system

Abstract

The system according to this embodiment aims to streamline the content collection and editing of wedding videos, thereby reducing the effort and time required. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects content from guests using a 2D code. The generation unit summarizes the content collected by the collection unit and constructs a storyline. The modification unit allows the bride and groom to review the movie generated by the generation unit and make corrections as necessary.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that collecting and editing the content of a wedding movie takes a great deal of effort and time, and professional technology is required.

[0005] The system according to the embodiment aims to streamline the collection and editing of wedding movie content and reduce the effort and time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects content from guests using a 2D code. The generation unit summarizes the content collected by the collection unit and constructs a storyline. The modification unit allows the bride and groom to review the movie generated by the generation unit and make corrections as necessary. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the collection and editing of wedding video content, reducing the effort and time required. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The wedding video production system according to an embodiment of the present invention is a system for streamlining the production of wedding videos. This wedding video production system solves the conventional problems of scheduling, planning the overall flow, difficulty in content collection, and the need for professional video editing skills by providing content collection using 2D codes (e.g., QR code®), automatic editing and summarization functions by generation AI, and fact-checking and interactive correction functions. For example, the wedding video production system prints a 2D code on the invitation, allowing guests to access a dedicated portal. Guests can easily post photos and stories through this portal. This significantly reduces the effort required for content collection. Next, the wedding video production system uses generation AI to summarize the content provided by guests and build a storyline. Furthermore, the wedding video production system automatically creates a movie in conjunction with a video editing tool. This eliminates the need for professional video editing skills and significantly reduces production time and effort. Finally, the wedding video production system allows the bride and groom to review the movie created by the generation AI and make interactive corrections as needed while interacting with the AI. This allows for adjustments to meet individual needs and enables the provision of a high-quality movie. This allows the wedding video production system to significantly reduce production time and effort, produce high-quality videos, and enhance participatory content. Active guest participation in content creation results in more personalized videos, making wedding memories even more moving. In short, the wedding video production system streamlines wedding video production and, through active guest participation, delivers more unique and personalized videos.

[0029] The wedding movie production system according to this embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects content from guests using a 2D code. The collection unit allows guests to access a dedicated portal by scanning a 2D code printed on an invitation with their smartphone. The collection unit provides a function that allows guests to easily post photos and episodes through the dedicated portal. For example, the collection unit provides an interface for guests to upload photos they have taken. The collection unit also provides an input form for guests to post episodes in text format. Furthermore, the collection unit includes a management unit for managing the posted content. The management unit provides a function to organize the posted content by category and to perform searches and filtering. The generation unit uses a generation AI to summarize the content collected by the collection unit and construct a storyline. For example, the generation AI analyzes the provided content, extracts important information, and summarizes it. The generation unit automatically constructs a storyline based on the summarized information. The generation unit further works in conjunction with a video editing tool to automatically create a movie. For example, the generation unit determines the order of scenes and adds appropriate effects and transitions based on the information summarized by the generation AI. The editing unit provides a function for the bride and groom to edit the movie while interacting with the generation AI. For example, the editing unit provides an interface for the bride and groom to edit specific scenes of the movie while interacting with the generation AI. Based on the revision suggestions made by the generation AI, the editing unit allows the bride and groom to review the revisions and make corrections as needed. As a result, the wedding movie production system according to this embodiment enables content collection using 2D codes, automatic editing and summarization by generation AI, and interactive editing.

[0030] The collection unit collects content from guests using QR codes. Specifically, guests can access a dedicated portal by scanning a QR code printed on their invitation with their smartphone. This dedicated portal is designed to allow guests to easily post photos and stories. For example, the collection unit provides an intuitive interface for guests to upload photos they have taken. Guests can select photos from their smartphone's camera roll and complete the upload in just a few taps. The collection unit also provides an input form for guests to post stories in text format. This input form is designed to allow guests to easily enter memorable stories and messages, and clearly indicates character limits and formatting guidelines. Furthermore, the collection unit has a management unit for managing the posted content. The management unit, for example, organizes the posted content by category and provides functions for searching and filtering. This allows for efficient management of the large amount of collected content and quick retrieval of necessary information. The management unit also has a function to automatically detect and organize duplicate content, preventing the same photos or stories from being posted multiple times. In addition, the management unit has a function to check the quality of the posted content and ensure that it does not contain any inappropriate material. This allows the collection unit to efficiently and securely collect and manage content from guests.

[0031] The generation unit uses a generation AI to summarize the content collected by the collection unit and construct a storyline. Specifically, the generation AI analyzes the provided content, extracts important information, and summarizes it. The generation AI utilizes natural language processing technology to extract touching moments and humorous episodes from text episodes and associates them with the content of photos and videos. Based on the summarized information, the generation unit automatically constructs a storyline. For example, the generation AI determines the order of scenes according to the wedding theme and the preferences of the bride and groom, and adds appropriate effects and transitions. The generation unit further works with video editing tools to automatically create the movie. The generation AI analyzes the collected photos and videos and selects scenes to display at the optimal timing. For example, the generation AI applies effects such as slow motion and zoom-in to emphasize the smiles and touching moments of the guests. The generation AI also adjusts scene changes and transitions to match the rhythm and atmosphere of the music. As a result, the generation unit can automatically generate a wedding movie with a moving and consistent storyline based on the collected content. Furthermore, the generation unit provides a preview of the generated movie, allowing the bride and groom to review the content. This enables the generation unit to produce wedding videos efficiently and with high quality.

[0032] The editing section provides a function that allows the bride and groom to edit the movie while interacting with the generating AI. Specifically, it provides an interface for the bride and groom to edit specific scenes in the movie while interacting with the generating AI. This interface is designed to be intuitive and easy to use, making it easy for the bride and groom to operate. For example, the editing section allows the bride and groom to review the suggested edits from the generating AI and make changes as needed. The generating AI receives feedback from the bride and groom in real time and presents edit suggestions. For example, if the bride and groom want to change a specific scene, the generating AI will suggest other content related to that scene and present the best edit suggestion. The bride and groom can review the AI's suggestions and make changes as needed. The editing section also has a function that instantly reflects the edits and provides a preview. This allows the bride and groom to adjust the final look of the movie while reviewing the edits. Furthermore, the editing section has a function that allows the generating AI to learn the bride and groom's preferences and requests, improving the accuracy of future edit suggestions. This allows the editing team to revise the video until the bride and groom are satisfied, ultimately completing a high-quality wedding video.

[0033] The collection unit provides guests with the functionality to access a dedicated portal and post photos and episodes. For example, guests can access the dedicated portal by scanning a QR code with their smartphone. The collection unit provides an interface for guests to upload photos they have taken. For example, the collection unit provides a photo upload button, making it easy for guests to post photos. The collection unit also provides an input form for guests to post episodes in text format. For example, the collection unit provides an input field for episodes, allowing guests to freely write their episodes. This makes it easy for guests to post content through the dedicated portal. The dedicated portal provides guidelines, for example, to clarify login methods and posting procedures. Some or all of the above processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can automatically categorize and manage the content posted by guests using AI.

[0034] The generation unit summarizes the provided content using generation AI, constructs a storyline, and automatically creates a movie. For example, the generation unit analyzes the content provided by the generation AI, extracts important information, and summarizes it. Based on the summarized information, the generation unit automatically constructs a storyline. The generation unit further works in conjunction with video editing tools to automatically create a movie. For example, based on the information summarized by the generation AI, the generation unit determines the order of scenes and adds appropriate effects and transitions. The generation AI summarizes the provided content using, for example, text generation AI (e.g., LLM). The generation AI analyzes the provided content, extracts important information, and summarizes it. Based on the summarized information, the generation AI automatically constructs a storyline. The generation AI further works in conjunction with video editing tools to automatically create a movie. For example, based on the summarized information, the generation AI determines the order of scenes and adds appropriate effects and transitions. This makes it possible to summarize content and automatically create movies using generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can construct a storyline and create a movie based on the information summarized by the generation AI.

