system

Abstract

The system according to the embodiment aims to efficiently edit moving images based on voice instructions. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an editing unit, and a confirmation unit. The reception unit receives voice instructions. The analysis unit analyzes the voice instructions received by the reception unit and identifies editing content. The editing unit implements the editing content identified by the analysis unit. The confirmation unit allows a user to review the edited video and confirm whether additional instructions should be issued.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, video editing is time-consuming and laborious, and there are problems with the lack of sufficient voice-instruction editing capabilities.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an editing unit, and a confirmation unit. The reception unit receives voice instructions. The analysis unit analyzes the voice instructions received by the reception unit and identifies editing content. The editing unit implements the editing content identified by the analysis unit. The confirmation unit allows the user to check the edited video and confirms whether additional instructions are to be issued. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently edit moving images based on voice instructions. [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 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 automatically edits video footage. In this video editing system, a user saves a video they have shot in an app and gives voice instructions on how they want the video to be edited. These voice instructions are then classified and summarized by a generation AI, and editing is performed within the app. Once the edits are complete, the user can review the video and provide voice instructions on any areas that require further correction. This allows users to easily create their own edited videos simply by giving voice instructions. For example, a user saves a video they have shot in an app. The video is saved in its original format. For example, a user uploads a video shot with a smartphone to an app. The video is saved in a format that allows editing within the app. Next, the user gives voice instructions on how they want the video to be edited. For example, the user may give instructions such as "cut this part," "add music," or "insert text." These voice instructions are then analyzed by a generation AI, which categorizes and summarizes the specific editing content. The generation AI analyzes the voice instructions and identifies the editing content. For example, in response to a command such as "cut this part," the AI ​​identifies which part to cut and summarizes the editing content. This allows editing to be performed according to the user's intentions. The user can then review the video once editing is complete. The user can play back the edited video and make further corrections as necessary. For example, they can give additional instructions such as "change the music in this part" or "adjust the position of the text." This system allows users to easily create their own edited videos simply by giving voice instructions. For example, this can be used in a variety of situations, such as creating a video summarizing travel memories or a video editing the highlights of an event. This allows the video editing system to automatically edit the video based on the user's voice instructions, allowing for review and additional instructions.

[0029] A video editing system according to an embodiment includes a receiving unit, an analysis unit, an editing unit, and a confirmation unit. The receiving unit receives voice instructions. Examples of voice instructions include, but are not limited to, "cut this part," "add music," and "insert text." The receiving unit converts the voice instructions into text data using, for example, voice recognition technology. The analysis unit analyzes the voice instructions received by the receiving unit and identifies editing content. The analysis unit analyzes the voice instructions using, for example, a generation AI and identifies editing content. The generation AI analyzes the voice instructions using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The editing unit implements the editing content identified by the analysis unit. The editing unit performs editing such as cutting, trimming, and adding effects to the video. The editing unit implements the editing content using, for example, the generation AI. The generation AI performs editing using, for example, a video editing AI or an effect generation AI. The confirmation unit allows a user to check the edited video and issue additional instructions. The confirmation unit, for example, previews the edited video and allows the user to issue additional instructions. The confirmation unit, for example, receives the user's additional instructions using a generation AI. This allows the video editing system according to the embodiment to automatically edit the video based on the user's voice instructions, and allows the user to confirm and issue additional instructions.

[0030] The video editing system includes a music adding unit that adds music. The music adding unit adds music to a video. The music adding unit adds, for example, music specified by a user to the video. The music adding unit selects music using, for example, a generation AI and adds it to the video. The generation AI selects music using, for example, a music generation AI or a music selection AI. The music adding unit selects music taking into consideration, for example, the music genre, length, license, etc. By adding music, the scope of video editing is expanded. Some or all of the above-described processing in the music adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the music adding unit can directly add a music file specified by a user to a video.

[0031] The video editing system includes a text insertion unit that inserts text. The text insertion unit inserts text into a video. The text insertion unit inserts, for example, text specified by a user into a video. The text insertion unit generates text using, for example, a generation AI and inserts it into a video. The generation AI generates text using, for example, a text generation AI or a text placement AI. The text insertion unit inserts text taking into consideration, for example, the font, size, position, etc. of the text. In this way, by inserting text, explanations or comments can be added to a video. Some or all of the above-described processing in the text insertion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text insertion unit can directly insert text specified by a user into a video.

[0032] The analysis unit can analyze the voice instructions and identify which parts to cut. The analysis unit, for example, analyzes the voice instructions and identifies which parts to cut. The analysis unit, for example, uses a generation AI to analyze the voice instructions and identify the parts to cut. The generation AI analyzes the voice instructions using, for example, a text generation AI or a voice analysis AI. The analysis unit, for example, converts the content of the voice instructions into text data and identifies the parts to cut. This makes it possible to edit in accordance with the user's intentions by cutting specific parts based on the voice instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can convert the voice instructions into text data and identify the parts to cut.

[0033] The confirmation unit can play the video edited by the user and issue additional instructions. The confirmation unit, for example, previews the edited video and allows the user to issue additional instructions. The confirmation unit, for example, uses a generation AI to accept the additional instructions from the user. The generation AI accepts the additional instructions using, for example, a voice recognition AI or a text generation AI. The confirmation unit, for example, analyzes the additional instructions specified by the user and identifies the editing content. This allows the user to confirm the editing results and issue further correction instructions. Some or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit can directly accept the additional instructions specified by the user and identify the editing content.

[0034] The reception unit can analyze the user's past voice instruction history and select the optimal reception method. The reception unit, for example, analyzes the user's past voice instruction history and selects the optimal reception method. The reception unit, for example, analyzes the past voice instruction history using a generation AI. The generation AI analyzes the voice instruction history using a voice recognition AI or a data analysis AI. The reception unit, for example, preferentially receives voice instructions that the user has frequently used in the past. The reception unit, for example, learns specific patterns from the user's past voice instruction history and suggests the optimal reception method. The reception unit, for example, analyzes time periods during which the user used voice instructions in the past and selects the optimal reception method for that time period. In this way, by analyzing the past voice instruction history, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can analyze the user's past voice instruction history and select the optimal reception method.

[0035] The reception unit may perform filtering based on the user's current project or field of interest when receiving a voice instruction. For example, the reception unit may perform filtering based on the user's current project or field of interest when receiving a voice instruction. The reception unit may analyze the user's project or field of interest using, for example, a generation AI. The generation AI may analyze the project or field of interest using, for example, a data analysis AI or a project management AI. The reception unit may preferentially receive, for example, voice instructions related to a project currently being carried out by the user. The reception unit may filter and receive related voice instructions based on, for example, the user's field of interest. The reception unit may filter and receive voice instructions based on, for example, fields in which the user has previously shown interest. By filtering voice instructions based on the user's current project or field of interest, highly relevant instructions can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may filter voice instructions based on the user's project or field of interest.

