system

Abstract

The system according to this embodiment aims to create emotionally moving custom movies based on photos and videos provided by the user. [Solution] The system according to the embodiment comprises a generation unit, a story construction unit, an adjustment unit, a language support unit, a security unit, and an output unit. The generation unit creates a custom movie based on photos and videos provided by the user. The story construction unit constructs an emotional story for the movie generated by the generation unit. The adjustment unit adjusts music and effects for the story constructed by the story construction unit. The language support unit makes the movie adjusted by the adjustment unit multilingual. The security unit ensures the privacy and security of the movie supported by the language support unit. The output unit outputs the movie secured by the security unit in a flexible format.

Description

Technical Field

[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, including steps of 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 as a 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 conventional technology, there is a problem that it is difficult to organize a huge number of photos and videos and create a moving custom movie.

[0005] The system according to the embodiment aims to create a moving custom movie based on photos and videos provided by a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a story construction unit, an adjustment unit, a language support unit, a security unit, and an output unit. The generation unit creates a custom movie based on photos and videos provided by the user. The story construction unit constructs an emotionally engaging story for the movie generated by the generation unit. The adjustment unit adjusts music and effects for the story constructed by the story construction unit. The language support unit makes the movie adjusted by the adjustment unit multilingual. The security unit ensures the privacy and security of the movie handled by the language support unit. The output unit outputs the movie secured by the security unit in a flexible format. [Effects of the Invention]

[0007] The system according to this embodiment can create a moving custom movie based on photos and videos provided by the user. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F manages communication between multiple 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 custom movie production system according to an embodiment of the present invention is a system that efficiently stores and organizes the vast amount of photos and videos taken with a smartphone on a daily basis, allowing users to quickly relive memories when needed. This custom movie production system uses a generating AI to create a custom movie based on photos and videos provided by the user. For example, it can accommodate various events such as self-introductions, weddings, and funerals. The generating AI constructs an emotionally moving and compelling story, combining photos and videos to express a consistent message. The story is created based on the user's wishes and specified theme. Next, the custom movie production system also selects music and adds effects, making adjustments to match the emotions and atmosphere. This results in a movie that leaves a strong impression on the viewer. Furthermore, the custom movie production system supports multiple languages, enabling production in accordance with different cultures and customs. It features international support, including language subtitles and narration. The custom movie production system also prioritizes user privacy and data security, and the information provided is handled with care. Users can preview the movie during production and request revisions as needed. The created movies can be output in various formats according to the user's wishes, allowing for flexible use such as online sharing or presentations at specific events. This system efficiently stores and organizes the vast number of photos and videos taken daily with smartphones, allowing users to quickly relive memories when needed. For example, when creating a wedding movie, the generating AI constructs a moving story based on photos and videos provided by the user, adding music and effects to express a consistent message. This results in a movie that leaves a strong impression on viewers. Furthermore, users can preview the movie during production and request revisions as needed. For example, if a user wants to add specific photos or videos, the generating AI can reconstruct the movie accordingly. This results in the creation of a custom movie tailored to the user's wishes.Thus, the present invention provides a custom movie production service that efficiently stores and organizes the vast amount of photos and videos taken with a smartphone every day, allowing users to quickly relive their memories when needed. This enables the custom movie production system to efficiently organize the user's photos and videos and create moving movies.

[0029] The custom movie production system according to this embodiment comprises a generation unit, a story construction unit, an adjustment unit, a language support unit, a security unit, and an output unit. The generation unit produces a custom movie based on photos and videos provided by the user. For example, the generation unit analyzes the photos and videos provided by the user, selects the optimal scenes, and generates a movie. The generation unit can also use generation AI to analyze the content of photos and videos and generate a movie that follows the flow of a story. Furthermore, the generation unit can produce a custom movie based on the user's wishes or a specified theme. For example, the generation unit can construct a moving story and generate a movie based on wedding photos and videos. The story construction unit constructs a moving story for the movie generated by the generation unit. For example, the story construction unit can use generation AI to analyze the content of photos and videos and construct a moving and compelling story. Furthermore, the story construction unit can construct a story based on the user's wishes or a specified theme. Furthermore, the story construction unit can combine photos and videos to express a consistent message. For example, the story construction unit can construct a moving story and generate a movie based on self-introduction photos and videos. The adjustment unit adjusts music and effects to the story constructed by the story construction unit. For example, the adjustment unit uses generative AI to select the most suitable music for the story and adds effects. The adjustment unit can also make adjustments to match emotions and atmosphere. Furthermore, the adjustment unit can adjust music and effects to make a strong impression on the viewer. For example, the adjustment unit adds emotional music and adjusts effects to a wedding video. The language support unit makes the video adjusted by the adjustment unit multilingual. For example, the language support unit uses generative AI to add subtitles and narration to the video. Furthermore, the language support unit can produce content that conforms to different cultures and customs. In addition, the language support unit can support multiple languages ​​to cater to an international audience. For example, the language support unit adds multilingual subtitles to a wedding video to cater to an international audience.The security unit ensures the privacy and security of movies handled by the language support unit. The security unit, for example, uses generative AI to encrypt the provided information and ensure data security. The security unit can also implement access control to protect user privacy. Furthermore, the security unit can handle the provided information with care. For example, the security unit encrypts the data of a wedding movie to protect its privacy. The output unit outputs the movies secured by the security unit in a flexible format. For example, the output unit uses generative AI to convert the movie into a format suitable for online sharing. The output unit can also output in a format suitable for presentations at specific events. Furthermore, the output unit can output movies in various formats according to the user's wishes. For example, the output unit can output a wedding movie in a high-resolution video format for online sharing. As a result, the custom movie production system according to this embodiment can efficiently organize the user's photos and videos and produce moving movies.

[0030] The generation unit creates custom movies based on photos and videos provided by the user. For example, the generation unit analyzes the photos and videos provided by the user, selects the optimal scenes, and generates a movie. Specifically, the generation unit uses image recognition technology to analyze the content of photos and videos in detail and automatically extracts important scenes and emotional moments. Furthermore, the generation unit can also use generation AI to analyze the content of photos and videos and generate a movie that follows a storyline. For example, the generation AI utilizes deep learning technology to identify people, backgrounds, and types of events in photos and videos and constructs a story based on that. The generation unit can also create custom movies based on the user's wishes or specified themes. For example, the generation unit can build an emotional story and generate a movie based on wedding photos and videos. To emphasize themes or specific scenes specified by the user, the generation unit uses AI to select and edit scenes to generate the optimal movie. Furthermore, the generation unit can perform image processing such as color correction and noise reduction on the materials provided by the user to produce high-quality movies. This allows the generation unit to efficiently organize the user's photos and videos and create an emotionally impactful movie.

[0031] The story construction unit builds a moving story for the movie generated by the generation unit. For example, the story construction unit uses generational AI to analyze the content of photos and videos and construct a moving and compelling story. Specifically, the story construction unit uses natural language processing technology to analyze the facial expressions, actions, and background context of people in photos and videos, and creates a story based on this. The story construction unit can also build stories based on user requests or specified themes. For example, if a user requests a wedding movie, the story construction unit will use wedding photos and videos to create a moving story about the bride and groom from their first meeting to their marriage. Furthermore, the story construction unit can combine photos and videos to express a consistent message. For example, the story construction unit can build a moving story and generate a movie based on self-introduction photos and videos. Using AI, the story construction unit can adjust the order and timing of photos and videos to create a story that leaves a strong impression on the viewer. This allows the story construction unit to produce moving movies that meet the user's wishes.