[0035] The editing section provides a function for the bride and groom to edit the movie while interacting with the generating AI. For example, the editing section provides an interface for the bride and groom to edit specific scenes in the movie while interacting with the generating AI. Based on the editing suggestions made by the generating AI, the editing section allows the bride and groom to review the edits and make corrections as needed. For example, the editing section displays the editing suggestions made by the generating AI, and the bride and groom can select the edits. The editing section also allows the bride and groom to adjust the edits while interacting with the generating AI. For example, the editing section allows the bride and groom to provide feedback on the editing suggestions made by the generating AI and adjust the edits. This allows the bride and groom to edit the movie while interacting with the generating AI. The dialogue provides guidelines to clarify the format and content of the dialogue. Some or all of the above processes in the editing section may be performed using AI, for example, or without AI. For example, based on the editing suggestions made by the generating AI, the editing section allows the bride and groom to review the edits and make corrections.

[0036] The collection unit includes a management unit for managing content posted by guests. For example, the collection unit provides a management unit for managing photos and episodes posted by guests. The management unit organizes posted content by category and provides functions for searching and filtering. For example, the management unit categorizes photos and episodes, making it easy to search for content posted by guests. The management unit can also filter posted content and display it based on specific conditions. For example, the management unit can display only content posted by a specific guest. This allows for efficient management of content posted by guests. Management provides guidelines to clarify management procedures and the scope of management. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can use AI to automatically categorize and manage posted content.

[0037] The generation unit includes a verification unit for reviewing the movie created by the generation AI. The generation unit provides, for example, a verification unit for reviewing the movie created by the generation AI. The verification unit displays the movie created by the generation AI, allowing the bride and groom to review it. For example, the verification unit provides an interface for playing the movie created by the generation AI. The verification unit also provides a function for the bride and groom to review specific scenes of the movie. For example, the verification unit can select and play a specific scene of the movie. This allows them to review the movie created by the generation AI. The verification provides, for example, guidelines to clarify the verification procedure and the scope of verification. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can review the movie created by the generation AI and make corrections as needed.

[0038] The editing unit includes a dialogue unit for interacting with a generating AI. The editing unit provides, for example, a dialogue unit for editing a movie while interacting with the generating AI. The dialogue unit allows the bride and groom to review the edits proposed by the generating AI and make corrections as needed. For example, the dialogue unit displays the edits proposed by the generating AI, and the bride and groom can select the edits. The dialogue unit also allows the bride and groom to adjust the edits while interacting with the generating AI. For example, the dialogue unit allows the bride and groom to provide feedback on the edits proposed by the generating AI and adjust the edits. This enables editing while interacting with the generating AI. The dialogue provides, for example, guidelines to clarify the format and content of the dialogue. Some or all of the above-described processes in the editing unit may be performed using, for example, AI, or not using AI. For example, the editing unit allows the bride and groom to review the edits proposed by the generating AI and make corrections.

[0039] The data collection unit analyzes the guest's past posting history and selects the optimal data collection method. For example, if the guest has posted many photos in the past, the data collection unit may prioritize encouraging photo posting. If the guest has posted many episodes in the past, the data collection unit may prioritize encouraging episode posting. The data collection unit can also select the most effective data collection method from the guest's past posting history and send notifications. This allows for the selection of the optimal data collection method based on the guest's past posting history. The posting history provides guidelines, for example, to clarify the type of posts and the methods of analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the guest's past posting history into AI and have the AI ​​select the optimal data collection method.

[0040] The collection unit filters posts based on the guest's areas of interest when collecting content. For example, if the guest is interested in travel, the collection unit will prioritize collecting travel-related photos and stories. If the guest is interested in cooking, the collection unit can prioritize collecting cooking-related photos and stories. The collection unit can also filter and collect highly relevant content based on the guest's areas of interest. This allows for the collection of highly relevant content based on the guest's areas of interest. Areas of interest can provide guidelines, such as clarifying survey results or past posts. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the guest's areas of interest into an AI and have the AI ​​perform the filtering of highly relevant content.

[0041] The collection unit prioritizes collecting highly relevant content by considering the guest's geographical location information during content collection. For example, if the guest is in a specific region, the collection unit prioritizes collecting content related to that region. If the guest is traveling, the collection unit can prioritize collecting content related to their travel destination. The collection unit can also filter and collect highly relevant content based on the guest's geographical location information. This allows for the collection of highly relevant content based on the guest's geographical location information. Geographical location information provides guidelines for clarifying methods such as GPS data and location information analysis. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the guest's geographical location information into AI and have the AI ​​perform the collection of highly relevant content.

[0042] The collection unit analyzes the guest's social media activity and collects relevant content during content collection. For example, the collection unit collects photos and stories shared by the guest on social media. The collection unit can collect content related to topics of interest from the guest's social media activity. The collection unit can also analyze the guest's social media activity, filter, and collect highly relevant content. This allows for the collection of highly relevant content based on the guest's social media activity. Social media activity provides guidelines for clarifying, for example, the analysis of post content and activity frequency. Some or all of the above processing in the collection unit may be performed using AI, or not, for example. For example, the collection unit can input the guest's social media activity into AI and have the AI ​​perform the collection of relevant content.

[0043] The generation unit adjusts the level of detail in the storyline based on the importance of the content during generation. For example, the generation unit can enrich the storyline by adding detailed explanations to important content, or simplify the storyline by adding concise explanations to less important content. The generation unit can also adjust the level of detail in the storyline according to the importance of the content to produce a well-balanced movie. This allows for adjustment of the level of detail in the storyline according to the importance of the content. Importance provides guidelines for clarifying content evaluation criteria and importance scoring methods, for example. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the importance of the content into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the storyline.

[0044] The generation unit applies different generation algorithms depending on the content category during generation. For example, the generation unit can apply an image processing algorithm to photographic content to generate a visually appealing storyline. For episodic content, it can apply a natural language processing algorithm to generate an emotionally rich storyline. Furthermore, the generation unit can apply a video processing algorithm to video content to generate a dynamic storyline. This allows the optimal generation algorithm to be applied according to the content category. Categories provide guidelines to clarify the definition of content types and categories, for example. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the content category into the AI ​​and have the AI ​​apply the optimal generation algorithm.

[0045] The generation unit determines the priority of storylines based on the submission timing of the content during generation. For example, the generation unit may prioritize incorporating content submitted early into the storyline. The generation unit may also prioritize incorporating content with upcoming submission dates into the storyline. Furthermore, the generation unit can determine the priority of storylines based on submission timing and generate a balanced movie. This allows for the prioritization of storylines based on the submission timing of the content. The submission timing provides guidelines, for example, to clarify how to record submission dates and criteria for evaluating submission timing. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the content submission timing into AI and have the AI ​​perform the determination of storyline prioritization.

[0046] The generation unit adjusts the order of storylines based on the relevance of the content during generation. For example, the generation unit can place highly relevant content consecutively to make the flow of the storyline smooth. The generation unit can also maintain the consistency of the storyline by postponing less relevant content. Furthermore, the generation unit can adjust the order of storylines based on the relevance of the content to generate a balanced movie. This allows for the adjustment of the order of storylines based on the relevance of the content. Relevance provides guidelines for clarifying, for example, the definition of content relevance and the method of scoring relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the relevance of the content into AI and have the AI ​​perform the adjustment of the storyline order.

[0047] The editing unit selects the optimal editing method by referring to the bride and groom's past editing history during editing. For example, the editing unit proposes the optimal editing method based on the editing content previously performed by the bride and groom. The editing unit can analyze and propose preferred editing styles from the bride and groom's past editing history. Furthermore, the editing unit can refer to the bride and groom's past editing history and propose ways to reduce the effort required for editing. This allows the optimal editing method to be selected based on the bride and groom's past editing history. The editing history provides guidelines to clarify past editing content and editing history recording methods, for example. Some or all of the above processes in the editing unit may be performed using AI, or not. For example, the editing unit can input the bride and groom's past editing history into AI and have the AI ​​select the optimal editing method.