[0036] When receiving a voice instruction, the reception unit can prioritize receiving relevant instructions by taking into account the user's geographical location information. For example, when receiving a voice instruction, the reception unit prioritizes receiving relevant instructions by taking into account the user's geographical location information. The reception unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, when the user is in a specific location, the reception unit prioritizes receiving voice instructions related to the location. For example, when the user is traveling, the reception unit prioritizes receiving voice instructions related to the destination. For example, when the user is in a specific area, the reception unit prioritizes receiving voice instructions related to the area. In this way, by taking into account the user's geographical location information, highly relevant instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or may be performed without using an AI. For example, the reception unit can prioritize receiving voice instructions by taking into account the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity and receive related instructions when receiving a voice instruction. For example, the reception unit can analyze the user's social media activity and receive related instructions when receiving a voice instruction. The reception unit can analyze the user's social media activity using, for example, a generation AI. The generation AI can analyze the social media activity using, for example, a social media analysis AI or a data analysis AI. The reception unit can preferentially receive related voice instructions based on, for example, content shared by the user on social media. The reception unit can receive, for example, voice instructions related to topics of interest from the user's social media activity. The reception unit can receive related voice instructions based on, for example, accounts the user follows on social media. In this way, by analyzing the user's social media activity, related instructions can be preferentially received. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can analyze the user's social media activity and receive related instructions.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice instruction during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the voice instruction during analysis. The analysis unit, for example, uses a generation AI to evaluate the importance of the voice instruction. The generation AI evaluates the importance of the voice instruction using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, performs a detailed analysis of an important voice instruction. The analysis unit, for example, performs a concise analysis of a general voice instruction. The analysis unit, for example, performs a minimal analysis of a low-priority voice instruction. In this way, by adjusting the level of detail of the analysis based on the importance of the voice instruction, it is possible to perform a detailed analysis of an important instruction. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the voice instruction.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the voice instruction during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the voice instruction during analysis. The analysis unit, for example, classifies the category of the voice instruction using a generation AI. The generation AI classifies the category of the voice instruction using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, applies a specific cut algorithm to a cut instruction. The analysis unit, for example, applies a music selection algorithm to an instruction to add music. The analysis unit, for example, applies a text placement algorithm to an instruction to insert text. In this way, by applying different analysis algorithms depending on the category of the voice instruction, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply different analysis algorithms depending on the category of the voice instruction.

[0040] The analysis unit can determine the order of analysis based on the timing of submission of the voice instructions during analysis. The analysis unit, for example, determines the order of analysis based on the timing of submission of the voice instructions during analysis. The analysis unit, for example, uses a generation AI to evaluate the timing of submission of the voice instructions. The generation AI evaluates the timing of submission of the voice instructions using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, prioritizes analysis of the most recent voice instructions. The analysis unit, for example, postpones voice instructions submitted earlier. The analysis unit, for example, dynamically adjusts the priority of analysis based on the submission timing. In this way, by determining the priority of analysis based on the timing of submission of the voice instructions, it is possible to prioritize analysis of the most recent instructions. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can determine the order of analysis based on the timing of submission of the voice instructions.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the voice instructions during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the voice instructions during analysis. The analysis unit, for example, uses a generation AI to evaluate the relevance of the voice instructions. The generation AI evaluates the relevance of the voice instructions using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, prioritizes analysis of highly relevant voice instructions. The analysis unit, for example, postpones less relevant voice instructions. The analysis unit, for example, dynamically adjusts the order of analysis based on the relevance of the voice instructions. In this way, by adjusting the order of analysis based on the relevance of the voice instructions, highly relevant instructions can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the voice instructions.

[0042] The editing department can select the optimal editing method by analyzing the user's past editing history when editing. For example, the editing department can select the optimal editing method by analyzing the user's past editing history when editing. The editing department can analyze the past editing history using, for example, a generation AI. The generation AI can analyze the editing history using, for example, a data analysis AI or a machine learning AI. The editing department can, for example, preferentially apply the editing method used by the user in the past. For example, the editing department can learn specific patterns from the user's past editing history and suggest the optimal editing method. The editing department can select the optimal editing method by, for example, referring to the edits made by the user in the past. In this way, the optimal editing method can be provided to the user by analyzing the past editing history. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can analyze the user's past editing history to select the optimal editing method.

[0043] The editing department can customize the editing means based on the user's current living situation during editing. For example, the editing department customizes the editing means based on the user's current living situation during editing. The editing department, for example, analyzes the user's living situation using a generation AI. The generation AI analyzes the living situation using a data analysis AI, a machine learning AI, or the like. For example, if the user is busy, the editing department performs quick editing. For example, if the user is relaxing, the editing department performs detailed editing. For example, if the user is participating in a specific event, the editing department performs editing related to the event. This allows the editing means to be customized according to the user's living situation, thereby providing more appropriate editing results. Some or all of the above-mentioned processing in the editing department may be performed using AI, for example, or may be performed without using AI. For example, the editing department can customize the editing means based on the user's living situation.

[0044] The editing department can select an appropriate editing method by taking into account the user's geographical location information when editing. For example, the editing department selects an appropriate editing method by taking into account the user's geographical location information when editing. The editing department, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, if the user is in a specific location, the editing department performs editing related to that location. For example, if the user is traveling, the editing department performs editing related to the user's destination. For example, if the user is in a specific area, the editing department performs editing related to that area. In this way, highly relevant editing can be performed by taking into account the user's geographical location information. Some or all of the above-described processing in the editing department may be performed using an AI, for example, or may be performed without using an AI. For example, the editing department can select an appropriate editing method by taking into account the user's geographical location information.

[0045] The editorial department can analyze the user's social media activity and suggest editing methods during editing. For example, the editorial department can analyze the user's social media activity and suggest editing methods during editing. The editorial department can analyze the user's social media activity using, for example, a generation AI. The generation AI can analyze the social media activity using, for example, a social media analysis AI or a data analysis AI. The editorial department can suggest relevant edits based on, for example, content shared by the user on social media. The editorial department can suggest edits related to topics of interest from the user's social media activity. The editorial department can suggest relevant edits based on, for example, accounts the user follows on social media. In this way, relevant editing methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the editorial department can be performed using, for example, AI, or can be performed without using AI. For example, the editorial department can analyze the user's social media activity and suggest editing methods.

[0046] The confirmation unit can select an appropriate display method by referring to the user's past operation history during confirmation. For example, the confirmation unit can select an appropriate display method by referring to the user's past operation history during confirmation. The confirmation unit can analyze the past operation history using, for example, a generation AI. The generation AI can analyze the operation history using, for example, a data analysis AI or a machine learning AI. The confirmation unit can, for example, preferentially provide a display method that the user has used in the past. The confirmation unit can learn specific patterns from the user's past operation history and suggest an optimal display method. The confirmation unit can select an optimal display method by referring to, for example, the operations performed by the user in the past. In this way, by referring to the past operation history, it is possible to provide the user with an optimal display method. Some or all of the above-described processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can select an appropriate display method by referring to the user's past operation history.