[0032] The adjustment unit adjusts music and effects to the story constructed by the story building unit. For example, the adjustment unit uses generative AI to select the most suitable music for the story and add effects. Specifically, the adjustment unit adjusts the tempo and atmosphere of the music to match the flow of the story, maximizing the effect of moving the viewer. The adjustment unit can also make adjustments to match emotions and atmosphere. For example, it might select a quiet piano piece for an emotional scene and upbeat music for an exciting scene. Furthermore, the adjustment unit can adjust music and effects to make a strong impression on the viewer. For example, it could add emotional music and adjust effects to a wedding video. Effects include fade-in, fade-out, and transition effects, and by appropriately placing these, the overall flow of the video can be made smoother, leaving a strong impression on the viewer. The adjustment unit can use AI to automate the selection and placement of music and effects, enabling the efficient production of high-quality videos. In this way, the adjustment unit can play a crucial role in creating videos that move viewers.

[0033] The language support unit makes movies adjusted by the adjustment unit multilingual. For example, the language support unit adds subtitles and narration to movies using generative AI. Specifically, the language support unit converts the movie's audio into text using speech recognition technology and generates multilingual subtitles based on that. The language support unit can also produce content that is tailored to different cultures and customs. For example, in a wedding movie, it can add subtitles and narration that take into account the cultural background and customs of viewers from different cultural backgrounds. Furthermore, the language support unit can support multiple languages ​​to cater to international audiences. For example, the language support unit can add multilingual subtitles to a wedding movie to cater to an international audience. The language support unit can use AI to improve translation accuracy and achieve natural expression. As a result, the language support unit can make movies multilingual and provide high-quality content to international audiences.

[0034] The Security Department ensures the privacy and security of movies handled by the Language Support Department. For example, the Security Department uses generative AI to encrypt the provided information and ensure data security. Specifically, the Security Department uses encryption technology to protect movie data and user personal information. The Security Department can also implement access control to protect user privacy. For example, it can implement passwords and authentication systems to ensure that only specific users can access movies. Furthermore, the Security Department can handle the provided information with care. For example, the Security Department can encrypt wedding movie data to protect privacy. The Security Department can use AI to detect data anomalies and monitor for unauthorized access, thereby enhancing security. This allows the Security Department to ensure user privacy and data security, providing an environment where users can use custom movies with peace of mind.

[0035] The output unit outputs movies secured by the security unit in flexible formats. For example, the output unit uses generation AI to convert movies into formats suitable for online sharing. Specifically, the output unit converts movies into high-resolution video formats or streaming formats, making them easily shareable by users. The output unit can also output in formats suitable for presentations at specific events. For example, it can convert wedding movies to the appropriate resolution and format for large-screen screenings. Furthermore, the output unit can output movies in various formats according to user preferences. For example, it can output wedding movies in high-resolution video formats for online sharing. The output unit uses AI to automatically select the optimal output format, enabling efficient movie output. This allows the output unit to provide flexible output tailored to user needs and promote the use of custom movies.

[0036] The generation unit can analyze the user's past photo and video usage history and select the optimal generation method. For example, the generation unit can analyze the style of photos and videos the user has frequently used in the past, and the generation AI can generate a custom movie based on that. The generation unit can also analyze the patterns of photos and videos the user has used in specific events, and the generation AI can select the optimal generation method based on that. Furthermore, the generation unit can analyze the evaluation of movies the user has created in the past, and the generation AI can select the optimal generation method based on that. In this way, by analyzing the user's past usage history, the optimal generation method can be selected and a more satisfying custom movie can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past usage history data into the generation AI and have the generation AI select the optimal generation method.

[0037] The generation unit can filter custom movies based on the user's current lifestyle and areas of interest. For example, the generation unit can prioritize the use of relevant photos and videos based on the user's current lifestyle. The generation unit can also filter and use relevant photos and videos based on the user's areas of interest. Furthermore, the generation unit can have the generation AI generate the most suitable custom movie based on the user's current lifestyle and areas of interest. This allows for the generation of more relevant custom movies by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's current lifestyle and areas of interest into the generation AI and have the generation AI perform the filtering.

[0038] The generation unit can prioritize the use of highly relevant photos and videos when generating a custom movie, taking into account the user's geographical location. For example, if the user is in a specific region, the generation AI will prioritize using photos and videos related to that region. Similarly, if the user is traveling, the generation AI can prioritize using photos and videos related to the travel destination. Furthermore, if the user is attending a specific event, the generation AI can prioritize using photos and videos related to that event. This allows for the generation of a custom movie using more relevant photos and videos by considering the user's geographical location. 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 user's geographical location data into the generation AI and have the AI ​​select highly relevant photos and videos.

[0039] The generation unit can analyze the user's social media activity and use relevant photos and videos when generating a custom movie. For example, the generation unit can analyze photos and videos shared by the user on social media, and the generation AI can generate a custom movie based on that analysis. The generation unit can also analyze the content of the user's social media posts, and the generation AI can use relevant photos and videos. Furthermore, the generation unit can analyze the reactions of the user's social media followers and friends, and the generation AI can generate a custom movie based on that analysis. This allows for the generation of custom movies using more relevant photos and videos by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's social media activity data into the generation AI and have the generation AI select relevant photos and videos.

[0040] The story construction unit can adjust the level of detail in a story based on the importance of photos and videos during the story construction process. For example, the story construction unit can prioritize the use of important photos and videos, and the generating AI can construct a detailed story. Alternatively, the story construction unit can simplify less important photos and videos, and the generating AI can construct a shorter story. Furthermore, the story construction unit can have the generating AI adjust the level of detail in the story according to its importance to create a balanced story. This allows for the creation of a balanced story by adjusting the level of detail in the story based on the importance of photos and videos. Some or all of the above processes in the story construction unit may be performed using AI, or not. For example, the story construction unit can input importance data of photos and videos into the generating AI and have the generating AI perform the adjustment of the level of detail in the story.

[0041] The story building unit can apply different story algorithms depending on the event category when building a story. For example, in the case of a wedding, the generating AI can apply an emotional story algorithm. In the case of a funeral, the generating AI can also apply a solemn story algorithm. Furthermore, in the case of a self-introduction, the generating AI can also apply a friendly story algorithm. By applying different story algorithms depending on the event category, a more appropriate story can be built. Some or all of the above processing in the story building unit may be performed using AI, for example, or without AI. For example, the story building unit can input event category data into the generating AI and have the generating AI execute the application of story algorithms.

[0042] The story building unit can prioritize stories based on when photos and videos were taken. For example, the story building unit can prioritize recently taken photos and videos, and the generating AI can build a story. The story building unit can also prioritize photos and videos of specific events, and the generating AI can build a story. Furthermore, the story building unit can have the generating AI prioritize stories according to when they were taken, creating a balanced story. This allows for the creation of a balanced story by prioritizing stories based on when photos and videos were taken. Some or all of the above processes in the story building unit may be performed using AI, or not. For example, the story building unit can input photo and video shooting date data into the generating AI and have the generating AI determine the story priorities.

[0043] The story construction unit can adjust the order of stories based on the relevance of photos and videos during story construction. For example, the story construction unit can use highly relevant photos and videos in sequence, and the generating AI can construct the story. The story construction unit can also simplify less relevant photos and videos, and the generating AI can adjust the order of the stories. Furthermore, the story construction unit can have the generating AI adjust the order of stories according to relevance to construct a balanced story. This allows for the construction of a balanced story by adjusting the order of stories based on the relevance of photos and videos. Some or all of the above processing in the story construction unit may be performed using AI, for example, or without AI. For example, the story construction unit can input relevance data of photos and videos into the generating AI and have the generating AI perform the adjustment of the story order.

