style changes frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's editing style remains constant, the learning unit can also decrease the update frequency of the learning data. Furthermore, if the user's editing style changes with the seasons, the learning unit can adjust the update frequency of the learning data according to the seasons. In this way, by analyzing variations in the user's editing style and adjusting the update frequency of the learning data, it is possible to respond to the latest editing styles. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's editing style data into the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0067] During learning, the learning unit can update the learning data to reflect user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also prioritize learning data suitable for a specific editing style based on user feedback. The learning unit can also reflect user feedback in real time and optimize the learning data. This improves the accuracy of learning by updating the learning data to reflect user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0068] The input unit can estimate the user's emotion and adjust the input timing of the raw video based on the estimated user's emotion. For example, when the user is relaxed, the input unit inputs the raw video at a relaxed timing. Furthermore, when the user is excited, the input unit can input the raw video at a rapid timing. Furthermore, when the user is tired, the input unit can input the raw video at an appropriate timing. By adjusting the input timing according to the user's emotion, the raw video can be input at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the input timing based on the emotion.

[0069] When inputting raw video, the input unit can filter the raw video based on the user's current project or area of ​​interest. For example, the input unit prioritizes input of raw video related to a project currently underway by the user. The input unit can also prioritize input of raw video related to the user's area of ​​interest. The input unit can also filter the raw video based on the user's past project history. This allows highly relevant raw video to be input by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's project data and area of ​​interest data to the generation AI and cause the generation AI to perform filtering.

[0070] When inputting raw video, the input unit can select an appropriate input means according to the user's input method. For example, if the user prefers voice input, the input unit can prioritize voice input. Also, if the user prefers text input, the input unit can prioritize text input. Also, if the user prefers image input, the input unit can prioritize image input. This enables efficient input of raw video by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0071] When inputting raw video, the input unit can optimize the input method by referring to the user's past input history. The input unit, for example, suggests an optimal input method based on input methods used by the user in the past. The input unit can also preferentially use a specific input method based on the user's past input history. The input unit can also analyze the user's past input history and suggest the most efficient input method. This enables efficient input of raw video by optimizing the input method by referring to the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and cause the generation AI to optimize the input method.

[0072] The editing department can estimate the user's emotions and adjust the method of adding captions and sound effects based on the estimated user's emotions. For example, if the user is relaxed, the editing department can add gentle captions and sound effects. If the user is excited, the editing department can add dynamic captions and sound effects. If the user is tired, the editing department can add simple, intuitive captions and sound effects. This enables more appropriate editing by adjusting the method of adding captions and sound effects according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or without AI. For example, the editing department can input the user's emotion data into the generation AI and have the generation AI adjust the method of adding captions and sound effects based on the emotion.

[0073] During editing, the editing department can add captions and sound effects by referring to the user's past editing style for a particular scene. For example, the editing department automatically adds captions that the user used in a particular scene in the past. The editing department can also automatically add sound effects that the user used in a particular scene in the past. The editing department can also analyze the user's past editing style and suggest optimal captions and sound effects. This allows more appropriate captions and sound effects to be added by referring to the user's past editing style for a particular scene. Some or all of the above-described processing by the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's past editing style data into a generation AI and have the generation AI add captions and sound effects.

[0074] During editing, the editing department can improve the editing method based on changes in the user's editing style. For example, if the user's editing style changes, the editing department adjusts the editing method to match the new style. The editing department can also fix the editing method if the user's editing style is constant. The editing department can also optimize the editing method to accommodate multiple styles if the user's editing style is diverse. This makes it possible to accommodate the latest editing style by optimizing the editing method in consideration of changes in the user's editing style. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's editing style data into a generation AI and have the generation AI improve the editing method.

[0075] During editing, the editing department can improve the method of adding captions and sound effects by reflecting user feedback. For example, the editing department can adjust the method of adding captions and sound effects based on feedback provided by the user. The editing department can also prioritize adding specific captions and sound effects based on user feedback. The editing department can also reflect user feedback in real time and optimize the method of adding captions and sound effects. This improves the accuracy of editing by improving the method of adding captions and sound effects by reflecting user feedback. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input user feedback data into a generation AI and have the generation AI improve the method of adding captions and sound effects.