[0047] The confirmation unit may perform filtering based on the user's current project or area of ​​interest at the time of confirmation. For example, the confirmation unit may perform filtering based on the user's current project or area of ​​interest at the time of confirmation. The confirmation unit may, for example, use a generation AI to analyze the user's project or area of ​​interest. The generation AI may, for example, use a data analysis AI or a project management AI to analyze the project or area of ​​interest. The confirmation unit may, for example, prioritize displaying confirmation content related to the user's current project. The confirmation unit may, for example, filter and display related confirmation content based on the user's area of ​​interest. The confirmation unit may, for example, filter and display confirmation content based on areas in which the user has previously shown interest. This allows for highly relevant confirmation by filtering the confirmation content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may filter the confirmation content based on the user's project or area of ​​interest.

[0048] The confirmation unit can prioritize relevant confirmations during confirmation, taking into account the user's geographical location information. For example, the confirmation unit prioritizes relevant confirmations during confirmation, taking into account the user's geographical location information. The confirmation unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, when the user is in a specific location, the confirmation unit prioritizes displaying confirmation content related to the location. For example, when the user is traveling, the confirmation unit prioritizes displaying confirmation content related to the destination. For example, when the user is in a specific region, the confirmation unit prioritizes displaying confirmation content related to the region. In this way, by taking into account the user's geographical location information, highly relevant confirmations can be prioritized. Some or all of the above-described processing in the confirmation unit may be performed using an AI, for example, or may be performed without using an AI. For example, the confirmation unit prioritizes relevant confirmations during confirmation, taking into account the user's geographical location information.

[0049] The confirmation unit may analyze the user's social media activity and perform related confirmations at the time of confirmation. For example, the confirmation unit may analyze the user's social media activity and perform related confirmations at the time of confirmation. The confirmation unit may analyze the user's social media activity using, for example, a generation AI. The generation AI may analyze the social media activity using, for example, a social media analysis AI or a data analysis AI. The confirmation unit may prioritize displaying related confirmation content based on, for example, content shared by the user on social media. The confirmation unit may prioritize displaying confirmation content related to topics of interest from the user's social media activity. The confirmation unit may prioritize displaying related confirmation content based on, for example, accounts the user follows on social media. In this way, by analyzing the user's social media activity, related confirmation content can be prioritized and displayed. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may analyze the user's social media activity and perform related confirmations.

[0050] When adding music, the music adding unit can analyze the user's past music selection history and select appropriate music. When adding music, for example, the music adding unit analyzes the user's past music selection history and selects appropriate music. The music adding unit analyzes the past music selection history, for example, using a generation AI. The generation AI analyzes the music selection history using, for example, a data analysis AI or a machine learning AI. The music adding unit, for example, preferentially suggests music previously selected by the user. For example, the music adding unit learns specific patterns from the user's past music selection history and suggests optimal music. For example, the music adding unit selects optimal music by referring to the genres of music previously selected by the user. In this way, optimal music can be provided to the user by analyzing the past music selection history. Some or all of the above-described processing in the music adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the music adding unit can analyze the user's past music selection history to select appropriate music.

[0051] The music adding unit can select optimal music by taking into consideration the user's geographical location information when adding music. For example, the music adding unit selects optimal music by taking into consideration the user's geographical location information when adding music. The music adding unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, if the user is in a specific location, the music adding unit selects music related to that location. For example, if the user is traveling, the music adding unit selects music related to the destination. For example, if the user is in a specific area, the music adding unit selects music related to that area. In this way, highly relevant music can be selected by taking into consideration the user's geographical location information. Some or all of the above-described processing in the music adding unit may be performed using AI, for example, or may be performed without using AI. For example, the music adding unit can select optimal music by taking into consideration the user's geographical location information.

[0052] The text insertion unit can select optimal text by analyzing the user's past text insertion history when inserting text. For example, the text insertion unit can select optimal text by analyzing the user's past text insertion history when inserting text. The text insertion unit can analyze the past text insertion history using, for example, a generation AI. The generation AI can analyze the text insertion history using, for example, a data analysis AI or a machine learning AI. The text insertion unit can, for example, preferentially suggest text that the user has previously inserted. The text insertion unit can learn specific patterns from the user's past text insertion history and suggest optimal text. The text insertion unit can select optimal text by, for example, referring to the style of text that the user has previously inserted. In this way, optimal text can be provided to the user by analyzing the past text insertion history. Some or all of the above-described processing in the text insertion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the text insertion unit can select optimal text by analyzing the user's past text insertion history.

[0053] The text insertion unit can select optimal text by taking into account the user's geographical location information when inserting text. For example, the text insertion unit selects optimal text by taking into account the user's geographical location information when inserting text. The text insertion unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, if the user is in a specific location, the text insertion unit selects text related to the location. For example, if the user is traveling, the text insertion unit selects text related to the destination. For example, if the user is in a specific area, the text insertion unit selects text related to the area. In this way, highly relevant text can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the text insertion unit may be performed using AI, for example, or may be performed without using AI. For example, the text insertion unit can select optimal text by taking into account the user's geographical location information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] Video editing systems not only automatically edit videos based on the user's voice instructions, but can also analyze the user's past editing history to suggest optimal editing methods. For example, by prioritizing suggested editing techniques that the user has frequently used in the past, editing that suits the user's preferences can be achieved. Furthermore, the system can automatically apply similar edits to similar scenes based on the user's past edits. Furthermore, the system can learn the music and text styles used by the user in the past and automatically suggest them the next time the user edits. This allows users to edit videos efficiently while maintaining a consistent editing style.

[0056] The video editing system can filter voice instructions based on the user's current project or area of ​​interest. For example, if a user is editing a travel video, voice instructions related to the travel can be prioritized. Also, if a user is interested in a particular event, voice instructions related to the event can be prioritized. Furthermore, related voice instructions can be filtered and accepted based on the area in which the user has previously shown interest. This allows the user to efficiently edit according to their interests.

[0057] The video editing system can preferentially accept voice instructions by taking into account the user's geographical location information. For example, if the user is in a specific location, voice instructions related to that location can be preferentially accepted. Also, if the user is traveling, voice instructions related to the user's destination can be preferentially accepted. Furthermore, if the user is in a specific region, voice instructions related to that region can be preferentially accepted. In this way, by taking into account the user's geographical location information, highly relevant voice instructions can be preferentially accepted.

[0058] The video editing system can analyze a user's social media activity to prioritize receiving voice instructions. For example, it can prioritize receiving relevant voice instructions based on the content the user has shared on social media. It can also prioritize receiving voice instructions related to topics of interest from the user's social media activity. It can also prioritize receiving relevant voice instructions based on the accounts the user follows on social media. In this way, it is possible to prioritize receiving relevant voice instructions by analyzing a user's social media activity.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives voice instructions. Voice instructions include "cut this part," "add music," "insert text," etc. The reception unit converts the voice instructions into text data using voice recognition technology. Step 2: The analysis unit analyzes the voice instructions received by the reception unit and identifies the edit content. The analysis unit analyzes the voice instructions using a generation AI and identifies the edit content. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The editing department implements the editing content identified by the analysis department. The editing department performs editing such as cutting, trimming, and adding effects to the video. The editing department uses generation AI to implement the editing content. The generation AI uses video editing AI, effect generation AI, etc. Step 4: The confirmation unit is a part where the user can confirm the edited video and give additional instructions. The confirmation unit previews the edited video and the user can give additional instructions. The confirmation unit uses the generation AI to accept the user's additional instructions.