[0044] The adjustment unit can select the optimal adjustment method based on the content of the photos and videos when adjusting music and effects. For example, the adjustment unit can use the generating AI to select the most suitable music based on the content of the photos and videos. The adjustment unit can also use the generating AI to select the most suitable effects based on the content of the photos and videos. Furthermore, the adjustment unit can use the generating AI to select the adjustment method for music and effects based on the content of the photos and videos. This allows for the generation of movies with more appropriate music and effects by selecting the optimal adjustment method based on the content of the photos and videos. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input photo and video content data into the generating AI and have the generating AI select the optimal adjustment method.

[0045] The adjustment unit can apply different adjustment algorithms depending on the event category when adjusting music and effects. For example, in the case of a wedding, the generation AI can select emotionally moving music and effects. In the case of a funeral, the generation AI can select solemn music and effects. Furthermore, in the case of a self-introduction, the generation AI can select friendly music and effects. By applying different adjustment algorithms depending on the event category, it is possible to generate a movie with more appropriate music and effects. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input event category data into the generation AI and have the generation AI execute the application of the adjustment algorithm.

[0046] The adjustment unit can determine the priority of adjustments based on the shooting dates of photos and videos when adjusting music and effects. For example, the adjustment unit can prioritize the use of recently taken photos and videos, and the generating AI can adjust the music and effects accordingly. Alternatively, the adjustment unit can prioritize the use of photos and videos from specific events, and the generating AI can adjust the music and effects accordingly. Furthermore, the adjustment unit can also determine the priority of music and effect adjustments based on the shooting dates, allowing the generating AI to create more appropriate movies. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input photo and video shooting date data into the generating AI and have the generating AI determine the adjustment priority.

[0047] The adjustment unit can adjust the order of adjustments based on the relevance of photos and videos when adjusting music and effects. For example, the adjustment unit can use highly relevant photos and videos in sequence, and the generating AI can adjust the music and effects. The adjustment unit can also simplify less relevant photos and videos, and the generating AI can adjust the order of music and effects. Furthermore, the adjustment unit can have the generating AI adjust the order of music and effects according to relevance to construct a balanced movie. This allows for the generation of a more balanced movie by adjusting the order of adjustments based on the relevance of photos and videos. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevance data of photos and videos into the generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0048] The language support unit can analyze the user's language history and select the optimal language support method during language support. For example, the language support unit can use a generating AI to select the optimal language support method based on the languages ​​the user has used in the past. The language support unit can also analyze the user's language history and have the generating AI select the optimal language for subtitles and narration. Furthermore, the language support unit can generate a movie based on the user's language history, with the generating AI selecting the optimal language support method. This allows for the selection of the optimal language support method and the generation of a more appropriate movie by analyzing the user's language history. Some or all of the above-described processes in the language support unit may be performed using AI, for example, or without AI. For example, the language support unit can input the user's language history data into the generating AI and have the generating AI select the optimal language support method.

[0049] The language handling unit can apply different language handling algorithms depending on the event category during language handling. For example, in the case of a wedding, the generating AI can apply an emotional language handling algorithm. Similarly, in the case of a funeral, the generating AI can apply a solemn language handling algorithm. Furthermore, in the case of a self-introduction, the generating AI can apply a friendly language handling algorithm. This allows for the generation of more appropriate movies by applying different language handling algorithms depending on the event category. Some or all of the above processing in the language handling unit may be performed using AI, for example, or without AI. For example, the language handling unit can input event category data into the generating AI and have the generating AI perform the application of the language handling algorithm.

[0050] The language support unit can select the optimal language support method by considering the user's geographical location information during language support. For example, if the user is in a specific region, the generating AI can select subtitles and narration corresponding to the language of that region. Furthermore, if the user is traveling, the generating AI can select subtitles and narration corresponding to the language of the travel destination. In addition, the language support unit can generate a movie based on the user's geographical location information, with the generating AI selecting the optimal language support method. This allows for the selection of a more appropriate language support method and the generation of a movie by considering the user's geographical location information. Some or all of the above-described processes in the language support unit may be performed using AI, or not. For example, the language support unit can input the user's geographical location data into the generating AI and have the generating AI select the optimal language support method.

[0051] The language support unit can analyze the user's social media activity and select the optimal language support method during language support. For example, the language support unit can analyze the language the user uses on social media, and the generating AI can select the optimal language support method based on that analysis. The language support unit can also analyze the content of the user's social media posts, and the generating AI can select the appropriate language support method. Furthermore, the language support unit can analyze the languages ​​of the user's social media followers and friends, and the generating AI can select the optimal language support method based on that analysis. This allows for the selection of a more appropriate language support method and the generation of a movie by analyzing the user's social media activity. Some or all of the above processing in the language support unit may be performed using AI, for example, or without AI. For example, the language support unit can input the user's social media activity data into the generating AI and have the generating AI select the optimal language support method.

[0052] The security unit can analyze a user's past security history to select the optimal security method when ensuring data security. For example, the security unit can use a generating AI to select the optimal security method based on the security settings the user has used in the past. The security unit can also analyze the user's security history and have the generating AI select the optimal data security method. Furthermore, the security unit can use a generating AI to select the optimal security method and protect the data based on the user's past security history. In this way, by analyzing the user's past security history, the optimal security method can be selected and the data protected. Some or all of the above processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the user's past security history data into a generating AI and have the generating AI select the optimal security method.

[0053] The security department can apply different security algorithms depending on the event category when ensuring data security. For example, in the case of a wedding, the generating AI can apply a security algorithm suitable for an emotional event. Similarly, in the case of a funeral, the generating AI can apply a security algorithm suitable for a solemn event. Furthermore, in the case of a self-introduction, the generating AI can apply a security algorithm suitable for a friendly event. By applying different security algorithms depending on the event category, more appropriate security can be provided. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input event category data into the generating AI and have the generating AI perform the application of security algorithms.

[0054] The security unit can select the optimal security method when ensuring data security, taking into account the user's geographical location information. For example, if the user is in a specific region, the generating AI can select a security method appropriate for that region. Furthermore, if the user is traveling, the generating AI can select a security method appropriate for the travel destination. In addition, the security unit can use the generating AI to select the optimal security method and protect the data based on the user's geographical location information. This allows for the selection of a more appropriate security method and data protection by considering the user's geographical location information. Some or all of the above-described processes in the security unit may be performed using AI, or not. For example, the security unit can input the user's geographical location data into the generating AI and have the generating AI select the optimal security method.

[0055] The security department can analyze a user's social media activity to select the optimal security method when ensuring data security. For example, the security department can analyze information shared by a user on social media, and a generative AI can select the optimal security method based on that analysis. The security department can also analyze the content of a user's social media posts, and the generative AI can select relevant security methods. Furthermore, the security department can analyze the reactions of a user's social media followers and friends, and the generative AI can select the optimal security method based on that analysis. This allows for the selection of more appropriate security methods and data protection by analyzing the user's social media activity. Some or all of the above processes in the security department may be performed using AI, for example, or not. For example, the security department can input user social media activity data into a generative AI and have the generative AI select the optimal security method.

[0056] The output unit can select the optimal output method by analyzing the user's past output history when selecting an output format. For example, the output unit can use a generating AI to select the optimal output method based on the output formats the user has used in the past. The output unit can also analyze the user's output history and have the generating AI select the optimal output format. Furthermore, the output unit can generate a movie by having the generating AI select the optimal output method based on the user's past output history. This allows for the selection of the optimal output method and the generation of a more appropriate movie by analyzing the user's past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into the generating AI and have the generating AI select the optimal output method.

[0057] The output unit can apply different output algorithms depending on the event category when selecting the output format. For example, in the case of a wedding, the output unit's generating AI can apply an emotional output algorithm. Similarly, in the case of a funeral, the output unit can apply a solemn output algorithm. Furthermore, in the case of a self-introduction, the output unit can apply a friendly output algorithm. This allows for the generation of more appropriate movies by applying different output algorithms depending on the event category. Some or all of the above processing in the output unit may be performed using AI, or without AI. For example, the output unit can input event category data into the generating AI and have the generating AI apply the output algorithm.