[0048] The editing unit customizes the editing process based on the bride and groom's current needs. For example, if the bride and groom want to emphasize a particular scene, the editing unit will make an edit that emphasizes that scene. If the bride and groom want to add a particular effect, the editing unit can make an edit that adds that effect. The editing unit can also customize the editing process based on the bride and groom's current needs to provide the optimal movie. This allows the editing process to be customized according to the bride and groom's current needs. Needs can be provided, for example, through survey results or guidelines to clarify current requirements. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's current needs into AI and have the AI ​​perform the customization of the editing process.

[0049] The editing unit selects the optimal editing method when editing, taking into account the geographical location information of the bride and groom. For example, if the bride and groom are in a specific region, the editing unit will suggest an editing method related to that region. If the bride and groom are traveling, the editing unit can suggest an editing method related to their travel destination. Furthermore, the editing unit can select and suggest the optimal editing method based on the geographical location information of the bride and groom. This allows for the selection of the optimal editing method based on the geographical location information of the bride and groom. Geographical location information provides, for example, GPS data and guidelines to clarify the method of analyzing location information. Some or all of the above processing in the editing unit may be performed using, for example, AI, or not using AI. For example, the editing unit can input the geographical location information of the bride and groom into AI and have the AI ​​select the optimal editing method.

[0050] The editing unit analyzes the bride and groom's social media activity during editing and proposes editing methods. For example, the editing unit proposes editing methods based on photos and stories shared by the bride and groom on social media. The editing unit can analyze and propose preferred editing styles from the bride and groom's social media activity. The editing unit can also analyze the bride and groom's social media activity and propose highly relevant editing methods. This allows the editing unit to propose the most suitable editing methods based on the bride and groom's social media activity. Social media activity provides guidelines to clarify, for example, the analysis of post content and activity frequency. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's social media activity into AI and have the AI ​​execute the editing method proposal.

[0051] The management department selects the optimal management method by referring to the guest's past posting history during management. For example, the management department proposes the optimal management method based on the content the guest has previously posted. The management department can analyze and propose preferred management styles from the guest's past posting history. Furthermore, the management department can refer to the guest's past posting history and propose ways to reduce the effort required for management. This allows the selection of the optimal management method based on the guest's past posting history. The posting history provides guidelines to clarify, for example, the type of posts and the methods of analysis. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the guest's past posting history into AI and have the AI ​​select the optimal management method.

[0052] The management department selects the optimal management method when managing guests, taking into account their geographical location information. For example, if a guest is in a specific region, the management department will prioritize managing content related to that region. If a guest is traveling, the management department can prioritize managing content related to their travel destination. Furthermore, the management department can select and propose the optimal management method based on the guest's geographical location information. This allows for the selection of the optimal management method based on the guest's geographical location information. Geographical location information provides guidelines to clarify methods for analyzing GPS data and location information. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the guest's geographical location information into AI and have the AI ​​select the optimal management method.

[0053] The verification unit selects the optimal verification method by referring to the bride and groom's past verification history during the verification process. For example, the verification unit proposes the optimal verification method based on the verification content performed by the bride and groom in the past. The verification unit can analyze and propose preferred verification styles from the bride and groom's past verification history. The verification unit can also refer to the bride and groom's past verification history and propose ways to reduce the effort required for verification. This allows the system to select the optimal verification method based on the bride and groom's past verification history. The verification history provides guidelines to clarify past verification content and how the verification history is recorded. Some or all of the above processes in the verification unit may be performed using AI, for example, or not. For example, the verification unit can input the bride and groom's past verification history into AI and have the AI ​​select the optimal verification method.

[0054] The verification unit selects the optimal verification method during verification, taking into account the geographical location information of the bride and groom. For example, if the bride and groom are in a specific region, the verification unit will suggest a verification method related to that region. If the bride and groom are traveling, the verification unit can suggest a verification method related to their travel destination. Furthermore, the verification unit can select and suggest the optimal verification method based on the geographical location information of the bride and groom. This allows for the selection of the optimal verification method based on the geographical location information of the bride and groom. Geographical location information provides, for example, GPS data and guidelines to clarify the method of analyzing location information. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input the geographical location information of the bride and groom into AI and have the AI ​​select the optimal verification method.

[0055] The dialogue unit selects the optimal dialogue method by referring to the bride and groom's past dialogue history during a conversation. For example, the dialogue unit proposes the optimal dialogue method based on the content of past conversations the bride and groom have had. The dialogue unit can analyze and propose preferred dialogue styles from the bride and groom's past dialogue history. The dialogue unit can also refer to the bride and groom's past dialogue history and propose ways to reduce the effort required for the conversation. This allows the optimal dialogue method to be selected based on the bride and groom's past dialogue history. The dialogue history provides guidelines to clarify, for example, the content of past conversations and how the dialogue history is recorded. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the bride and groom's past dialogue history into AI and have the AI ​​select the optimal dialogue method.

[0056] The dialogue unit selects the optimal dialogue method during a conversation, taking into account the geographical location information of the bride and groom. For example, if the bride and groom are in a specific region, the dialogue unit will suggest a dialogue method related to that region. If the bride and groom are traveling, the dialogue unit can suggest a dialogue method related to their travel destination. Furthermore, the dialogue unit can select and suggest the optimal dialogue method based on the geographical location information of the bride and groom. This allows for the selection of the optimal dialogue method based on the geographical location information of the bride and groom. Geographical location information can include, for example, GPS data or guidelines to clarify the method of analyzing location information. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input the geographical location information of the bride and groom into the AI ​​and have the AI ​​select the optimal dialogue method.

[0057] The dialogue unit analyzes the bride and groom's social media activity during a dialogue and proposes a dialogue method. For example, the dialogue unit proposes a dialogue method based on photos and stories shared by the bride and groom on social media. The dialogue unit can analyze and propose a preferred dialogue style from the bride and groom's social media activity. Furthermore, the dialogue unit can analyze the bride and groom's social media activity and propose a highly relevant dialogue method. This allows the dialogue unit to propose the optimal dialogue method based on the bride and groom's social media activity. Social media activity provides guidelines to clarify, for example, the analysis of post content and activity frequency. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the bride and groom's social media activity into AI and have the AI ​​propose a dialogue method.

[0058] The dialogue unit, during a conversation, refers to the bride and groom's calendar information and makes suggestions based on their schedule. For example, the dialogue unit refers to the schedule registered in the bride and groom's calendar and adjusts the content of the conversation. The dialogue unit can suggest conversations related to specific events based on the bride and groom's calendar information. Furthermore, the dialogue unit can suggest the most suitable conversation method based on the schedule, based on the bride and groom's calendar information. This allows the dialogue unit to suggest the most suitable conversation method based on the bride and groom's calendar information. The calendar information provides guidelines to clarify, for example, how to record schedules and how to analyze calendar information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the bride and groom's calendar information into AI and have the AI ​​execute suggestions based on the schedule.

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

[0060] The generation unit can summarize the content provided using generation AI and adjust the level of detail in the summary based on the importance of the content when constructing a storyline. For example, important episodes can be summarized in detail to ensure they occupy a significant position in the storyline. Conversely, less important content can be summarized concisely to ensure a smooth flow of the storyline. Furthermore, the generation unit can adjust the level of detail in the storyline according to the importance of the content to generate a well-balanced movie.

[0061] The data collection unit can analyze a guest's past posting history and select the most suitable collection method. For example, if a guest has posted many photos in the past, it can prioritize encouraging photo posting. Similarly, if a guest has posted many episodes in the past, it can prioritize encouraging episode posting. Furthermore, the data collection unit can select the most effective collection method based on the guest's past posting history and send notifications. This enables the optimal collection method based on the guest's past posting history.

[0062] The editing function can select the optimal editing method by referring to the bride and groom's past editing history. For example, it can propose the most suitable editing method based on the editing content the bride and groom have done in the past. It can also analyze and propose preferred editing styles from the bride and groom's past editing history. Furthermore, it can suggest ways to reduce the effort required for editing by referring to the bride and groom's past editing history. This makes it possible to determine the optimal editing method based on the bride and groom's past editing history.

[0063] The generation unit can apply different generation algorithms depending on the content category during generation. For example, an image processing algorithm can be applied to photographic content to generate a visually appealing storyline. A natural language processing algorithm can be applied to episodic content to generate an emotionally rich storyline. Furthermore, a video processing algorithm can be applied to video content to generate a dynamic storyline. This makes it possible to apply the optimal generation algorithm according to the content category.