[0061] (Example 2) A video editing system according to an embodiment of the present invention automatically edits video footage. In this video editing system, a user saves a video they have shot in an app and gives voice instructions on how they want the video to be edited. These voice instructions are then classified and summarized by a generation AI, and editing is performed within the app. Once the edits are complete, the user can review the video and provide voice instructions on any areas that require further correction. This allows users to easily create their own edited videos simply by giving voice instructions. For example, a user saves a video they have shot in an app. The video is saved in its original format. For example, a user uploads a video shot with a smartphone to an app. The video is saved in a format that allows editing within the app. Next, the user gives voice instructions on how they want the video to be edited. For example, the user may give instructions such as "cut this part," "add music," or "insert text." These voice instructions are then analyzed by a generation AI, which categorizes and summarizes the specific editing content. The generation AI analyzes the voice instructions and identifies the editing content. For example, in response to a command such as "cut this part," the AI ​​identifies which part to cut and summarizes the editing content. This allows editing to be performed according to the user's intentions. The user can then review the video once editing is complete. The user can play back the edited video and make further corrections as necessary. For example, they can give additional instructions such as "change the music in this part" or "adjust the position of the text." This system allows users to easily create their own edited videos simply by giving voice instructions. For example, this can be used in a variety of situations, such as creating a video summarizing travel memories or a video editing the highlights of an event. This allows the video editing system to automatically edit the video based on the user's voice instructions, allowing for review and additional instructions.

[0062] A video editing system according to an embodiment includes a receiving unit, an analysis unit, an editing unit, and a confirmation unit. The receiving unit receives voice instructions. Examples of voice instructions include, but are not limited to, "cut this part," "add music," and "insert text." The receiving unit converts the voice instructions into text data using, for example, voice recognition technology. The analysis unit analyzes the voice instructions received by the receiving unit and identifies editing content. The analysis unit analyzes the voice instructions using, for example, a generation AI and identifies editing content. The generation AI analyzes the voice instructions using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The editing unit implements the editing content identified by the analysis unit. The editing unit performs editing such as cutting, trimming, and adding effects to the video. The editing unit implements the editing content using, for example, the generation AI. The generation AI performs editing using, for example, a video editing AI or an effect generation AI. The confirmation unit allows a user to check the edited video and issue additional instructions. The confirmation unit, for example, previews the edited video and allows the user to issue additional instructions. The confirmation unit, for example, receives the user's additional instructions using a generation AI. This allows the video editing system according to the embodiment to automatically edit the video based on the user's voice instructions, and allows the user to confirm and issue additional instructions.

[0063] The video editing system includes a music adding unit that adds music. The music adding unit adds music to a video. The music adding unit adds, for example, music specified by a user to the video. The music adding unit selects music using, for example, a generation AI and adds it to the video. The generation AI selects music using, for example, a music generation AI or a music selection AI. The music adding unit selects music taking into consideration, for example, the music genre, length, license, etc. By adding music, the scope of video editing is expanded. Some or all of the above-described processing in the music adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the music adding unit can directly add a music file specified by a user to a video.

[0064] The video editing system includes a text insertion unit that inserts text. The text insertion unit inserts text into a video. The text insertion unit inserts, for example, text specified by a user into a video. The text insertion unit generates text using, for example, a generation AI and inserts it into a video. The generation AI generates text using, for example, a text generation AI or a text placement AI. The text insertion unit inserts text taking into consideration, for example, the font, size, position, etc. of the text. In this way, by inserting text, explanations or comments can be added to a video. Some or all of the above-described processing in the text insertion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text insertion unit can directly insert text specified by a user into a video.

[0065] The analysis unit can analyze the voice instructions and identify which parts to cut. The analysis unit, for example, analyzes the voice instructions and identifies which parts to cut. The analysis unit, for example, uses a generation AI to analyze the voice instructions and identify the parts to cut. The generation AI analyzes the voice instructions using, for example, a text generation AI or a voice analysis AI. The analysis unit, for example, converts the content of the voice instructions into text data and identifies the parts to cut. This makes it possible to edit in accordance with the user's intentions by cutting specific parts based on the voice instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can convert the voice instructions into text data and identify the parts to cut.

[0066] The confirmation unit can play the video edited by the user and issue additional instructions. The confirmation unit, for example, previews the edited video and allows the user to issue additional instructions. The confirmation unit, for example, uses a generation AI to accept the additional instructions from the user. The generation AI accepts the additional instructions using, for example, a voice recognition AI or a text generation AI. The confirmation unit, for example, analyzes the additional instructions specified by the user and identifies the editing content. This allows the user to confirm the editing results and issue further correction instructions. Some or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit can directly accept the additional instructions specified by the user and identify the editing content.

[0067] The video editing system includes a reception unit that estimates a user's emotion and adjusts the timing of receiving voice instructions based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of receiving voice instructions based on the estimated user emotion. The reception unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using an emotion estimation AI or a voice analysis AI. For example, when the user is stressed, the reception unit quickly receives voice instructions and starts editing immediately. For example, when the user is relaxed, the reception unit slowly receives voice instructions and accepts detailed instructions. For example, when the user is in a hurry, the reception unit prioritizes receiving voice instructions and postpones other processing. This allows instructions to be received at a more appropriate timing by adjusting the timing of receiving voice instructions according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may estimate the user's emotion and adjust the timing of receiving a voice instruction based on the estimated emotion.

[0068] The reception unit can analyze the user's past voice instruction history and select the optimal reception method. The reception unit, for example, analyzes the user's past voice instruction history and selects the optimal reception method. The reception unit, for example, analyzes the past voice instruction history using a generation AI. The generation AI analyzes the voice instruction history using a voice recognition AI or a data analysis AI. The reception unit, for example, preferentially receives voice instructions that the user has frequently used in the past. The reception unit, for example, learns specific patterns from the user's past voice instruction history and suggests the optimal reception method. The reception unit, for example, analyzes time periods during which the user used voice instructions in the past and selects the optimal reception method for that time period. In this way, by analyzing the past voice instruction history, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can analyze the user's past voice instruction history and select the optimal reception method.