[0058] The output unit can select the optimal output method by considering the user's geographical location information when selecting the output format. For example, if the user is in a specific region, the generation AI can select an output format suitable for that region. Furthermore, if the user is traveling, the generation AI can select an output format suitable for the travel destination. In addition, the output unit can generate a movie based on the user's geographical location information, with the generation AI selecting the optimal output method. This allows for the selection of a more appropriate output method and the generation of a movie by considering the user's geographical location information. Some or all of the above-described processes in the output unit may be performed using AI, or without AI. For example, the output unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal output method.

[0059] The output unit can analyze the user's social media activity to select the optimal output method when selecting the output format. For example, the output unit can consider whether the user will share the content on social media, and the generating AI can select the optimal output format. The output unit can also analyze the content of the user's social media posts, and the generating AI can select a relevant output format. Furthermore, the output unit can analyze the reactions of the user's social media followers and friends, and the generating AI can select the optimal output method based on that. In this way, by analyzing the user's social media activity, a more appropriate output method can be selected and the movie can be generated. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into the generating AI and have the generating AI select the optimal output method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The generation unit can analyze the user's past photo and video usage history and select the optimal generation method. For example, it can analyze the style of photos and videos the user has frequently used in the past, and the generation AI can generate a custom movie based on that. It can also analyze the patterns of photos and videos the user has used for specific events, and the generation AI can select the optimal generation method based on that. Furthermore, it can analyze the ratings of movies the user has created in the past, and the generation AI can select the optimal generation method based on that. In this way, by analyzing the user's past usage history, the optimal generation method can be selected, and a more satisfying custom movie can be generated.

[0062] The generation unit can filter custom movies based on the user's current lifestyle and areas of interest. For example, the generation AI can prioritize using relevant photos and videos based on the user's current lifestyle. It can also filter and use relevant photos and videos based on the user's areas of interest. Furthermore, the generation AI can generate the most relevant custom movie based on the user's current lifestyle and areas of interest. This allows for the generation of more relevant custom movies by filtering based on the user's current lifestyle and areas of interest.

[0063] The generation unit can prioritize the use of highly relevant photos and videos when generating custom movies, taking into account the user's geographical location. For example, if the user is in a specific region, the generation AI will prioritize using photos and videos related to that region. Similarly, if the user is traveling, the generation AI can prioritize using photos and videos related to their travel destination. Furthermore, if the user is attending a specific event, the generation AI can prioritize using photos and videos related to that event. This allows for the generation of custom movies using more relevant photos and videos by considering the user's geographical location.

[0064] The generation unit can analyze the user's social media activity and use relevant photos and videos when generating custom movies. For example, it can analyze photos and videos shared by the user on social media and generate a custom movie based on that analysis. It can also analyze the content of the user's social media posts and use relevant photos and videos. Furthermore, it can analyze the reactions of the user's social media followers and friends and generate a custom movie based on that analysis. This allows for the generation of custom movies using more relevant photos and videos by analyzing the user's social media activity.

[0065] The story building section can adjust the level of detail in a story based on the importance of photos and videos used during the story creation process. For example, it can prioritize the use of important photos and videos, allowing the generating AI to construct a detailed story. It can also simplify less important photos and videos, allowing the generating AI to construct a shorter story. Furthermore, the generating AI can adjust the level of detail in a story according to its importance, creating a well-balanced story. This allows for the creation of a balanced story by adjusting the level of detail in a story based on the importance of photos and videos.

[0066] The story generation unit can apply different story algorithms depending on the event category during story creation. For example, in the case of a wedding, the generating AI can apply an emotional story algorithm. In the case of a funeral, the generating AI can apply a solemn story algorithm. Furthermore, in the case of a self-introduction, the generating AI can apply a friendly story algorithm. By applying different story algorithms depending on the event category, a more appropriate story can be constructed.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The generation unit creates a custom movie based on photos and videos provided by the user. For example, the generation unit analyzes the photos and videos provided by the user, selects the optimal scenes, and generates the movie. The generation unit can also use generation AI to analyze the content of photos and videos and generate a movie that follows a storyline. Furthermore, the generation unit can create a custom movie based on the user's wishes or specified theme. Step 2: The story building unit constructs an emotionally compelling story for the movie generated by the generation unit. For example, the story building unit uses generation AI to analyze the content of photos and videos and construct an emotionally moving and persuasive story. The story building unit can also construct a story based on the user's wishes or specified theme. Furthermore, the story building unit can combine photos and videos to express a consistent message. Step 3: The adjustment unit adjusts the music and effects to the story constructed by the story building unit. For example, the adjustment unit uses generative AI to select the most suitable music for the story and add effects. The adjustment unit can also make adjustments to match emotions and atmosphere. Furthermore, the adjustment unit can adjust the music and effects to make a strong impression on the viewer. Step 4: The language support unit makes the movie, which has been adjusted by the adjustment unit, multilingual. The language support unit adds subtitles and narration to the movie, for example, using generative AI. The language support unit can also produce content that is appropriate for different cultures and customs. Furthermore, the language support unit can support multiple languages ​​to accommodate international audiences. Step 5: The security department ensures the privacy and security of the movies handled by the language support department. The security department ensures data security by, for example, encrypting the provided information using generative AI. The security department can also implement access control to protect user privacy. Furthermore, the security department can handle the provided information with care. Step 6: The output unit outputs the movie secured by the security unit in a flexible format. For example, the output unit uses generation AI to convert the movie into a format suitable for online sharing. The output unit can also output in a format suitable for presentations at specific events. Furthermore, the output unit can output the movie in various formats according to the user's preferences.

[0069] (Example of form 2) The custom movie production system according to an embodiment of the present invention is a system that efficiently stores and organizes the vast amount of photos and videos taken with a smartphone on a daily basis, allowing users to quickly relive memories when needed. This custom movie production system uses a generating AI to create a custom movie based on photos and videos provided by the user. For example, it can accommodate various events such as self-introductions, weddings, and funerals. The generating AI constructs an emotionally moving and compelling story, combining photos and videos to express a consistent message. The story is created based on the user's wishes and specified theme. Next, the custom movie production system also selects music and adds effects, making adjustments to match the emotions and atmosphere. This results in a movie that leaves a strong impression on the viewer. Furthermore, the custom movie production system supports multiple languages, enabling production in accordance with different cultures and customs. It features international support, including language subtitles and narration. The custom movie production system also prioritizes user privacy and data security, and the information provided is handled with care. Users can preview the movie during production and request revisions as needed. The created movies can be output in various formats according to the user's wishes, allowing for flexible use such as online sharing or presentations at specific events. This system efficiently stores and organizes the vast number of photos and videos taken daily with smartphones, allowing users to quickly relive memories when needed. For example, when creating a wedding movie, the generating AI constructs a moving story based on photos and videos provided by the user, adding music and effects to express a consistent message. This results in a movie that leaves a strong impression on viewers. Furthermore, users can preview the movie during production and request revisions as needed. For example, if a user wants to add specific photos or videos, the generating AI can reconstruct the movie accordingly. This results in the creation of a custom movie tailored to the user's wishes.Thus, the present invention provides a custom movie production service that efficiently stores and organizes the vast amount of photos and videos taken with a smartphone every day, allowing users to quickly relive their memories when needed. This enables the custom movie production system to efficiently organize the user's photos and videos and create moving movies.