[0076] The output unit can estimate the user's emotions and prioritize the videos to be output based on the estimated user's emotions. For example, if the user is relaxed, the output unit can prioritize calm videos. Furthermore, if the user is excited, the output unit can prioritize dynamic videos. Furthermore, if the user is tired, the output unit can prioritize simple and intuitive videos. This allows for more appropriate videos to be output by prioritizing videos according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the output unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the output unit can input the user's emotion data to the generation AI and cause the generation AI to prioritize videos based on the emotions.

[0077] The output unit can optimize the output method by referring to the user's past output history at the time of output. For example, the output unit can suggest an optimal output method based on output methods used by the user in the past. The output unit can also preferentially use a specific output method based on the user's past output history. The output unit can also analyze the user's past output history and suggest the most efficient output method. This enables efficient video output by optimizing the output method by referring to the user's past output history. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's past output history data into a generation AI and cause the generation AI to optimize the output method.

[0078] The output unit can customize the output content based on the user's current project or area of ​​interest at the time of output. For example, the output unit can prioritize providing output content related to the user's current project. The output unit can also prioritize providing output content related to the user's area of ​​interest. The output unit can also customize the output content based on the user's past project history. This allows for customizing the output content based on the user's current project or area of ​​interest, thereby outputting highly relevant videos. Some or all of the above-described processing in the output unit can be performed using, for example, AI, or can be performed without using AI. For example, the output unit can input the user's project data and area of ​​interest data into a generation AI and have the generation AI customize the output content.

[0079] The output unit can improve the output method by reflecting user feedback at the time of output. The output unit can adjust the output method based on, for example, feedback provided by the user. The output unit can also preferentially use a specific output method based on the user feedback. The output unit can also reflect user feedback in real time and optimize the output method. This improves the accuracy of the output by improving the output method by reflecting user feedback. Some or all of the above-mentioned processing in the output unit can be performed, for example, using AI or without AI. For example, the output unit can input user feedback data to a generation AI and have the generation AI improve the output method.

[0080] The output unit can estimate the user's emotion and adjust the display method of the video to be output based on the estimated user's emotion. For example, the output unit can provide a calm display method when the user is relaxed. The output unit can also provide a dynamic display method when the user is excited. The output unit can also provide a simple and intuitive display method when the user is tired. This enables more appropriate video display by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the output unit can be performed using, for example, an AI, or without an AI. For example, the output unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0081] At the time of output, the output unit can prioritize outputting highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, the output unit can prioritize outputting videos related to that area. Furthermore, if the user is traveling, the output unit can prioritize outputting videos related to the travel destination. Furthermore, if the user is at home, the output unit can prioritize outputting videos related to the area around the user's home. In this way, by outputting highly relevant videos by taking into account the user's geographical location information, more appropriate videos can be provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant videos.

[0082] At the time of output, the output unit can analyze the user's social media activity and output related videos. For example, the output unit outputs videos related to content shared by the user on social media. The output unit can also analyze the content posted by the user on social media and output related videos. The output unit can also output related videos by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and outputting related videos, more appropriate videos can be provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media activity data to a generation AI and cause the generation AI to select related videos.