[0069] The reception unit may perform filtering based on the user's current project or field of interest when receiving a voice instruction. For example, the reception unit may perform filtering based on the user's current project or field of interest when receiving a voice instruction. The reception unit may analyze the user's project or field of interest using, for example, a generation AI. The generation AI may analyze the project or field of interest using, for example, a data analysis AI or a project management AI. The reception unit may preferentially receive, for example, voice instructions related to a project currently being carried out by the user. The reception unit may filter and receive related voice instructions based on, for example, the user's field of interest. The reception unit may filter and receive voice instructions based on, for example, fields in which the user has previously shown interest. By filtering voice instructions based on the user's current project or field of interest, highly relevant instructions can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may filter voice instructions based on the user's project or field of interest.

[0070] The video editing system includes a reception unit that estimates a user's emotion and determines the priority of voice instructions to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of voice instructions to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using an emotion estimation AI or a voice analysis AI. For example, if the user is nervous, the reception unit prioritizes receiving important voice instructions. For example, if the user is relaxed, the reception unit prioritizes receiving detailed voice instructions. For example, if the user is in a hurry, the reception unit prioritizes receiving voice instructions that can be processed quickly. In this way, by determining the priority of voice instructions according to the user's emotion, important instructions can be received preferentially. The emotion estimation is realized using an emotion estimation function using, 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may estimate the user's emotion and determine the priority of voice instructions based on the estimated emotion.

[0071] When receiving a voice instruction, the reception unit can prioritize receiving relevant instructions by taking into account the user's geographical location information. For example, when receiving a voice instruction, the reception unit prioritizes receiving relevant instructions by taking into account the user's geographical location information. The reception unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, when the user is in a specific location, the reception unit prioritizes receiving voice instructions related to the location. For example, when the user is traveling, the reception unit prioritizes receiving voice instructions related to the destination. For example, when the user is in a specific area, the reception unit prioritizes receiving voice instructions related to the area. In this way, by taking into account the user's geographical location information, highly relevant instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or may be performed without using an AI. For example, the reception unit can prioritize receiving voice instructions by taking into account the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity and receive related instructions when receiving a voice instruction. For example, the reception unit can analyze the user's social media activity and receive related instructions when receiving a voice instruction. The reception unit can analyze the user's social media activity using, for example, a generation AI. The generation AI can analyze the social media activity using, for example, a social media analysis AI or a data analysis AI. The reception unit can preferentially receive related voice instructions based on, for example, content shared by the user on social media. The reception unit can receive, for example, voice instructions related to topics of interest from the user's social media activity. The reception unit can receive related voice instructions based on, for example, accounts the user follows on social media. In this way, by analyzing the user's social media activity, related instructions can be preferentially received. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can analyze the user's social media activity and receive related instructions.

[0073] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using, for example, an emotion estimation AI or a data analysis AI. The analysis unit provides detailed analysis results, for example, when the user is relaxed. The analysis unit provides concise analysis results, for example, when the user is in a hurry. The analysis unit provides visually stimulating analysis results, for example, when the user is excited. This allows for adjusting the presentation method of the analysis according to the user's emotion to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice instruction during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the voice instruction during analysis. The analysis unit, for example, uses a generation AI to evaluate the importance of the voice instruction. The generation AI evaluates the importance of the voice instruction using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, performs a detailed analysis of an important voice instruction. The analysis unit, for example, performs a concise analysis of a general voice instruction. The analysis unit, for example, performs a minimal analysis of a low-priority voice instruction. In this way, by adjusting the level of detail of the analysis based on the importance of the voice instruction, it is possible to perform a detailed analysis of an important instruction. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the voice instruction.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the voice instruction during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the voice instruction during analysis. The analysis unit, for example, classifies the category of the voice instruction using a generation AI. The generation AI classifies the category of the voice instruction using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, applies a specific cut algorithm to a cut instruction. The analysis unit, for example, applies a music selection algorithm to an instruction to add music. The analysis unit, for example, applies a text placement algorithm to an instruction to insert text. In this way, by applying different analysis algorithms depending on the category of the voice instruction, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply different analysis algorithms depending on the category of the voice instruction.

[0076] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using an emotion estimation AI or a data analysis AI. The analysis unit provides a short analysis result, for example, when the user is in a hurry. The analysis unit provides a detailed analysis result, for example, when the user is relaxed. The analysis unit provides a visually stimulating analysis result, for example, when the user is excited. This allows for adjusting the length of the analysis according to the user's emotion to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated emotion.

[0077] The analysis unit can determine the order of analysis based on the timing of submission of the voice instructions during analysis. The analysis unit, for example, determines the order of analysis based on the timing of submission of the voice instructions during analysis. The analysis unit, for example, uses a generation AI to evaluate the timing of submission of the voice instructions. The generation AI evaluates the timing of submission of the voice instructions using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, prioritizes analysis of the most recent voice instructions. The analysis unit, for example, postpones voice instructions submitted earlier. The analysis unit, for example, dynamically adjusts the priority of analysis based on the submission timing. In this way, by determining the priority of analysis based on the timing of submission of the voice instructions, it is possible to prioritize analysis of the most recent instructions. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can determine the order of analysis based on the timing of submission of the voice instructions.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the voice instructions during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the voice instructions during analysis. The analysis unit, for example, uses a generation AI to evaluate the relevance of the voice instructions. The generation AI evaluates the relevance of the voice instructions using, for example, a voice analysis AI or a data analysis AI. The analysis unit, for example, prioritizes analysis of highly relevant voice instructions. The analysis unit, for example, postpones less relevant voice instructions. The analysis unit, for example, dynamically adjusts the order of analysis based on the relevance of the voice instructions. In this way, by adjusting the order of analysis based on the relevance of the voice instructions, highly relevant instructions can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the voice instructions.

[0079] The video editing system includes an editing unit that estimates a user's emotions and adjusts the editing method based on the estimated user emotions. The editing unit, for example, estimates the user's emotions and adjusts the editing method based on the estimated user emotions. The editing unit, for example, estimates the user's emotions using a generation AI. The generation AI estimates the user's emotions using an emotion estimation AI or a data analysis AI. For example, the editing unit performs detailed editing when the user is relaxed. For example, the editing unit performs concise editing when the user is in a hurry. For example, the editing unit performs visually stimulating editing when the user is excited. This allows the editing method to be adjusted according to the user's emotions, thereby providing a more appropriate editing result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or without AI. For example, the editing department can estimate the user's emotions and adjust the editing method based on the estimated emotions.

[0080] The editing department can select the optimal editing method by analyzing the user's past editing history when editing. For example, the editing department can select the optimal editing method by analyzing the user's past editing history when editing. The editing department can analyze the past editing history using, for example, a generation AI. The generation AI can analyze the editing history using, for example, a data analysis AI or a machine learning AI. The editing department can, for example, preferentially apply the editing method used by the user in the past. For example, the editing department can learn specific patterns from the user's past editing history and suggest the optimal editing method. The editing department can select the optimal editing method by, for example, referring to the edits made by the user in the past. In this way, the optimal editing method can be provided to the user by analyzing the past editing history. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI. For example, the editing department can analyze the user's past editing history to select the optimal editing method.