[0070] The custom movie production system according to this embodiment comprises a generation unit, a story construction unit, an adjustment unit, a language support unit, a security unit, and an output unit. The generation unit produces a custom movie based on photos and videos provided by the user. For example, the generation unit analyzes the photos and videos provided by the user, selects the optimal scenes, and generates a movie. The generation unit can also use generation AI to analyze the content of photos and videos and generate a movie that follows the flow of a story. Furthermore, the generation unit can produce a custom movie based on the user's wishes or a specified theme. For example, the generation unit can construct a moving story and generate a movie based on wedding photos and videos. The story construction unit constructs a moving story for the movie generated by the generation unit. For example, the story construction unit can use generation AI to analyze the content of photos and videos and construct a moving and compelling story. Furthermore, the story construction unit can construct a story based on the user's wishes or a specified theme. Furthermore, the story construction unit can combine photos and videos to express a consistent message. For example, the story construction unit can construct a moving story and generate a movie based on self-introduction photos and videos. The adjustment unit adjusts music and effects to the story constructed by the story construction unit. For example, the adjustment unit uses generative AI to select the most suitable music for the story and adds effects. The adjustment unit can also make adjustments to match emotions and atmosphere. Furthermore, the adjustment unit can adjust music and effects to make a strong impression on the viewer. For example, the adjustment unit adds emotional music and adjusts effects to a wedding video. The language support unit makes the video adjusted by the adjustment unit multilingual. For example, the language support unit uses generative AI to add subtitles and narration to the video. Furthermore, the language support unit can produce content that conforms to different cultures and customs. In addition, the language support unit can support multiple languages ​​to cater to an international audience. For example, the language support unit adds multilingual subtitles to a wedding video to cater to an international audience.The security unit ensures the privacy and security of movies handled by the language support unit. The security unit, for example, uses generative AI to encrypt the provided information and ensure data security. The security unit can also implement access control to protect user privacy. Furthermore, the security unit can handle the provided information with care. For example, the security unit encrypts the data of a wedding movie to protect its privacy. The output unit outputs the movies secured by the security unit in a flexible format. For example, the output unit uses generative AI to convert the movie into a format suitable for online sharing. The output unit can also output in a format suitable for presentations at specific events. Furthermore, the output unit can output movies in various formats according to the user's wishes. For example, the output unit can output a wedding movie in a high-resolution video format for online sharing. As a result, the custom movie production system according to this embodiment can efficiently organize the user's photos and videos and produce moving movies.

[0071] The generation unit creates custom movies based on photos and videos provided by the user. For example, the generation unit analyzes the photos and videos provided by the user, selects the optimal scenes, and generates a movie. Specifically, the generation unit uses image recognition technology to analyze the content of photos and videos in detail and automatically extracts important scenes and emotional moments. Furthermore, the generation unit can also use generation AI to analyze the content of photos and videos and generate a movie that follows a storyline. For example, the generation AI utilizes deep learning technology to identify people, backgrounds, and types of events in photos and videos and constructs a story based on that. The generation unit can also create custom movies based on the user's wishes or specified themes. For example, the generation unit can build an emotional story and generate a movie based on wedding photos and videos. To emphasize themes or specific scenes specified by the user, the generation unit uses AI to select and edit scenes to generate the optimal movie. Furthermore, the generation unit can perform image processing such as color correction and noise reduction on the materials provided by the user to produce high-quality movies. This allows the generation unit to efficiently organize the user's photos and videos and create an emotionally impactful movie.

[0072] The story construction unit builds a moving story for the movie generated by the generation unit. For example, the story construction unit uses generational AI to analyze the content of photos and videos and construct a moving and compelling story. Specifically, the story construction unit uses natural language processing technology to analyze the facial expressions, actions, and background context of people in photos and videos, and creates a story based on this. The story construction unit can also build stories based on user requests or specified themes. For example, if a user requests a wedding movie, the story construction unit will use wedding photos and videos to create a moving story about the bride and groom from their first meeting to their marriage. Furthermore, the story construction unit can combine photos and videos to express a consistent message. For example, the story construction unit can build a moving story and generate a movie based on self-introduction photos and videos. Using AI, the story construction unit can adjust the order and timing of photos and videos to create a story that leaves a strong impression on the viewer. This allows the story construction unit to produce moving movies that meet the user's wishes.

[0073] The adjustment unit adjusts music and effects to the story constructed by the story building unit. For example, the adjustment unit uses generative AI to select the most suitable music for the story and add effects. Specifically, the adjustment unit adjusts the tempo and atmosphere of the music to match the flow of the story, maximizing the effect of moving the viewer. The adjustment unit can also make adjustments to match emotions and atmosphere. For example, it might select a quiet piano piece for an emotional scene and upbeat music for an exciting scene. Furthermore, the adjustment unit can adjust music and effects to make a strong impression on the viewer. For example, it could add emotional music and adjust effects to a wedding video. Effects include fade-in, fade-out, and transition effects, and by appropriately placing these, the overall flow of the video can be made smoother, leaving a strong impression on the viewer. The adjustment unit can use AI to automate the selection and placement of music and effects, enabling the efficient production of high-quality videos. In this way, the adjustment unit can play a crucial role in creating videos that move viewers.

[0074] The language support unit makes movies adjusted by the adjustment unit multilingual. For example, the language support unit adds subtitles and narration to movies using generative AI. Specifically, the language support unit converts the movie's audio into text using speech recognition technology and generates multilingual subtitles based on that. The language support unit can also produce content that is tailored to different cultures and customs. For example, in a wedding movie, it can add subtitles and narration that take into account the cultural background and customs of viewers from different cultural backgrounds. Furthermore, the language support unit can support multiple languages ​​to cater to international audiences. For example, the language support unit can add multilingual subtitles to a wedding movie to cater to an international audience. The language support unit can use AI to improve translation accuracy and achieve natural expression. As a result, the language support unit can make movies multilingual and provide high-quality content to international audiences.

[0075] The Security Department ensures the privacy and security of movies handled by the Language Support Department. For example, the Security Department uses generative AI to encrypt the provided information and ensure data security. Specifically, the Security Department uses encryption technology to protect movie data and user personal information. The Security Department can also implement access control to protect user privacy. For example, it can implement passwords and authentication systems to ensure that only specific users can access movies. Furthermore, the Security Department can handle the provided information with care. For example, the Security Department can encrypt wedding movie data to protect privacy. The Security Department can use AI to detect data anomalies and monitor for unauthorized access, thereby enhancing security. This allows the Security Department to ensure user privacy and data security, providing an environment where users can use custom movies with peace of mind.

[0076] The output unit outputs movies secured by the security unit in flexible formats. For example, the output unit uses generation AI to convert movies into formats suitable for online sharing. Specifically, the output unit converts movies into high-resolution video formats or streaming formats, making them easily shareable by users. The output unit can also output in formats suitable for presentations at specific events. For example, it can convert wedding movies to the appropriate resolution and format for large-screen screenings. Furthermore, the output unit can output movies in various formats according to user preferences. For example, it can output wedding movies in high-resolution video formats for online sharing. The output unit uses AI to automatically select the optimal output format, enabling efficient movie output. This allows the output unit to provide flexible output tailored to user needs and promote the use of custom movies.

[0077] The generation unit can estimate the user's emotions and adjust the timing of custom movie generation based on the estimated emotions. For example, if the user is emotional, the generation AI will immediately generate a custom movie to maintain that emotion. The generation unit can also generate a custom movie at an appropriate time, taking into account the user's schedule, if the user is busy. Furthermore, if the user is relaxed, the generation AI can generate a custom movie at a slower pace to maximize that emotion. This allows for more appropriate timing of movie generation by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The generation unit can analyze the user's past photo and video usage history and select the optimal generation method. For example, the generation unit can analyze the style of photos and videos the user has frequently used in the past, and the generation AI can generate a custom movie based on that. The generation unit can also analyze the patterns of photos and videos the user has used in specific events, and the generation AI can select the optimal generation method based on that. Furthermore, the generation unit can analyze the evaluation of movies the user has created in the past, and the generation AI can select the optimal generation method based on that. In this way, by analyzing the user's past usage history, the optimal generation method can be selected and a more satisfying custom movie can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past usage history data into the generation AI and have the generation AI select the optimal generation method.