[0083] The output unit can customize the output method by reflecting the user's past feedback at the time of output. The output unit can adjust the output method based on, for example, feedback provided by the user in the past. The output unit can also preferentially use a specific output method based on the user's past feedback. The output unit can also reflect the user's feedback in real time and optimize the output method. This enables more appropriate video output by customizing the output method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the output unit can be performed using, for example, AI, or can be performed without using AI. For example, the output unit can input the user's past feedback data into a generation AI and have the generation AI customize the output method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, input unit, editing unit, and output 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 is realized by the specific processing unit 290 of the data processing device 12 and collects past editing data of a video poster. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the editing style of the video poster based on the collected editing data. The input unit is realized, for example, by the control unit 46A of the smart device 14 and inputs raw video footage shot by the video poster. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adds captions and sound effects based on the input raw video footage. The output unit is realized, for example, by the control unit 46A of the smart device 14 and outputs the edited video. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, learning unit, input unit, editing unit, and output 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 is realized by the specific processing unit 290 of the data processing device 12 and collects past editing data of a video poster. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the editing style of the video poster based on the collected editing data. The input unit is realized, for example, by the control unit 46A of the smart glasses 214 and inputs raw video footage shot by the video poster. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adds captions and sound effects based on the input raw video footage. The output unit is realized, for example, by the control unit 46A of the smart glasses 214 and outputs the edited video. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, input unit, editing unit, and output 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 is realized by the specific processing unit 290 of the data processing device 12 and collects past editing data of a video poster. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the editing style of the video poster based on the collected editing data. The input unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and inputs raw video footage shot by the video poster. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adds captions and sound effects based on the input raw video footage. The output unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and outputs the edited video. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, input unit, editing unit, and output 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 is realized by the specific processing unit 290 of the data processing device 12 and collects past editing data of a video poster. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the editing style of the video poster based on the collected editing data. The input unit is realized, for example, by the control unit 46A of the robot 414 and inputs raw video footage shot by the video poster. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adds captions and sound effects based on the input raw video footage. The output unit is realized, for example, by the control unit 46A of the robot 414 and outputs the edited video.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] In addition to the user's editing style, the collection unit can also collect the user's viewing history and determine the priority of editing data based on the viewing history. For example, if the user frequently watches videos of a specific genre, the collection unit can prioritize collecting editing data related to that genre. Also, if the user watches many videos by a specific creator, the collection unit can prioritize collecting data similar to the creator's editing style. Furthermore, the collection unit can analyze the ratings and comments on videos watched by the user and prioritize collecting editing styles that have high ratings. This allows for more appropriate editing data to be collected based on the user's viewing history.

[0086] The collection unit can also estimate the user's emotions and determine the type of edited data to collect based on the estimated user's emotions. For example, if the user is sad, it can prioritize collecting moving edited data. Also, if the user is happy, it can prioritize collecting fun edited data. Furthermore, if the user is nervous, it can prioritize collecting relaxing edited data. In this way, by determining the type of edited data according to the user's emotions, it is possible to collect more appropriate edited data.

[0087] The collection unit can weight data based on the user's editing time period in addition to the user's editing frequency. For example, if the user often edits at night, a high weight can be assigned to edited data suitable for nighttime. Also, if the user often edits on weekends, a high weight can be assigned to edited data suitable for weekends. Furthermore, if the user edits frequently in a particular season, a high weight can be assigned to edited data suitable for that season. In this way, by weighting data based on the user's editing time period, more appropriate edited data can be collected.

[0088] The collection unit can adjust the collection range based on the user's editing purpose in addition to changes in the user's editing style. For example, if the user is creating a promotional video, it can prioritize collecting edited data suitable for promotion. Also, if the user is creating an educational video, it can prioritize collecting edited data suitable for education. Furthermore, if the user is creating an entertainment video, it can prioritize collecting edited data suitable for entertainment. In this way, by adjusting the collection range based on the user's editing purpose, more appropriate edited data can be collected.

[0089] In addition to using different collection methods for each project, the collection department can also adjust the collection method based on the progress of the user's project. For example, in the early stages of a project, a wide range of editing data is collected, and in the middle stages, editing data related to a specific theme is collected. In addition, in the final stages of a project, the collection method can be customized based on user feedback. Furthermore, the priority of collected data can be adjusted depending on the progress of the project. This allows more appropriate editing data to be collected by adjusting the collection method based on the progress of the project.