[0081] The editing department can customize the editing means based on the user's current living situation during editing. For example, the editing department customizes the editing means based on the user's current living situation during editing. The editing department, for example, analyzes the user's living situation using a generation AI. The generation AI analyzes the living situation using a data analysis AI, a machine learning AI, or the like. For example, if the user is busy, the editing department performs quick editing. For example, if the user is relaxing, the editing department performs detailed editing. For example, if the user is participating in a specific event, the editing department performs editing related to the event. This allows the editing means to be customized according to the user's living situation, thereby providing more appropriate editing results. Some or all of the above-mentioned processing in the editing department may be performed using AI, for example, or may be performed without using AI. For example, the editing department can customize the editing means based on the user's living situation.

[0082] The video editing system includes an editing unit that estimates a user's emotions and determines editing priorities based on the estimated user emotions. The editing unit, for example, estimates the user's emotions and determines editing priorities based on the estimated user emotions. The editing unit, for example, estimates the user's emotions using a generation AI. The generation AI estimates the user's emotions using an emotion estimation AI or a data analysis AI. For example, if the user is nervous, the editing unit prioritizes important editing. For example, if the user is relaxed, the editing unit prioritizes detailed editing. For example, if the user is in a hurry, the editing unit prioritizes editing that can be completed quickly. In this way, by determining editing priorities according to the user's emotions, important editing can be prioritized. Emotion estimation is realized using an emotion estimation function using, 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 editing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the editing department can estimate the user's emotions and determine the priority of editing based on the estimated emotions.

[0083] The editing department can select an appropriate editing method by taking into account the user's geographical location information when editing. For example, the editing department selects an appropriate editing method by taking into account the user's geographical location information when editing. The editing department, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, if the user is in a specific location, the editing department performs editing related to that location. For example, if the user is traveling, the editing department performs editing related to the user's destination. For example, if the user is in a specific area, the editing department performs editing related to that area. In this way, highly relevant editing can be performed by taking into account the user's geographical location information. Some or all of the above-described processing in the editing department may be performed using an AI, for example, or may be performed without using an AI. For example, the editing department can select an appropriate editing method by taking into account the user's geographical location information.

[0084] The editorial department can analyze the user's social media activity and suggest editing methods during editing. For example, the editorial department can analyze the user's social media activity and suggest editing methods during editing. The editorial department can analyze the user's social media activity using, for example, a generation AI. The generation AI can analyze the social media activity using, for example, a social media analysis AI or a data analysis AI. The editorial department can suggest relevant edits based on, for example, content shared by the user on social media. The editorial department can suggest edits related to topics of interest from the user's social media activity. The editorial department can suggest relevant edits based on, for example, accounts the user follows on social media. In this way, relevant editing methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the editorial department can be performed using, for example, AI, or can be performed without using AI. For example, the editorial department can analyze the user's social media activity and suggest editing methods.

[0085] The video editing system includes a confirmation unit that estimates a user's emotion and adjusts a confirmation display method based on the estimated user's emotion. The confirmation unit, for example, estimates the user's emotion and adjusts the confirmation display method based on the estimated user's emotion. The confirmation unit, for example, estimates the user's emotion using a generation AI. The generation AI, for example, estimates the user's emotion using an emotion estimation AI or a data analysis AI. For example, if the user is nervous, the confirmation unit provides a simple, highly visible display method. For example, if the user is relaxed, the confirmation unit provides a display method including detailed information. For example, if the user is in a hurry, the confirmation unit provides a display method that focuses on the main points. This allows the confirmation display method to be adjusted according to the user's emotion, thereby providing a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or without AI. For example, the confirmation unit can estimate the user's emotion and adjust the way the confirmation is displayed based on the estimated emotion.

[0086] The confirmation unit can select an appropriate display method by referring to the user's past operation history during confirmation. For example, the confirmation unit can select an appropriate display method by referring to the user's past operation history during confirmation. The confirmation unit can analyze the past operation history using, for example, a generation AI. The generation AI can analyze the operation history using, for example, a data analysis AI or a machine learning AI. The confirmation unit can, for example, preferentially provide a display method that the user has used in the past. The confirmation unit can learn specific patterns from the user's past operation history and suggest an optimal display method. The confirmation unit can select an optimal display method by referring to, for example, the operations performed by the user in the past. In this way, by referring to the past operation history, it is possible to provide the user with an optimal display method. Some or all of the above-described processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can select an appropriate display method by referring to the user's past operation history.

[0087] The confirmation unit may perform filtering based on the user's current project or area of ​​interest at the time of confirmation. For example, the confirmation unit may perform filtering based on the user's current project or area of ​​interest at the time of confirmation. The confirmation unit may, for example, use a generation AI to analyze the user's project or area of ​​interest. The generation AI may, for example, use a data analysis AI or a project management AI to analyze the project or area of ​​interest. The confirmation unit may, for example, prioritize displaying confirmation content related to the user's current project. The confirmation unit may, for example, filter and display related confirmation content based on the user's area of ​​interest. The confirmation unit may, for example, filter and display confirmation content based on areas in which the user has previously shown interest. This allows for highly relevant confirmation by filtering the confirmation content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may filter the confirmation content based on the user's project or area of ​​interest.

[0088] The video editing system includes a confirmation unit that estimates a user's emotions and determines confirmation priorities based on the estimated user emotions. The confirmation unit, for example, estimates the user's emotions and determines confirmation priorities based on the estimated user emotions. The confirmation unit, for example, estimates the user's emotions using a generation AI. The generation AI, for example, estimates the user's emotions using an emotion estimation AI or a data analysis AI. For example, if the user is nervous, the confirmation unit prioritizes displaying important confirmation items. For example, if the user is relaxed, the confirmation unit prioritizes displaying detailed confirmation items. For example, if the user is in a hurry, the confirmation unit prioritizes displaying confirmation items that can be processed quickly. In this way, by determining confirmation priorities according to the user's emotions, important confirmation items can be prioritized and displayed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the confirmation unit can estimate the user's emotions and determine the priority of confirmation based on the estimated emotions.

[0089] The confirmation unit can prioritize relevant confirmations during confirmation, taking into account the user's geographical location information. For example, the confirmation unit prioritizes relevant confirmations during confirmation, taking into account the user's geographical location information. The confirmation unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, when the user is in a specific location, the confirmation unit prioritizes displaying confirmation content related to the location. For example, when the user is traveling, the confirmation unit prioritizes displaying confirmation content related to the destination. For example, when the user is in a specific region, the confirmation unit prioritizes displaying confirmation content related to the region. In this way, by taking into account the user's geographical location information, highly relevant confirmations can be prioritized. Some or all of the above-described processing in the confirmation unit may be performed using an AI, for example, or may be performed without using an AI. For example, the confirmation unit prioritizes relevant confirmations during confirmation, taking into account the user's geographical location information.