[0079] The generation unit can filter custom movies based on the user's current lifestyle and areas of interest. For example, the generation unit can prioritize the use of relevant photos and videos based on the user's current lifestyle. The generation unit can also filter and use relevant photos and videos based on the user's areas of interest. Furthermore, the generation unit can have the generation AI generate the most suitable custom movie based on the user's current lifestyle and areas of interest. This allows for the generation of more relevant custom movies by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's current lifestyle and areas of interest into the generation AI and have the generation AI perform the filtering.

[0080] The generation unit can estimate the user's emotions and determine the priority of the movies to generate based on the estimated user emotions. For example, if the user is emotional, the generation unit will prioritize generating emotional movies to maintain that emotion through the generation AI. Similarly, if the user is relaxed, the generation unit can prioritize generating relaxing movies to maximize that emotion through the generation AI. Furthermore, if the user is busy, the generation unit can consider that emotion and prioritize generating movies that can be viewed quickly. This allows for the generation of more appropriate movies by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0081] The generation unit can prioritize the use of highly relevant photos and videos when generating a custom movie, taking into account the user's geographical location. For example, if the user is in a specific region, the generation AI will prioritize using photos and videos related to that region. Similarly, if the user is traveling, the generation AI can prioritize using photos and videos related to the travel destination. Furthermore, if the user is attending a specific event, the generation AI can prioritize using photos and videos related to that event. This allows for the generation of a custom movie using more relevant photos and videos by considering the user's geographical location. 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 user's geographical location data into the generation AI and have the AI ​​select highly relevant photos and videos.

[0082] The generation unit can analyze the user's social media activity and use relevant photos and videos when generating a custom movie. For example, the generation unit can analyze photos and videos shared by the user on social media, and the generation AI can generate a custom movie based on that analysis. The generation unit can also analyze the content of the user's social media posts, and the generation AI can use relevant photos and videos. Furthermore, the generation unit can analyze the reactions of the user's social media followers and friends, and the generation AI can generate a custom movie based on that analysis. This allows for the generation of custom movies using more relevant photos and videos by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's social media activity data into the generation AI and have the generation AI select relevant photos and videos.

[0083] The story construction unit can estimate the user's emotions and adjust the way the story is presented based on those emotions. For example, if the user is emotional, the story construction unit's generative AI can construct a story using emotionally moving expressions. Similarly, if the user is relaxed, the story construction unit can construct a story using relaxing expressions. Furthermore, if the user is excited, the story construction unit can construct a story using visually stimulating expressions. This allows for the creation of more emotionally impactful stories by adjusting the story's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the story construction unit may be performed using AI, or not. For example, the story construction unit can input user emotion data into the generative AI and have the generative AI adjust the story's presentation.

[0084] The story construction unit can adjust the level of detail in a story based on the importance of photos and videos during the story construction process. For example, the story construction unit can prioritize the use of important photos and videos, and the generating AI can construct a detailed story. Alternatively, the story construction unit can simplify less important photos and videos, and the generating AI can construct a shorter story. Furthermore, the story construction unit can have the generating AI adjust the level of detail in the story according to its importance to create a balanced story. This allows for the creation of a balanced story by adjusting the level of detail in the story based on the importance of photos and videos. Some or all of the above processes in the story construction unit may be performed using AI, or not. For example, the story construction unit can input importance data of photos and videos into the generating AI and have the generating AI perform the adjustment of the level of detail in the story.

[0085] The story building unit can apply different story algorithms depending on the event category when building a story. For example, in the case of a wedding, the generating AI can apply an emotional story algorithm. In the case of a funeral, the generating AI can also apply a solemn story algorithm. Furthermore, in the case of a self-introduction, the generating AI can also apply a friendly story algorithm. By applying different story algorithms depending on the event category, a more appropriate story can be built. Some or all of the above processing in the story building unit may be performed using AI, for example, or without AI. For example, the story building unit can input event category data into the generating AI and have the generating AI execute the application of story algorithms.

[0086] The story building unit can estimate the user's emotions and adjust the story length based on the estimated emotions. For example, if the user is emotional, the story building unit's generating AI can construct a longer story. If the user is relaxed, the story building unit's generating AI can construct a story of a moderate length. Furthermore, if the user is in a hurry, the story building unit's generating AI can construct a shorter story. This allows for the construction of a more appropriate story length by adjusting the story length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the story building unit may be performed using AI, or not. For example, the story building unit can input user emotion data into the generating AI and have the generating AI adjust the story length.

[0087] The story building unit can prioritize stories based on when photos and videos were taken. For example, the story building unit can prioritize recently taken photos and videos, and the generating AI can build a story. The story building unit can also prioritize photos and videos of specific events, and the generating AI can build a story. Furthermore, the story building unit can have the generating AI prioritize stories according to when they were taken, creating a balanced story. This allows for the creation of a balanced story by prioritizing stories based on when photos and videos were taken. Some or all of the above processes in the story building unit may be performed using AI, or not. For example, the story building unit can input photo and video shooting date data into the generating AI and have the generating AI determine the story priorities.

[0088] The story construction unit can adjust the order of stories based on the relevance of photos and videos during story construction. For example, the story construction unit can use highly relevant photos and videos in sequence, and the generating AI can construct the story. The story construction unit can also simplify less relevant photos and videos, and the generating AI can adjust the order of the stories. Furthermore, the story construction unit can have the generating AI adjust the order of stories according to relevance to construct a balanced story. This allows for the construction of a balanced story by adjusting the order of stories based on the relevance of photos and videos. Some or all of the above processing in the story construction unit may be performed using AI, for example, or without AI. For example, the story construction unit can input relevance data of photos and videos into the generating AI and have the generating AI perform the adjustment of the story order.

[0089] The adjustment unit can estimate the user's emotions and adjust the selection of music and effects based on the estimated emotions. For example, if the user is emotional, the adjustment unit can have the generating AI select emotional music and effects. The adjustment unit can also have the generating AI select relaxing music and effects if the user is relaxed. Furthermore, if the user is excited, the adjustment unit can have the generating AI select visually stimulating effects. This allows for the creation of more emotionally impactful movies by adjusting the selection of music and effects according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user emotion data into the generating AI and have the generating AI select music and effects.

[0090] The adjustment unit can select the optimal adjustment method based on the content of the photos and videos when adjusting music and effects. For example, the adjustment unit can use the generating AI to select the most suitable music based on the content of the photos and videos. The adjustment unit can also use the generating AI to select the most suitable effects based on the content of the photos and videos. Furthermore, the adjustment unit can use the generating AI to select the adjustment method for music and effects based on the content of the photos and videos. This allows for the generation of movies with more appropriate music and effects by selecting the optimal adjustment method based on the content of the photos and videos. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input photo and video content data into the generating AI and have the generating AI select the optimal adjustment method.

[0091] The adjustment unit can apply different adjustment algorithms depending on the event category when adjusting music and effects. For example, in the case of a wedding, the generation AI can select emotionally moving music and effects. In the case of a funeral, the generation AI can select solemn music and effects. Furthermore, in the case of a self-introduction, the generation AI can select friendly music and effects. By applying different adjustment algorithms depending on the event category, it is possible to generate a movie with more appropriate music and effects. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input event category data into the generation AI and have the generation AI execute the application of the adjustment algorithm.