[0064] The data collection unit can prioritize collecting highly relevant content by considering the guest's geographical location. For example, if a guest is in a specific region, it can prioritize collecting content related to that region. Similarly, if a guest is traveling, it can prioritize collecting content related to their travel destination. Furthermore, the data collection unit can filter and collect highly relevant content based on the guest's geographical location. This enables optimal content collection based on the guest's geographical location.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects content from guests using QR codes. For example, guests can access a dedicated portal by scanning a QR code printed on their invitation with their smartphone. The collection unit provides a function that allows guests to easily post photos and stories through the dedicated portal. The collection unit provides an interface for guests to upload photos they have taken and an input form for posting stories in text format. Furthermore, the collection unit has a management unit for managing the posted content, providing functions to organize the posted content by category and to search and filter it. Step 2: The generation unit uses generation AI to summarize the content collected by the collection unit and build a storyline. The generation unit analyzes the provided content, extracts important information, and summarizes it. Based on the summarized information, it automatically builds a storyline and automatically creates a movie in conjunction with a video editing tool. For example, based on the information summarized by the generation AI, the generation unit determines the order of scenes and adds appropriate effects and transitions. Step 3: The editing section provides a function for the bride and groom to edit the movie while interacting with the generating AI. The editing section provides an interface for the bride and groom to edit specific scenes in the movie while interacting with the generating AI. Based on the revision suggestions made by the generating AI, the bride and groom can review the revisions and make corrections as needed.

[0067] (Example of form 2) The wedding video production system according to an embodiment of the present invention is a system for streamlining the production of wedding videos. This wedding video production system solves the conventional problems of scheduling, planning the overall flow, difficulty in content collection, and the need for professional video editing skills by providing content collection using 2D codes (e.g., QR codes), automatic editing and summarization functions by generation AI, and fact-checking and interactive correction functions. For example, the wedding video production system prints a 2D code on the invitation, allowing guests to access a dedicated portal. Guests can easily post photos and stories through this portal. This significantly reduces the effort required for content collection. Next, the wedding video production system uses generation AI to summarize the content provided by guests and build a storyline. Furthermore, the wedding video production system automatically creates a video in conjunction with a video editing tool. This eliminates the need for professional video editing skills and significantly reduces production time and effort. Finally, the wedding video production system allows the bride and groom to review the video created by the generation AI and make interactive corrections as needed while interacting with the AI. This enables adjustments to meet individual needs and provides a high-quality video. This allows the wedding video production system to significantly reduce production time and effort, produce high-quality videos, and enhance participatory content. Active guest participation in content creation results in more personalized videos, making wedding memories even more moving. In short, the wedding video production system streamlines wedding video production and, through active guest participation, delivers more unique and personalized videos.

[0068] The wedding movie production system according to this embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects content from guests using a 2D code. The collection unit allows guests to access a dedicated portal by scanning a 2D code printed on an invitation with their smartphone. The collection unit provides a function that allows guests to easily post photos and episodes through the dedicated portal. For example, the collection unit provides an interface for guests to upload photos they have taken. The collection unit also provides an input form for guests to post episodes in text format. Furthermore, the collection unit includes a management unit for managing the posted content. The management unit provides a function to organize the posted content by category and to perform searches and filtering. The generation unit uses a generation AI to summarize the content collected by the collection unit and construct a storyline. For example, the generation AI analyzes the provided content, extracts important information, and summarizes it. The generation unit automatically constructs a storyline based on the summarized information. The generation unit further works in conjunction with a video editing tool to automatically create a movie. For example, the generation unit determines the order of scenes and adds appropriate effects and transitions based on the information summarized by the generation AI. The editing unit provides a function for the bride and groom to edit the movie while interacting with the generation AI. For example, the editing unit provides an interface for the bride and groom to edit specific scenes of the movie while interacting with the generation AI. Based on the revision suggestions made by the generation AI, the editing unit allows the bride and groom to review the revisions and make corrections as needed. As a result, the wedding movie production system according to this embodiment enables content collection using 2D codes, automatic editing and summarization by generation AI, and interactive editing.

[0069] The collection unit collects content from guests using QR codes. Specifically, guests can access a dedicated portal by scanning a QR code printed on their invitation with their smartphone. This dedicated portal is designed to allow guests to easily post photos and stories. For example, the collection unit provides an intuitive interface for guests to upload photos they have taken. Guests can select photos from their smartphone's camera roll and complete the upload in just a few taps. The collection unit also provides an input form for guests to post stories in text format. This input form is designed to allow guests to easily enter memorable stories and messages, and clearly indicates character limits and formatting guidelines. Furthermore, the collection unit has a management unit for managing the posted content. The management unit, for example, organizes the posted content by category and provides functions for searching and filtering. This allows for efficient management of the large amount of collected content and quick retrieval of necessary information. The management unit also has a function to automatically detect and organize duplicate content, preventing the same photos or stories from being posted multiple times. In addition, the management unit has a function to check the quality of the posted content and ensure that it does not contain any inappropriate material. This allows the collection unit to efficiently and securely collect and manage content from guests.

[0070] The generation unit uses a generation AI to summarize the content collected by the collection unit and construct a storyline. Specifically, the generation AI analyzes the provided content, extracts important information, and summarizes it. The generation AI utilizes natural language processing technology to extract touching moments and humorous episodes from text episodes and associates them with the content of photos and videos. Based on the summarized information, the generation unit automatically constructs a storyline. For example, the generation AI determines the order of scenes according to the wedding theme and the preferences of the bride and groom, and adds appropriate effects and transitions. The generation unit further works with video editing tools to automatically create the movie. The generation AI analyzes the collected photos and videos and selects scenes to display at the optimal timing. For example, the generation AI applies effects such as slow motion and zoom-in to emphasize the smiles and touching moments of the guests. The generation AI also adjusts scene changes and transitions to match the rhythm and atmosphere of the music. As a result, the generation unit can automatically generate a wedding movie with a moving and consistent storyline based on the collected content. Furthermore, the generation unit provides a preview of the generated movie, allowing the bride and groom to review the content. This enables the generation unit to produce wedding videos efficiently and with high quality.

[0071] The editing section provides a function that allows the bride and groom to edit the movie while interacting with the generating AI. Specifically, it provides an interface for the bride and groom to edit specific scenes in the movie while interacting with the generating AI. This interface is designed to be intuitive and easy to use, making it easy for the bride and groom to operate. For example, the editing section allows the bride and groom to review the suggested edits from the generating AI and make changes as needed. The generating AI receives feedback from the bride and groom in real time and presents edit suggestions. For example, if the bride and groom want to change a specific scene, the generating AI will suggest other content related to that scene and present the best edit suggestion. The bride and groom can review the AI's suggestions and make changes as needed. The editing section also has a function that instantly reflects the edits and provides a preview. This allows the bride and groom to adjust the final look of the movie while reviewing the edits. Furthermore, the editing section has a function that allows the generating AI to learn the bride and groom's preferences and requests, improving the accuracy of future edit suggestions. This allows the editing team to revise the video until the bride and groom are satisfied, ultimately completing a high-quality wedding video.

[0072] The collection unit provides guests with the functionality to access a dedicated portal and post photos and episodes. For example, guests can access the dedicated portal by scanning a QR code with their smartphone. The collection unit provides an interface for guests to upload photos they have taken. For example, the collection unit provides a photo upload button, making it easy for guests to post photos. The collection unit also provides an input form for guests to post episodes in text format. For example, the collection unit provides an input field for episodes, allowing guests to freely write their episodes. This makes it easy for guests to post content through the dedicated portal. The dedicated portal provides guidelines, for example, to clarify login methods and posting procedures. Some or all of the above processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can automatically categorize and manage the content posted by guests using AI.