[0090] The confirmation unit may analyze the user's social media activity and perform related confirmations at the time of confirmation. For example, the confirmation unit may analyze the user's social media activity and perform related confirmations at the time of confirmation. The confirmation unit may analyze the user's social media activity using, for example, a generation AI. The generation AI may analyze the social media activity using, for example, a social media analysis AI or a data analysis AI. The confirmation unit may prioritize displaying related confirmation content based on, for example, content shared by the user on social media. The confirmation unit may prioritize displaying confirmation content related to topics of interest from the user's social media activity. The confirmation unit may prioritize displaying related confirmation content based on, for example, accounts the user follows on social media. In this way, by analyzing the user's social media activity, related confirmation content can be prioritized and displayed. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may analyze the user's social media activity and perform related confirmations.

[0091] The video editing system includes a music adding unit that estimates a user's emotion and selects music to add based on the estimated user's emotion. The music adding unit, for example, estimates the user's emotion and selects music to add based on the estimated user's emotion. The music adding unit, for example, estimates the user's emotion using a generation AI. The generation AI, for example, estimates the user's emotion using an emotion estimation AI or a music selection AI. The music adding unit, for example, selects calming music when the user is relaxed. The music adding unit, for example, selects up-tempo music when the user is excited. The music adding unit, for example, selects soothing music when the user is sad. This allows for selecting optimal music according to the user's emotion, thereby enabling more appropriate music to be added. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the music adding unit may be performed using, for example, AI, or without AI. For example, the music adding unit can estimate the user's emotion and select music to add based on the estimated emotion.

[0092] When adding music, the music adding unit can analyze the user's past music selection history and select appropriate music. When adding music, for example, the music adding unit analyzes the user's past music selection history and selects appropriate music. The music adding unit analyzes the past music selection history, for example, using a generation AI. The generation AI analyzes the music selection history using, for example, a data analysis AI or a machine learning AI. The music adding unit, for example, preferentially suggests music previously selected by the user. For example, the music adding unit learns specific patterns from the user's past music selection history and suggests optimal music. For example, the music adding unit selects optimal music by referring to the genres of music previously selected by the user. In this way, optimal music can be provided to the user by analyzing the past music selection history. Some or all of the above-described processing in the music adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the music adding unit can analyze the user's past music selection history to select appropriate music.

[0093] The video editing system includes a music adding unit that estimates a user's emotion and adjusts the timing of adding music based on the estimated user's emotion. The music adding unit, for example, estimates the user's emotion and adjusts the timing of adding music based on the estimated user's emotion. The music adding unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using, for example, an emotion estimation AI or a music selection AI. For example, if the user is relaxed, the music adding unit adjusts the timing of adding music slowly. For example, if the user is in a hurry, the music adding unit adjusts the timing of adding music quickly. For example, if the user is excited, the music adding unit adjusts the timing of adding music to be visually stimulating. This allows music to be added at a more appropriate timing by adjusting the timing of adding music according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 music adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the music adding unit may estimate the user's emotion and adjust the timing of adding music based on the estimated emotion.

[0094] The music adding unit can select optimal music by taking into consideration the user's geographical location information when adding music. For example, the music adding unit selects optimal music by taking into consideration the user's geographical location information when adding music. The music adding unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, if the user is in a specific location, the music adding unit selects music related to that location. For example, if the user is traveling, the music adding unit selects music related to the destination. For example, if the user is in a specific area, the music adding unit selects music related to that area. In this way, highly relevant music can be selected by taking into consideration the user's geographical location information. Some or all of the above-described processing in the music adding unit may be performed using AI, for example, or may be performed without using AI. For example, the music adding unit can select optimal music by taking into consideration the user's geographical location information.

[0095] The video editing system includes a text insertion unit that estimates a user's emotion and selects text to be inserted based on the estimated user's emotion. The text insertion unit, for example, estimates the user's emotion and selects text to be inserted based on the estimated user's emotion. The text insertion unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using, for example, an emotion estimation AI or a text generation AI. For example, the text insertion unit selects calming text when the user is relaxed. For example, the text insertion unit selects visually stimulating text when the user is excited. For example, the text insertion unit selects soothing text when the user is sad. This allows for more appropriate text to be inserted by selecting optimal text according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 text insertion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text insertion unit may estimate the user's emotion and select text to insert based on the estimated emotion.

[0096] The text insertion unit can select optimal text by analyzing the user's past text insertion history when inserting text. For example, the text insertion unit can select optimal text by analyzing the user's past text insertion history when inserting text. The text insertion unit can analyze the past text insertion history using, for example, a generation AI. The generation AI can analyze the text insertion history using, for example, a data analysis AI or a machine learning AI. The text insertion unit can, for example, preferentially suggest text that the user has previously inserted. The text insertion unit can learn specific patterns from the user's past text insertion history and suggest optimal text. The text insertion unit can select optimal text by, for example, referring to the style of text that the user has previously inserted. In this way, optimal text can be provided to the user by analyzing the past text insertion history. Some or all of the above-described processing in the text insertion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the text insertion unit can select optimal text by analyzing the user's past text insertion history.

[0097] The video editing system includes a text insertion unit that estimates a user's emotion and adjusts the timing of text insertion based on the estimated user's emotion. The text insertion unit, for example, estimates the user's emotion and adjusts the timing of text insertion based on the estimated user's emotion. The text insertion unit, for example, estimates the user's emotion using a generation AI. The generation AI estimates the user's emotion using an emotion estimation AI or a text generation AI. For example, if the user is relaxed, the text insertion unit adjusts the timing of text insertion slowly. For example, if the user is in a hurry, the text insertion unit adjusts the timing of text insertion quickly. For example, if the user is excited, the text insertion unit adjusts the timing of text insertion to be visually stimulating. This allows the text to be inserted at a more appropriate timing by adjusting the timing of text insertion according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 text insertion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text insertion unit may estimate the user's emotion and adjust the timing of text insertion based on the estimated emotion.