[0092] The adjustment unit can estimate the user's emotions and adjust the length of music and effects based on the estimated emotions. For example, if the user is emotional, the adjustment unit's generating AI can select longer music and effects. The adjustment unit can also select music and effects of appropriate length if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can select shorter music and effects. This allows for the creation of more appropriate movies by adjusting the length of music and effects according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user emotion data into the generating AI and have the generating AI adjust the length of music and effects.

[0093] The adjustment unit can determine the priority of adjustments based on the shooting dates of photos and videos when adjusting music and effects. For example, the adjustment unit can prioritize the use of recently taken photos and videos, and the generating AI can adjust the music and effects accordingly. Alternatively, the adjustment unit can prioritize the use of photos and videos from specific events, and the generating AI can adjust the music and effects accordingly. Furthermore, the adjustment unit can also determine the priority of music and effect adjustments based on the shooting dates, allowing the generating AI to create more appropriate movies. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input photo and video shooting date data into the generating AI and have the generating AI determine the adjustment priority.

[0094] The adjustment unit can adjust the order of adjustments based on the relevance of photos and videos when adjusting music and effects. For example, the adjustment unit can use highly relevant photos and videos in sequence, and the generating AI can adjust the music and effects. The adjustment unit can also simplify less relevant photos and videos, and the generating AI can adjust the order of music and effects. Furthermore, the adjustment unit can have the generating AI adjust the order of music and effects according to relevance to construct a balanced movie. This allows for the generation of a more balanced movie by adjusting the order of adjustments based on the relevance of photos and videos. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevance data of photos and videos into the generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0095] The language support unit can estimate the user's emotions and adjust the expression of subtitles and narration based on the estimated emotions. For example, if the user is emotional, the generating AI can adjust the subtitles and narration using emotionally charged expressions. Similarly, if the user is relaxed, the generating AI can adjust the subtitles and narration using relaxing expressions. Furthermore, if the user is excited, the generating AI can adjust the subtitles and narration using visually stimulating expressions. This allows for the creation of more emotionally impactful movies by adjusting the expression of subtitles and narration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the language support unit may be performed using AI, or not. For example, the language support unit can input user emotion data into the generating AI and have the generating AI adjust the expression of subtitles and narration.

[0096] The language support unit can analyze the user's language history and select the optimal language support method during language support. For example, the language support unit can use a generating AI to select the optimal language support method based on the languages ​​the user has used in the past. The language support unit can also analyze the user's language history and have the generating AI select the optimal language for subtitles and narration. Furthermore, the language support unit can generate a movie based on the user's language history, with the generating AI selecting the optimal language support method. This allows for the selection of the optimal language support method and the generation of a more appropriate movie by analyzing the user's language history. Some or all of the above-described processes in the language support unit may be performed using AI, for example, or without AI. For example, the language support unit can input the user's language history data into the generating AI and have the generating AI select the optimal language support method.

[0097] The language handling unit can apply different language handling algorithms depending on the event category during language handling. For example, in the case of a wedding, the generating AI can apply an emotional language handling algorithm. Similarly, in the case of a funeral, the generating AI can apply a solemn language handling algorithm. Furthermore, in the case of a self-introduction, the generating AI can apply a friendly language handling algorithm. This allows for the generation of more appropriate movies by applying different language handling algorithms depending on the event category. Some or all of the above processing in the language handling unit may be performed using AI, for example, or without AI. For example, the language handling unit can input event category data into the generating AI and have the generating AI perform the application of the language handling algorithm.

[0098] The language support unit can estimate the user's emotions and adjust the length of subtitles and narration based on the estimated emotions. For example, if the user is emotional, the generating AI in the language support unit will select longer subtitles and narration. If the user is relaxed, the generating AI can select subtitles and narration of an appropriate length. Furthermore, if the user is in a hurry, the generating AI can select shorter subtitles and narration. This allows for the generation of more appropriate movies by adjusting the length of subtitles and narration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the language support unit may be performed using AI, or not using AI. For example, the language support unit can input user emotion data into the generating AI and have the generating AI adjust the length of subtitles and narration.

[0099] The language support unit can select the optimal language support method by considering the user's geographical location information during language support. For example, if the user is in a specific region, the generating AI can select subtitles and narration corresponding to the language of that region. Furthermore, if the user is traveling, the generating AI can select subtitles and narration corresponding to the language of the travel destination. In addition, the language support unit can generate a movie based on the user's geographical location information, with the generating AI selecting the optimal language support method. This allows for the selection of a more appropriate language support method and the generation of a movie by considering the user's geographical location information. Some or all of the above-described processes in the language support unit may be performed using AI, or not. For example, the language support unit can input the user's geographical location data into the generating AI and have the generating AI select the optimal language support method.

[0100] The language support unit can analyze the user's social media activity and select the optimal language support method during language support. For example, the language support unit can analyze the language the user uses on social media, and the generating AI can select the optimal language support method based on that analysis. The language support unit can also analyze the content of the user's social media posts, and the generating AI can select the appropriate language support method. Furthermore, the language support unit can analyze the languages ​​of the user's social media followers and friends, and the generating AI can select the optimal language support method based on that analysis. This allows for the selection of a more appropriate language support method and the generation of a movie by analyzing the user's social media activity. Some or all of the above processing in the language support unit may be performed using AI, for example, or without AI. For example, the language support unit can input the user's social media activity data into the generating AI and have the generating AI select the optimal language support method.

[0101] The security unit can estimate the user's emotions and adjust the data security level based on the estimated emotions. For example, if the user is feeling anxious, the generating AI can apply a high security level. The security unit can also have the generating AI apply a moderate security level if the user is relaxed. Furthermore, if the user is in a hurry, the security unit can have the generating AI apply a security level that allows for a quick response. This allows for more appropriate security by adjusting the data security level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not using AI. For example, the security unit can input user emotion data into a generating AI and have the generating AI adjust the data security level.

[0102] The security unit can analyze a user's past security history to select the optimal security method when ensuring data security. For example, the security unit can use a generating AI to select the optimal security method based on the security settings the user has used in the past. The security unit can also analyze the user's security history and have the generating AI select the optimal data security method. Furthermore, the security unit can use a generating AI to select the optimal security method and protect the data based on the user's past security history. In this way, by analyzing the user's past security history, the optimal security method can be selected and the data protected. Some or all of the above processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the user's past security history data into a generating AI and have the generating AI select the optimal security method.

[0103] The security department can apply different security algorithms depending on the event category when ensuring data security. For example, in the case of a wedding, the generating AI can apply a security algorithm suitable for an emotional event. Similarly, in the case of a funeral, the generating AI can apply a security algorithm suitable for a solemn event. Furthermore, in the case of a self-introduction, the generating AI can apply a security algorithm suitable for a friendly event. By applying different security algorithms depending on the event category, more appropriate security can be provided. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input event category data into the generating AI and have the generating AI perform the application of security algorithms.

[0104] The security unit can estimate the user's emotions and determine data security priorities based on those estimated emotions. For example, if the user is feeling anxious, the generating AI can prioritize data security. If the user is relaxed, the generating AI can prioritize data security with a moderate level of importance. Furthermore, if the user is in a hurry, the generating AI can prioritize data security with a level of importance that allows for a quick response. This allows for more appropriate security by prioritizing data security according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not. For example, the security unit can input user emotion data into a generating AI and have the generating AI determine data security priorities.