[0073] The generation unit summarizes the provided content using generation AI, constructs a storyline, and automatically creates a movie. For example, the generation unit analyzes the content provided by the generation AI, extracts important information, and summarizes it. Based on the summarized information, the generation unit automatically constructs a storyline. The generation unit further works in conjunction with video editing tools to automatically create a movie. For example, based on the information summarized by the generation AI, the generation unit determines the order of scenes and adds appropriate effects and transitions. The generation AI summarizes the provided content using, for example, text generation AI (e.g., LLM). The generation AI analyzes the provided content, extracts important information, and summarizes it. Based on the summarized information, the generation AI automatically constructs a storyline. The generation AI further works in conjunction with video editing tools to automatically create a movie. For example, based on the summarized information, the generation AI determines the order of scenes and adds appropriate effects and transitions. This makes it possible to summarize content and automatically create movies using generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can construct a storyline and create a movie based on the information summarized by the generation AI.

[0074] The editing section provides a function for the bride and groom to edit the movie while interacting with the generating AI. For example, the editing section provides an interface for the bride and groom to edit specific scenes in the movie while interacting with the generating AI. Based on the editing suggestions made by the generating AI, the editing section allows the bride and groom to review the edits and make corrections as needed. For example, the editing section displays the editing suggestions made by the generating AI, and the bride and groom can select the edits. The editing section also allows the bride and groom to adjust the edits while interacting with the generating AI. For example, the editing section allows the bride and groom to provide feedback on the editing suggestions made by the generating AI and adjust the edits. This allows the bride and groom to edit the movie while interacting with the generating AI. The dialogue provides guidelines to clarify the format and content of the dialogue. Some or all of the above processes in the editing section may be performed using AI, for example, or without AI. For example, based on the editing suggestions made by the generating AI, the editing section allows the bride and groom to review the edits and make corrections.

[0075] The collection unit includes a management unit for managing content posted by guests. For example, the collection unit provides a management unit for managing photos and episodes posted by guests. The management unit organizes posted content by category and provides functions for searching and filtering. For example, the management unit categorizes photos and episodes, making it easy to search for content posted by guests. The management unit can also filter posted content and display it based on specific conditions. For example, the management unit can display only content posted by a specific guest. This allows for efficient management of content posted by guests. Management provides guidelines to clarify management procedures and the scope of management. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can use AI to automatically categorize and manage posted content.

[0076] The generation unit includes a verification unit for reviewing the movie created by the generation AI. The generation unit provides, for example, a verification unit for reviewing the movie created by the generation AI. The verification unit displays the movie created by the generation AI, allowing the bride and groom to review it. For example, the verification unit provides an interface for playing the movie created by the generation AI. The verification unit also provides a function for the bride and groom to review specific scenes of the movie. For example, the verification unit can select and play a specific scene of the movie. This allows them to review the movie created by the generation AI. The verification provides, for example, guidelines to clarify the verification procedure and the scope of verification. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can review the movie created by the generation AI and make corrections as needed.

[0077] The editing unit includes a dialogue unit for interacting with a generating AI. The editing unit provides, for example, a dialogue unit for editing a movie while interacting with the generating AI. The dialogue unit allows the bride and groom to review the edits proposed by the generating AI and make corrections as needed. For example, the dialogue unit displays the edits proposed by the generating AI, and the bride and groom can select the edits. The dialogue unit also allows the bride and groom to adjust the edits while interacting with the generating AI. For example, the dialogue unit allows the bride and groom to provide feedback on the edits proposed by the generating AI and adjust the edits. This enables editing while interacting with the generating AI. The dialogue provides, for example, guidelines to clarify the format and content of the dialogue. Some or all of the above-described processes in the editing unit may be performed using, for example, AI, or not using AI. For example, the editing unit allows the bride and groom to review the edits proposed by the generating AI and make corrections.

[0078] The collection unit estimates the guest's emotions and adjusts the timing of content collection based on the estimated emotions. For example, if the guest is excited, the collection unit can start collecting content immediately to encourage posting while emotions are high. If the guest is relaxed, the collection unit can collect content at a slower pace to encourage natural posting. If the guest is busy, the collection unit can send a notification at an appropriate time to encourage posting. This allows for content collection at the optimal time according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input guest emotion data into an AI and have the AI ​​perform emotion estimation.

[0079] The data collection unit analyzes the guest's past posting history and selects the optimal data collection method. For example, if the guest has posted many photos in the past, the data collection unit may prioritize encouraging photo posting. If the guest has posted many episodes in the past, the data collection unit may prioritize encouraging episode posting. The data collection unit can also select the most effective data collection method from the guest's past posting history and send notifications. This allows for the selection of the optimal data collection method based on the guest's past posting history. The posting history provides guidelines, for example, to clarify the type of posts and the methods of analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the guest's past posting history into AI and have the AI ​​select the optimal data collection method.

[0080] The collection unit filters posts based on the guest's areas of interest when collecting content. For example, if the guest is interested in travel, the collection unit will prioritize collecting travel-related photos and stories. If the guest is interested in cooking, the collection unit can prioritize collecting cooking-related photos and stories. The collection unit can also filter and collect highly relevant content based on the guest's areas of interest. This allows for the collection of highly relevant content based on the guest's areas of interest. Areas of interest can provide guidelines, such as clarifying survey results or past posts. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the guest's areas of interest into an AI and have the AI ​​perform the filtering of highly relevant content.

[0081] The collection unit estimates the guest's emotions and determines the priority of content to collect based on the estimated emotions. For example, if the guest is excited, the collection unit prioritizes collecting important content while their emotions are heightened. If the guest is relaxed, the collection unit can prioritize collecting natural posts. Also, if the guest is busy, the collection unit can postpone collecting less important content and prioritize collecting important content. This allows the collection unit to determine the priority of content to collect according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input guest emotion data into an AI and have the AI ​​determine the priority of content to collect.

[0082] The collection unit prioritizes collecting highly relevant content by considering the guest's geographical location information during content collection. For example, if the guest is in a specific region, the collection unit prioritizes collecting content related to that region. If the guest is traveling, the collection unit can prioritize collecting content related to their travel destination. The collection unit can also filter and collect highly relevant content based on the guest's geographical location information. This allows for the collection of highly relevant content based on the guest's geographical location information. Geographical location information provides guidelines for clarifying methods such as GPS data and location information analysis. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the guest's geographical location information into AI and have the AI ​​perform the collection of highly relevant content.

[0083] The collection unit analyzes the guest's social media activity and collects relevant content during content collection. For example, the collection unit collects photos and stories shared by the guest on social media. The collection unit can collect content related to topics of interest from the guest's social media activity. The collection unit can also analyze the guest's social media activity, filter, and collect highly relevant content. This allows for the collection of highly relevant content based on the guest's social media activity. Social media activity provides guidelines for clarifying, for example, the analysis of post content and activity frequency. Some or all of the above processing in the collection unit may be performed using AI, or not, for example. For example, the collection unit can input the guest's social media activity into AI and have the AI ​​perform the collection of relevant content.

[0084] The generation unit estimates the guest's emotions and adjusts the way the storyline is expressed based on the estimated emotions. For example, if the guest is moved, the generation unit generates a storyline that emphasizes emotional expressions. If the guest is having fun, the generation unit can generate a storyline that emphasizes a fun atmosphere. Also, if the guest is relaxed, the generation unit can generate a storyline with a calm atmosphere. This allows the way the storyline is expressed to be adjusted according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input guest emotion data into an AI and have the AI ​​adjust the way the storyline is expressed.

[0085] The generation unit adjusts the level of detail in the storyline based on the importance of the content during generation. For example, the generation unit can enrich the storyline by adding detailed explanations to important content, or simplify the storyline by adding concise explanations to less important content. The generation unit can also adjust the level of detail in the storyline according to the importance of the content to produce a well-balanced movie. This allows for adjustment of the level of detail in the storyline according to the importance of the content. Importance provides guidelines for clarifying content evaluation criteria and importance scoring methods, for example. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the importance of the content into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the storyline.

[0086] The generation unit applies different generation algorithms depending on the content category during generation. For example, the generation unit can apply an image processing algorithm to photographic content to generate a visually appealing storyline. For episodic content, it can apply a natural language processing algorithm to generate an emotionally rich storyline. Furthermore, the generation unit can apply a video processing algorithm to video content to generate a dynamic storyline. This allows the optimal generation algorithm to be applied according to the content category. Categories provide guidelines to clarify the definition of content types and categories, for example. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the content category into the AI ​​and have the AI ​​apply the optimal generation algorithm.