[0098] The text insertion unit can select optimal text by taking into account the user's geographical location information when inserting text. For example, the text insertion unit selects optimal text by taking into account the user's geographical location information when inserting text. The text insertion unit, for example, analyzes the user's geographical location information using a generation AI. The generation AI analyzes the geographical location information using a location information analysis AI or a data analysis AI. For example, if the user is in a specific location, the text insertion unit selects text related to the location. For example, if the user is traveling, the text insertion unit selects text related to the destination. For example, if the user is in a specific area, the text insertion unit selects text related to the area. In this way, highly relevant text can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the text insertion unit may be performed using AI, for example, or may be performed without using AI. For example, the text insertion unit can select optimal text by taking into account the user's geographical location information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, editing unit, confirmation unit, music addition unit, and text insertion unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice instructions using the microphone 38B of the smart device 14 and converts the voice instructions into text data using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions using a generation AI to identify editing content. The editing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and performs editing such as cutting video and adding effects. The confirmation unit previews the edited video using the display 40A of the smart device 14, allowing the user to issue additional instructions. The music addition unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and selects music using a generation AI and adds it to the video. The text insertion unit is implemented, for example, by the control unit 46A of the smart device 14 and inserts user-specified text into the video. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, editing unit, confirmation unit, music addition unit, and text insertion 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 reception unit receives voice instructions using the microphone 238 of the smart glasses 214 and converts the voice instructions into text data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions using a generation AI to identify editing content. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs editing such as cutting the video and adding effects. The confirmation unit, for example, previews the edited video using the display of the smart glasses 214, allowing the user to issue additional instructions. The music addition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects music using a generation AI and adds it to the video. The text insertion unit is realized, for example, by the control unit 46A of the smart glasses 214 and inserts user-specified text into the video. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, editing unit, confirmation unit, music addition unit, and text insertion 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 reception unit receives voice instructions using the microphone 238 of the headset-type terminal 314 and converts the voice instructions into text data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions using a generation AI to identify editing content. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs editing such as cutting video and adding effects. The confirmation unit, for example, previews the edited video using the display 343 of the headset-type terminal 314, allowing the user to issue additional instructions. The music addition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects music using a generation AI and adds it to the video. The text insertion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and inserts user-specified text into the video. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, editing unit, confirmation unit, music addition unit, and text insertion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice instructions using the microphone 238 of the robot 414 and converts the voice instructions into text data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions using a generation AI to identify editing content. The editing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs editing such as cutting video and adding effects. The confirmation unit, for example, previews the edited video using the display of the robot 414, allowing the user to issue additional instructions. The music addition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects music using a generation AI and adds it to the video. The text insertion unit is realized, for example, by the control unit 46A of the robot 414 and inserts user-specified text into the video.

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

[0100] Video editing systems not only automatically edit videos based on the user's voice instructions, but can also analyze the user's past editing history to suggest optimal editing methods. For example, by prioritizing suggested editing techniques that the user has frequently used in the past, editing that suits the user's preferences can be achieved. Furthermore, the system can automatically apply similar edits to similar scenes based on the user's past edits. Furthermore, the system can learn the music and text styles used by the user in the past and automatically suggest them the next time the user edits. This allows users to edit videos efficiently while maintaining a consistent editing style.

[0101] The video editing system can filter voice instructions based on the user's current project or area of ​​interest. For example, if a user is editing a travel video, voice instructions related to the travel can be prioritized. Also, if a user is interested in a particular event, voice instructions related to the event can be prioritized. Furthermore, related voice instructions can be filtered and accepted based on the area in which the user has previously shown interest. This allows the user to efficiently edit according to their interests.

[0102] The video editing system can preferentially accept voice instructions by taking into account the user's geographical location information. For example, if the user is in a specific location, voice instructions related to that location can be preferentially accepted. Also, if the user is traveling, voice instructions related to the user's destination can be preferentially accepted. Furthermore, if the user is in a specific region, voice instructions related to that region can be preferentially accepted. In this way, by taking into account the user's geographical location information, highly relevant voice instructions can be preferentially accepted.

[0103] The video editing system can analyze a user's social media activity to prioritize receiving voice instructions. For example, it can prioritize receiving relevant voice instructions based on the content the user has shared on social media. It can also prioritize receiving voice instructions related to topics of interest from the user's social media activity. It can also prioritize receiving relevant voice instructions based on the accounts the user follows on social media. In this way, it is possible to prioritize receiving relevant voice instructions by analyzing a user's social media activity.

[0104] The video editing system can estimate the user's emotions and adjust the timing of accepting voice instructions based on the estimated user emotions. For example, if the user is feeling stressed, the system can quickly accept voice instructions and start editing immediately. Alternatively, if the user is relaxed, the system can slowly accept voice instructions and accept detailed instructions. Furthermore, if the user is in a hurry, the system can prioritize accepting voice instructions and postpone other processing. In this way, by adjusting the timing of accepting voice instructions according to the user's emotions, instructions can be accepted at a more appropriate timing.

[0105] The video editing system can estimate the user's emotions and determine the priority of voice instructions based on the estimated user's emotions. For example, if the user is nervous, important voice instructions can be given priority. Also, if the user is relaxed, detailed voice instructions can be given priority. Furthermore, if the user is in a hurry, voice instructions that can be processed quickly can be given priority. Thus, by determining the priority of voice instructions according to the user's emotions, important instructions can be given priority.

[0106] The video editing system can estimate the user's emotions and adjust the editing method based on the estimated user's emotions. For example, if the user is relaxed, detailed editing can be performed. If the user is in a hurry, simple editing can be performed. Furthermore, if the user is excited, visually stimulating editing can be performed. In this way, by adjusting the editing method according to the user's emotions, more appropriate editing results can be provided.

[0107] The video editing system can estimate the user's emotions and adjust the confirmation display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the confirmation display method according to the user's emotions, a more appropriate display method can be provided.

[0108] The video editing system can estimate the user's emotions and select music to add based on the estimated user emotions. For example, if the user is relaxed, calm music can be selected. If the user is excited, upbeat music can be selected. Furthermore, if the user is sad, soothing music can be selected. In this way, more appropriate music can be added by selecting the most appropriate music according to the user's emotions.

[0109] The video editing system can estimate the user's emotions and select text to insert based on the estimated user emotions. For example, if the user is relaxed, calming text can be selected. If the user is excited, visually stimulating text can be selected. Furthermore, if the user is sad, soothing text can be selected. In this way, more appropriate text can be inserted by selecting the most appropriate text according to the user's emotions.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit receives voice instructions. Voice instructions include "cut this part," "add music," "insert text," etc. The reception unit converts the voice instructions into text data using voice recognition technology. Step 2: The analysis unit analyzes the voice instructions received by the reception unit and identifies the edit content. The analysis unit analyzes the voice instructions using a generation AI and identifies the edit content. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The editing department implements the editing content identified by the analysis department. The editing department performs editing such as cutting, trimming, and adding effects to the video. The editing department uses generation AI to implement the editing content. The generation AI uses video editing AI, effect generation AI, etc. Step 4: The confirmation unit is a part where the user can confirm the edited video and give additional instructions. The confirmation unit previews the edited video and the user can give additional instructions. The confirmation unit uses the generation AI to accept the user's additional instructions.

[0112] 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.

[0113] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice instructions; an analysis unit that analyzes the voice instruction received by the reception unit and identifies editing content; an editing unit that implements the editing content identified by the analysis unit; A confirmation section is provided for the user to check the edited video and give additional instructions. system.

2. The editorial department Includes a music adding section for adding music 2. The system of claim 1.

3. The editorial department A text insertion section is provided for inserting text.

2. The system of claim 1.

4. The analysis unit Analyzes voice instructions and determines which parts to cut 2. The system of claim 1.

5. The confirmation unit The user plays the edited video and gives additional instructions 2. The system of claim 1.

6. The reception unit The system estimates the user's emotions and adjusts the timing of accepting voice instructions based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past voice instruction history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit When receiving voice commands, filtering is performed based on the user's current projects and interests.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and determine the priority of voice instructions to be accepted based on the estimated user emotions.

2. The system of claim 1.

Images