[0090] The learning unit can also estimate the user's emotions and receive feedback on the learning data based on the estimated user's emotions. For example, if the user is relaxed, feedback on data with a gentle editing style can be given priority. If the user is excited, feedback on data with a dynamic editing style can be given priority. Furthermore, if the user is tired, feedback on data with a simple and intuitive editing style can be given priority. In this way, by receiving feedback according to the user's emotions, a more appropriate editing style can be learned.

[0091] The learning unit can improve the learning algorithm by referring to the learning data of other users in addition to past learning data. For example, the learning unit can analyze the editing styles used by other users and select the optimal learning algorithm. It can also select an algorithm suitable for a specific editing style from the learning data of other users. It can also analyze the learning data of other users and adjust the parameters of the learning algorithm. In this way, the learning algorithm can be optimized by referring to the learning data of other users, thereby improving the accuracy of learning.

[0092] In addition to variations in the user's editing style, the learning unit can also adjust the update frequency of the learning data based on the user's editing environment. For example, if the user edits using different devices, the update frequency of the learning data can be adjusted depending on the device. Also, if the user edits using different software, the update frequency of the learning data can be adjusted depending on the software. Furthermore, if the user edits in different locations, the update frequency of the learning data can be adjusted depending on the location. In this way, by adjusting the update frequency of the learning data based on the user's editing environment, it is possible to respond to the latest editing styles.

[0093] In addition to user feedback, the learning unit can also update the learning data to reflect the user's social media activity. For example, data related to content shared by the user on social media can be prioritized for learning. The learning unit can also analyze the content posted by the user on social media and prioritize learning related data. Furthermore, the learning unit can also prioritize learning related data by referring to the activities of the user's friends on social media. In this way, updating the learning data to reflect the user's social media activity improves the accuracy of learning.

[0094] The input unit can also estimate the user's emotion and adjust the input method of the raw video based on the estimated user's emotion. For example, if the user is relaxed, a gentle input method can be provided. If the user is excited, a dynamic input method can be provided. Furthermore, if the user is tired, a simple and intuitive input method can be provided. This allows for more appropriate input of raw video by adjusting the input method according to the user's emotion.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The collection unit collects the video uploader's past editing data. This includes cutting videos, adding subtitles, and inserting sound effects. The collection unit also collects data on the editing tools and techniques used by the video uploader in the past. For example, it collects log data from editing software. Step 2: The learning unit learns the editing style of the video poster based on the editing data collected by the collection unit. The learning unit analyzes the collected editing data, identifies the editing style of the video poster, and models it using a machine learning algorithm. Step 3: The input unit inputs the raw video captured by the video uploader. The raw video includes the resolution, frame rate, file format, etc. The input unit inputs the raw video captured by the video uploader in digital format, and can also input it in real-time streaming format. Step 4: The editing department automatically adds captions and sound effects based on the raw footage input by the input department. Using generative AI, captions and sound effects are automatically added, and captions and sound effects that are frequently used by video uploaders in specific scenes are inserted. Step 5: The output unit outputs the video edited by the editing unit. The edited video can be output in a digital format and can also be output in a streaming format in real time.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 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 past editing data of video contributors; a learning unit that learns the editing style of a video poster based on the editing data collected by the collecting unit; an input unit for inputting raw video footage taken by a video poster; an editing unit that automatically adds captions and sound effects based on the raw video input by the input unit; an output unit that outputs the video edited by the editing unit; A system characterized by:

2. The collecting unit Estimate user emotions and determine the priority of edited data to be collected based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit When collecting historical edit data, the data is weighted based on the frequency of edits made by the user.

2. The system of claim 1.

4. The collecting unit When collecting editing data, adjust the collection scope based on changes in the user's editing style.

2. The system of claim 1.

5. The collecting unit When collecting edit data, apply different collection methods for each user's project.

2. The system of claim 1.

6. The learning unit Estimate the user's emotions and select training data based on the estimated user emotions.

2. The system of claim 1.

7. The learning unit During training, past training data is referenced to improve the learning algorithm.

2. The system of claim 1.

8. The learning unit During learning, the system analyzes changes in the user's editing style and adjusts the update frequency of the learning data.

2. The system of claim 1.

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