[0087] The generation unit estimates the guest's emotions and adjusts the length of the storyline based on the estimated emotions. For example, if the guest is moved, the generation unit can lengthen emotional scenes to enrich the storyline. If the guest is having fun, the generation unit can lengthen enjoyable scenes to make the storyline more enjoyable. Similarly, if the guest is relaxed, the generation unit can lengthen calming scenes to make the storyline more relaxing. This allows the length of the storyline to be adjusted according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, 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 processing in the generation unit may be performed using AI, or not. For example, the generation unit can input guest emotion data into an AI and have the AI ​​adjust the length of the storyline.

[0088] The generation unit determines the priority of storylines based on the submission timing of the content during generation. For example, the generation unit may prioritize incorporating content submitted early into the storyline. The generation unit may also prioritize incorporating content with upcoming submission dates into the storyline. Furthermore, the generation unit can determine the priority of storylines based on submission timing and generate a balanced movie. This allows for the prioritization of storylines based on the submission timing of the content. The submission timing provides guidelines, for example, to clarify how to record submission dates and criteria for evaluating submission timing. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the content submission timing into AI and have the AI ​​perform the determination of storyline prioritization.

[0089] The generation unit adjusts the order of storylines based on the relevance of the content during generation. For example, the generation unit can place highly relevant content consecutively to make the flow of the storyline smooth. The generation unit can also maintain the consistency of the storyline by postponing less relevant content. Furthermore, the generation unit can adjust the order of storylines based on the relevance of the content to generate a balanced movie. This allows for the adjustment of the order of storylines based on the relevance of the content. Relevance provides guidelines for clarifying, for example, the definition of content relevance and the method of scoring relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the relevance of the content into AI and have the AI ​​perform the adjustment of the storyline order.

[0090] The editing unit estimates the emotions of the bride and groom and adjusts the editing method based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the editing unit can make edits that emphasize emotional scenes. If the bride and groom are having fun, the editing unit can make edits that emphasize fun scenes. Also, if the bride and groom are relaxed, the editing unit can make edits that emphasize calm scenes. In this way, the editing method can be adjusted according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's emotion data into an AI and have the AI ​​perform the adjustment of the editing method.

[0091] The editing unit estimates the emotions of the bride and groom and adjusts the editing method based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the editing unit can make edits that emphasize emotional scenes. If the bride and groom are having fun, the editing unit can make edits that emphasize fun scenes. Also, if the bride and groom are relaxed, the editing unit can make edits that emphasize calm scenes. In this way, the editing method can be adjusted according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's emotion data into an AI and have the AI ​​perform the adjustment of the editing method.

[0092] The editing unit selects the optimal editing method by referring to the bride and groom's past editing history during editing. For example, the editing unit proposes the optimal editing method based on the editing content previously performed by the bride and groom. The editing unit can analyze and propose preferred editing styles from the bride and groom's past editing history. Furthermore, the editing unit can refer to the bride and groom's past editing history and propose ways to reduce the effort required for editing. This allows the optimal editing method to be selected based on the bride and groom's past editing history. The editing history provides guidelines to clarify past editing content and editing history recording methods, for example. Some or all of the above processes in the editing unit may be performed using AI, or not. For example, the editing unit can input the bride and groom's past editing history into AI and have the AI ​​select the optimal editing method.

[0093] The editing unit customizes the editing process based on the bride and groom's current needs. For example, if the bride and groom want to emphasize a particular scene, the editing unit will make an edit that emphasizes that scene. If the bride and groom want to add a particular effect, the editing unit can make an edit that adds that effect. The editing unit can also customize the editing process based on the bride and groom's current needs to provide the optimal movie. This allows the editing process to be customized according to the bride and groom's current needs. Needs can be provided, for example, through survey results or guidelines to clarify current requirements. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's current needs into AI and have the AI ​​perform the customization of the editing process.

[0094] The editing unit estimates the emotions of the bride and groom and determines the priority of edits based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the editing unit may prioritize editing emotional scenes. If the bride and groom are having fun, the editing unit may prioritize editing fun scenes. Also, if the bride and groom are relaxed, the editing unit may prioritize editing calm scenes. In this way, the priority of edits can be determined according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's emotion data into an AI and have the AI ​​perform the determination of the priority of edits.

[0095] The editing unit selects the optimal editing method when editing, taking into account the geographical location information of the bride and groom. For example, if the bride and groom are in a specific region, the editing unit will suggest an editing method related to that region. If the bride and groom are traveling, the editing unit can suggest an editing method related to their travel destination. Furthermore, the editing unit can select and suggest the optimal editing method based on the geographical location information of the bride and groom. This allows for the selection of the optimal editing method based on the geographical location information of the bride and groom. Geographical location information provides, for example, GPS data and guidelines to clarify the method of analyzing location information. Some or all of the above processing in the editing unit may be performed using, for example, AI, or not using AI. For example, the editing unit can input the geographical location information of the bride and groom into AI and have the AI ​​select the optimal editing method.

[0096] The editing unit analyzes the bride and groom's social media activity during editing and proposes editing methods. For example, the editing unit proposes editing methods based on photos and stories shared by the bride and groom on social media. The editing unit can analyze and propose preferred editing styles from the bride and groom's social media activity. The editing unit can also analyze the bride and groom's social media activity and propose highly relevant editing methods. This allows the editing unit to propose the most suitable editing methods based on the bride and groom's social media activity. Social media activity provides guidelines to clarify, for example, the analysis of post content and activity frequency. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the bride and groom's social media activity into AI and have the AI ​​execute the editing method proposal.

[0097] The management department estimates the guest's emotions and adjusts the content management method based on the estimated emotions. For example, if a guest is emotional, the management department will prioritize emotionally moving content. If a guest is having fun, the management department can prioritize fun content. Also, if a guest is relaxed, the management department can prioritize calming content. This allows the content management method to be adjusted according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input guest emotion data into an AI and have the AI ​​adjust the content management method.

[0098] The management department selects the optimal management method by referring to the guest's past posting history during management. For example, the management department proposes the optimal management method based on the content the guest has previously posted. The management department can analyze and propose preferred management styles from the guest's past posting history. Furthermore, the management department can refer to the guest's past posting history and propose ways to reduce the effort required for management. This allows the selection of the optimal management method based on the guest's past posting history. The posting history provides guidelines to clarify, for example, the type of posts and the methods of analysis. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the guest's past posting history into AI and have the AI ​​select the optimal management method.

[0099] The management department estimates the guest's emotions and determines the priority of content management based on the estimated emotions. For example, if the guest is emotional, the management department will prioritize managing emotionally moving content. If the guest is having fun, the management department may prioritize managing enjoyable content. Also, if the guest is relaxed, the management department may prioritize managing calming content. This allows the management department to determine the priority of content management according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input guest emotion data into an AI and have the AI ​​perform the determination of content management priorities.

[0100] The management department selects the optimal management method when managing guests, taking into account their geographical location information. For example, if a guest is in a specific region, the management department will prioritize managing content related to that region. If a guest is traveling, the management department can prioritize managing content related to their travel destination. Furthermore, the management department can select and propose the optimal management method based on the guest's geographical location information. This allows for the selection of the optimal management method based on the guest's geographical location information. Geographical location information provides guidelines to clarify methods for analyzing GPS data and location information. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the guest's geographical location information into AI and have the AI ​​select the optimal management method.

[0101] The verification unit estimates the emotions of the bride and groom and adjusts the movie review method based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the verification unit can provide a method that emphasizes emotional scenes during the review. If the bride and groom are having fun, the verification unit can provide a method that emphasizes fun scenes during the review. Furthermore, if the bride and groom are relaxed, the verification unit can provide a method that emphasizes calm scenes during the review. This allows the movie review method to be adjusted according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the bride and groom's emotion data into the AI ​​and have the AI ​​perform the adjustment of the movie review method.

[0102] The verification unit selects the optimal verification method by referring to the bride and groom's past verification history during the verification process. For example, the verification unit proposes the optimal verification method based on the verification content performed by the bride and groom in the past. The verification unit can analyze and propose preferred verification styles from the bride and groom's past verification history. The verification unit can also refer to the bride and groom's past verification history and propose ways to reduce the effort required for verification. This allows the system to select the optimal verification method based on the bride and groom's past verification history. The verification history provides guidelines to clarify past verification content and how the verification history is recorded. Some or all of the above processes in the verification unit may be performed using AI, for example, or not. For example, the verification unit can input the bride and groom's past verification history into AI and have the AI ​​select the optimal verification method.

[0103] The verification unit estimates the emotions of the bride and groom and determines the priority of movie review based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the verification unit may prioritize reviewing emotional scenes. If the bride and groom are having fun, the verification unit may prioritize reviewing fun scenes. Also, if the bride and groom are relaxed, the verification unit may prioritize reviewing calm scenes. In this way, the priority of movie review can be determined according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the bride and groom's emotion data into an AI and have the AI ​​perform the determination of the movie review priority.

[0104] The verification unit selects the optimal verification method during verification, taking into account the geographical location information of the bride and groom. For example, if the bride and groom are in a specific region, the verification unit will suggest a verification method related to that region. If the bride and groom are traveling, the verification unit can suggest a verification method related to their travel destination. Furthermore, the verification unit can select and suggest the optimal verification method based on the geographical location information of the bride and groom. This allows for the selection of the optimal verification method based on the geographical location information of the bride and groom. Geographical location information provides, for example, GPS data and guidelines to clarify the method of analyzing location information. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input the geographical location information of the bride and groom into AI and have the AI ​​select the optimal verification method.

[0105] The dialogue unit estimates the emotions of the bride and groom and adjusts the dialogue method based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the dialogue unit will engage in an emotional dialogue. If the bride and groom are having fun, the dialogue unit can engage in a fun dialogue. Also, if the bride and groom are relaxed, the dialogue unit can engage in a calm dialogue. In this way, the dialogue method can be adjusted according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the bride and groom's emotion data into an AI and have the AI ​​perform the adjustment of the dialogue method.

[0106] The dialogue unit selects the optimal dialogue method by referring to the bride and groom's past dialogue history during a conversation. For example, the dialogue unit proposes the optimal dialogue method based on the content of past conversations the bride and groom have had. The dialogue unit can analyze and propose preferred dialogue styles from the bride and groom's past dialogue history. The dialogue unit can also refer to the bride and groom's past dialogue history and propose ways to reduce the effort required for the conversation. This allows the optimal dialogue method to be selected based on the bride and groom's past dialogue history. The dialogue history provides guidelines to clarify, for example, the content of past conversations and how the dialogue history is recorded. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the bride and groom's past dialogue history into AI and have the AI ​​select the optimal dialogue method.

[0107] The dialogue unit estimates the emotions of the bride and groom and determines the priority of the dialogue based on the estimated emotions of the bride and groom. For example, if the bride and groom are moved, the dialogue unit will prioritize emotional dialogue. If the bride and groom are having fun, the dialogue unit may prioritize enjoyable dialogue. Also, if the bride and groom are relaxed, the dialogue unit may prioritize calm dialogue. In this way, the priority of the dialogue can be determined according to the emotions of the bride and groom. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the bride and groom's emotion data into an AI and have the AI ​​perform the determination of dialogue priorities.

[0108] The dialogue unit selects the optimal dialogue method during a conversation, taking into account the geographical location information of the bride and groom. For example, if the bride and groom are in a specific region, the dialogue unit will suggest a dialogue method related to that region. If the bride and groom are traveling, the dialogue unit can suggest a dialogue method related to their travel destination. Furthermore, the dialogue unit can select and suggest the optimal dialogue method based on the geographical location information of the bride and groom. This allows for the selection of the optimal dialogue method based on the geographical location information of the bride and groom. Geographical location information can include, for example, GPS data or guidelines to clarify the method of analyzing location information. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input the geographical location information of the bride and groom into the AI ​​and have the AI ​​select the optimal dialogue method.

[0109] The dialogue unit analyzes the bride and groom's social media activity during a dialogue and proposes a dialogue method. For example, the dialogue unit proposes a dialogue method based on photos and stories shared by the bride and groom on social media. The dialogue unit can analyze and propose a preferred dialogue style from the bride and groom's social media activity. Furthermore, the dialogue unit can analyze the bride and groom's social media activity and propose a highly relevant dialogue method. This allows the dialogue unit to propose the optimal dialogue method based on the bride and groom's social media activity. Social media activity provides guidelines to clarify, for example, the analysis of post content and activity frequency. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the bride and groom's social media activity into AI and have the AI ​​propose a dialogue method.

[0110] The dialogue unit, during a conversation, refers to the bride and groom's calendar information and makes suggestions based on their schedule. For example, the dialogue unit refers to the schedule registered in the bride and groom's calendar and adjusts the content of the conversation. The dialogue unit can suggest conversations related to specific events based on the bride and groom's calendar information. Furthermore, the dialogue unit can suggest the most suitable conversation method based on the schedule, based on the bride and groom's calendar information. This allows the dialogue unit to suggest the most suitable conversation method based on the bride and groom's calendar information. The calendar information provides guidelines to clarify, for example, how to record schedules and how to analyze calendar information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the bride and groom's calendar information into AI and have the AI ​​execute suggestions based on the schedule.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The content collection unit can estimate the guest's emotions and adjust the content collection method based on those estimates. For example, if the guest is emotional, it can prioritize collecting emotionally moving episodes and photos. If the guest is having fun, it can prioritize collecting content that captures enjoyable moments. Furthermore, if the guest is relaxed, it can adjust to collecting content with a natural atmosphere. This enables optimal content collection tailored to the guest's emotions.

[0113] The generation unit can summarize the content provided using generation AI and adjust the level of detail in the summary based on the importance of the content when constructing a storyline. For example, important episodes can be summarized in detail to ensure they occupy a significant position in the storyline. Conversely, less important content can be summarized concisely to ensure a smooth flow of the storyline. Furthermore, the generation unit can adjust the level of detail in the storyline according to the importance of the content to generate a well-balanced movie.

[0114] The editing function can estimate the emotions of the bride and groom and adjust the editing method based on those estimated emotions. For example, if the bride and groom are moved, the editing can emphasize emotional scenes. If the bride and groom are having fun, the editing can emphasize happy scenes. Furthermore, if the bride and groom are relaxed, the editing can emphasize calm scenes. This allows for optimal editing tailored to the emotions of the bride and groom.

[0115] The data collection unit can analyze a guest's past posting history and select the most suitable collection method. For example, if a guest has posted many photos in the past, it can prioritize encouraging photo posting. Similarly, if a guest has posted many episodes in the past, it can prioritize encouraging episode posting. Furthermore, the data collection unit can select the most effective collection method based on the guest's past posting history and send notifications. This enables the optimal collection method based on the guest's past posting history.

[0116] The generation unit summarizes the content provided using generation AI and constructs a storyline. It can estimate the guest's emotions and adjust the storyline's presentation based on these estimated emotions. For example, if a guest is moved, it can generate a storyline that emphasizes emotional expressions. If a guest is enjoying themselves, it can generate a storyline that emphasizes a fun atmosphere. Furthermore, if a guest is relaxed, it can generate a storyline with a calm atmosphere. This enables the creation of an optimal storyline presentation tailored to the guest's emotions.

[0117] The editing function can select the optimal editing method by referring to the bride and groom's past editing history. For example, it can propose the most suitable editing method based on the editing content the bride and groom have done in the past. It can also analyze and propose preferred editing styles from the bride and groom's past editing history. Furthermore, it can suggest ways to reduce the effort required for editing by referring to the bride and groom's past editing history. This makes it possible to determine the optimal editing method based on the bride and groom's past editing history.

[0118] The content collection unit can estimate the guest's emotions and prioritize the content to collect based on those estimates. For example, if the guest is excited, important content can be prioritized while their emotions are heightened. If the guest is relaxed, natural posts can be prioritized. Furthermore, if the guest is busy, less important content can be postponed, and important content can be prioritized. This enables optimal content collection tailored to the guest's emotions.

[0119] The generation unit can apply different generation algorithms depending on the content category during generation. For example, an image processing algorithm can be applied to photographic content to generate a visually appealing storyline. A natural language processing algorithm can be applied to episodic content to generate an emotionally rich storyline. Furthermore, a video processing algorithm can be applied to video content to generate a dynamic storyline. This makes it possible to apply the optimal generation algorithm according to the content category.

[0120] The editing function can estimate the emotions of the bride and groom and determine the priority of edits based on those estimated emotions. For example, if the bride and groom are moved, the editing of emotional scenes can be prioritized. If the bride and groom are having fun, the editing of fun scenes can be prioritized. Furthermore, if the bride and groom are relaxed, the editing of calm scenes can be prioritized. This makes it possible to prioritize edits optimally according to the emotions of the bride and groom.

[0121] The data collection unit can prioritize collecting highly relevant content by considering the guest's geographical location. For example, if a guest is in a specific region, it can prioritize collecting content related to that region. Similarly, if a guest is traveling, it can prioritize collecting content related to their travel destination. Furthermore, the data collection unit can filter and collect highly relevant content based on the guest's geographical location. This enables optimal content collection based on the guest's geographical location.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects content from guests using QR codes. For example, guests can access a dedicated portal by scanning a QR code printed on their invitation with their smartphone. The collection unit provides a function that allows guests to easily post photos and stories through the dedicated portal. The collection unit provides an interface for guests to upload photos they have taken and an input form for posting stories in text format. Furthermore, the collection unit has a management unit for managing the posted content, providing functions to organize the posted content by category and to search and filter it. Step 2: The generation unit uses generation AI to summarize the content collected by the collection unit and build a storyline. The generation unit analyzes the provided content, extracts important information, and summarizes it. Based on the summarized information, it automatically builds a storyline and automatically creates a movie in conjunction with a video editing tool. For example, based on the information summarized by the generation AI, the generation unit determines the order of scenes and adds appropriate effects and transitions. Step 3: The editing section provides a function for the bride and groom to edit the movie while interacting with the generating AI. The editing section provides an interface for the bride and groom to edit specific scenes in the movie while interacting with the generating AI. Based on the revision suggestions made by the generating AI, the bride and groom can review the revisions and make corrections as needed.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the collection unit, generation unit, modification unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit estimates the emotions of guests using the camera 42 and microphone 38B of the smart device 14 and collects content at the appropriate time. The generation unit is implemented by the identification processing unit 290 of the data processing device 12, which summarizes the collected content and constructs a storyline. The modification unit is implemented by the control unit 46A of the smart device 14, which modifies the movie while the bride and groom interact with the generation AI. The emotion estimation function is implemented by the identification processing unit 290 of the data processing device 12, which adjusts the timing of content collection based on the emotions of the guests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, generation unit, modification unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit estimates the emotions of guests using the camera 42 and microphone 238 of the smart glasses 214 and collects content at the appropriate time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which summarizes the collected content and constructs a storyline. The modification unit is implemented by the control unit 46A of the smart glasses 214, which modifies the movie while the bride and groom interact with the generation AI. The emotion estimation function is implemented by the identification processing unit 290 of the data processing unit 12, which adjusts the timing of content collection based on the emotions of the guests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, generation unit, modification unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit estimates the emotions of guests using the camera 42 and microphone 238 of the headset terminal 314 and collects content at the appropriate time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which summarizes the collected content and constructs a storyline. The modification unit is implemented by the control unit 46A of the headset terminal 314, which modifies the movie while the bride and groom interact with the generation AI. The emotion estimation function is implemented by the identification processing unit 290 of the data processing unit 12, which adjusts the timing of content collection based on the emotions of the guests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, generation unit, modification unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to estimate the emotions of the guests and collect content at the appropriate time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which summarizes the collected content and constructs a storyline. The modification unit is implemented by the control unit 46A of the robot 414, which modifies the movie while the bride and groom interact with the generating AI. The emotion estimation function is implemented by the identification processing unit 290 of the data processing unit 12, which adjusts the timing of content collection based on the emotions of the guests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A collection unit that collects content from guests using 2D codes, A generation unit that summarizes the content collected by the collection unit and constructs a storyline, The system includes a correction unit that allows the bride and groom to review the movie generated by the generation unit and make corrections as needed. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system provides guests with the ability to access a dedicated portal and post photos and stories. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Using generative AI, the system summarizes the provided content, constructs a storyline, and automatically creates a movie. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned modification section is, The service provides a feature that allows the bride and groom to edit their video while interacting with the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It has a management department that manages content posted by guests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is It includes a verification unit to check the movie created by the generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned modification section is, It features a dialogue unit that interacts with the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the guest's emotions and adjust the timing of content collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the guest's past posting history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting content, filter posts based on the guest's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate the guest's emotions and prioritize the content to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting content, the system prioritizes collecting highly relevant content by considering the guest's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting content, we analyze the guest's social media activity and collect relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the guests' emotions and adjusts how the storyline is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, adjust the level of detail in the storyline based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, different generation algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Estimate the guests' emotions and adjust the length of the storyline based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, prioritize storylines based on when the content will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the order of storylines is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, The system estimates the emotions of the bride and groom and adjusts the modification method based on the estimated emotions of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modification section is, The system estimates the emotions of the bride and groom and adjusts the modification method based on the estimated emotions of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modification section is, When making revisions, the most suitable method of revision is selected by referring to the bride and groom's past revision history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, During the revision process, the methods of revision will be customized based on the current needs of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modification section is, The system estimates the emotions of the bride and groom and determines the priority of modifications based on the estimated emotions of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned modification section is, When making corrections, the most suitable correction method will be selected, taking into account the geographical location information of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned modification section is, During the revision process, we will analyze the bride and groom's social media activity and propose ways to make corrections. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, We estimate guest sentiment and adjust content management methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, During management, the system will refer to the guest's past posting history to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, Estimate guest sentiment and prioritize content management based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During management, the optimal management method is selected considering the guest's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is The system estimates the emotions of the bride and groom and adjusts the video review method based on the estimated emotions of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is During the verification process, the most suitable verification method will be selected by referring to the bride and groom's past verification history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned verification unit is The system estimates the emotions of the bride and groom and determines the priority of video review based on the estimated emotions of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned verification unit is During the verification process, the most suitable verification method will be selected, taking into account the geographical location information of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned dialogue unit, The system estimates the emotions of the bride and groom and adjusts the dialogue method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned dialogue unit, During the conversation, the optimal method of communication is selected by referring to the bride and groom's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned dialogue unit, The system estimates the emotions of the bride and groom and determines the priority of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned dialogue unit, During the conversation, the most suitable method of communication will be selected, taking into account the geographical location of the bride and groom. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned dialogue unit, During the conversation, we analyze the bride and groom's social media activity and propose ways to communicate. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned dialogue unit, During the conversation, we refer to the bride and groom's calendar information and make suggestions based on their schedules. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects content from guests using 2D codes, A generation unit that summarizes the content collected by the collection unit and constructs a storyline, The system includes a correction unit that allows the bride and groom to review the movie generated by the generation unit and make corrections as needed. A system characterized by the following features.

2. The aforementioned collection unit is The system provides guests with the ability to access a dedicated portal and post photos and stories. The system according to feature 1.

3. The generating unit is This system uses generative AI to summarize provided content, build a storyline, and automatically create a movie. The system according to feature 1.

4. The aforementioned modification section is, The service provides a feature that allows the bride and groom to edit the video while interacting with the generated AI. The system according to feature 1.

5. The aforementioned collection unit is It has a management department that manages content posted by guests. The system according to feature 1.

6. The generating unit is It includes a verification unit to check the movie created by the generation AI. The system according to feature 1.

7. The aforementioned modification section is, It features a dialogue unit that interacts with the generating AI. The system according to feature 1.

8. The aforementioned collection unit is We estimate the guest's emotions and adjust the timing of content collection based on those estimated emotions. The system according to feature 1.

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