[0105] The security unit can select the optimal security method when ensuring data security, taking into account the user's geographical location information. For example, if the user is in a specific region, the generating AI can select a security method appropriate for that region. Furthermore, if the user is traveling, the generating AI can select a security method appropriate for the travel destination. In addition, the security unit can use the generating AI to select the optimal security method and protect the data based on the user's geographical location information. This allows for the selection of a more appropriate security method and data protection by considering the user's geographical location information. Some or all of the above-described processes in the security unit may be performed using AI, or not. For example, the security unit can input the user's geographical location data into the generating AI and have the generating AI select the optimal security method.

[0106] The security department can analyze a user's social media activity to select the optimal security method when ensuring data security. For example, the security department can analyze information shared by a user on social media, and a generative AI can select the optimal security method based on that analysis. The security department can also analyze the content of a user's social media posts, and the generative AI can select relevant security methods. Furthermore, the security department can analyze the reactions of a user's social media followers and friends, and the generative AI can select the optimal security method based on that analysis. This allows for the selection of more appropriate security methods and data protection by analyzing the user's social media activity. Some or all of the above processes in the security department may be performed using AI, for example, or not. For example, the security department can input user social media activity data into a generative AI and have the generative AI select the optimal security method.

[0107] The output unit can estimate the user's emotions and adjust the output format based on the estimated emotions. For example, if the user is emotional, the generating AI can select an emotionally charged output format. The output unit can also select a relaxing output format if the user is relaxed. Furthermore, if the user is excited, the generating AI can select a visually stimulating output format. By adjusting the output format according to the user's emotions, a more appropriate movie can be generated. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into the generating AI and have the generating AI perform the adjustment of the output format.

[0108] The output unit can select the optimal output method by analyzing the user's past output history when selecting an output format. For example, the output unit can use a generating AI to select the optimal output method based on the output formats the user has used in the past. The output unit can also analyze the user's output history and have the generating AI select the optimal output format. Furthermore, the output unit can generate a movie by having the generating AI select the optimal output method based on the user's past output history. This allows for the selection of the optimal output method and the generation of a more appropriate movie by analyzing the user's past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into the generating AI and have the generating AI select the optimal output method.

[0109] The output unit can apply different output algorithms depending on the event category when selecting the output format. For example, in the case of a wedding, the output unit's generating AI can apply an emotional output algorithm. Similarly, in the case of a funeral, the output unit can apply a solemn output algorithm. Furthermore, in the case of a self-introduction, the output unit can apply a friendly output algorithm. This allows for the generation of more appropriate movies by applying different output algorithms depending on the event category. Some or all of the above processing in the output unit may be performed using AI, or without AI. For example, the output unit can input event category data into the generating AI and have the generating AI apply the output algorithm.

[0110] The output unit can estimate the user's emotions and determine the priority of output formats based on the estimated emotions. For example, if the user is emotional, the output unit's generating AI will prioritize selecting emotionally moving output formats. Similarly, if the user is relaxed, the output unit can prioritize selecting output formats that promote relaxation. Furthermore, if the user is in a hurry, the output unit can prioritize selecting output formats that allow for quick responses. This allows for the generation of more appropriate movies by prioritizing output formats according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the output unit may be performed using AI, or not. For example, the output unit can input user emotion data into the generating AI and have the generating AI determine the priority of output formats.

[0111] The output unit can select the optimal output method by considering the user's geographical location information when selecting the output format. For example, if the user is in a specific region, the generation AI can select an output format suitable for that region. Furthermore, if the user is traveling, the generation AI can select an output format suitable for the travel destination. In addition, the output unit can generate a movie based on the user's geographical location information, with the generation AI selecting the optimal output method. This allows for the selection of a more appropriate output method and the generation of a movie by considering the user's geographical location information. Some or all of the above-described processes in the output unit may be performed using AI, or without AI. For example, the output unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal output method.

[0112] The output unit can analyze the user's social media activity to select the optimal output method when selecting the output format. For example, the output unit can consider whether the user will share the content on social media, and the generating AI can select the optimal output format. The output unit can also analyze the content of the user's social media posts, and the generating AI can select a relevant output format. Furthermore, the output unit can analyze the reactions of the user's social media followers and friends, and the generating AI can select the optimal output method based on that. In this way, by analyzing the user's social media activity, a more appropriate output method can be selected and the movie can be generated. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into the generating AI and have the generating AI select the optimal output method.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The generation unit can estimate the user's emotions and adjust the timing of custom movie generation based on those emotions. For example, if the user is emotional, the generation AI will immediately generate a custom movie to maintain that emotion. If the user is busy, the generation AI can also consider the user's schedule and generate a custom movie at an appropriate time. Furthermore, if the user is relaxed, the generation AI can generate a custom movie at a slower pace to make the most of that emotion. In this way, by adjusting the timing of custom movie generation according to the user's emotions, movies can be generated at a more appropriate time.

[0115] The generation unit can analyze the user's past photo and video usage history and select the optimal generation method. For example, it can analyze the style of photos and videos the user has frequently used in the past, and the generation AI can generate a custom movie based on that. It can also analyze the patterns of photos and videos the user has used for specific events, and the generation AI can select the optimal generation method based on that. Furthermore, it can analyze the ratings of movies the user has created in the past, and the generation AI can select the optimal generation method based on that. In this way, by analyzing the user's past usage history, the optimal generation method can be selected, and a more satisfying custom movie can be generated.

[0116] The generation unit can filter custom movies based on the user's current lifestyle and areas of interest. For example, the generation AI can prioritize using relevant photos and videos based on the user's current lifestyle. It can also filter and use relevant photos and videos based on the user's areas of interest. Furthermore, the generation AI can generate the most relevant custom movie based on the user's current lifestyle and areas of interest. This allows for the generation of more relevant custom movies by filtering based on the user's current lifestyle and areas of interest.

[0117] The generation unit can estimate the user's emotions and determine the priority of the movies to generate based on those estimated emotions. For example, if the user is emotional, the generation AI will prioritize generating emotional movies to maintain that emotion. If the user is relaxed, the generation AI can also prioritize generating relaxing movies to maximize that emotion. Furthermore, if the user is busy, the generation AI can take that emotion into consideration and prioritize generating movies that can be watched in a short amount of time. In this way, by determining the priority of movies according to the user's emotions, more appropriate movies can be generated.

[0118] The generation unit can prioritize the use of highly relevant photos and videos when generating custom movies, taking into account the user's geographical location. For example, if the user is in a specific region, the generation AI will prioritize using photos and videos related to that region. Similarly, if the user is traveling, the generation AI can prioritize using photos and videos related to their travel destination. Furthermore, if the user is attending a specific event, the generation AI can prioritize using photos and videos related to that event. This allows for the generation of custom movies using more relevant photos and videos by considering the user's geographical location.

[0119] The generation unit can analyze the user's social media activity and use relevant photos and videos when generating custom movies. For example, it can analyze photos and videos shared by the user on social media and generate a custom movie based on that analysis. It can also analyze the content of the user's social media posts and use relevant photos and videos. Furthermore, it can analyze the reactions of the user's social media followers and friends and generate a custom movie based on that analysis. This allows for the generation of custom movies using more relevant photos and videos by analyzing the user's social media activity.

[0120] The story construction unit can estimate the user's emotions and adjust the way the story is presented based on those emotions. For example, if the user is moved, the generative AI will construct a story using emotionally moving expressions. If the user is relaxed, the generative AI can also construct a story using relaxing expressions. Furthermore, if the user is excited, the generative AI can construct a story using visually stimulating expressions. In this way, by adjusting the way the story is presented according to the user's emotions, a more emotionally impactful story can be created.

[0121] The story building section can adjust the level of detail in a story based on the importance of photos and videos used during the story creation process. For example, it can prioritize the use of important photos and videos, allowing the generating AI to construct a detailed story. It can also simplify less important photos and videos, allowing the generating AI to construct a shorter story. Furthermore, the generating AI can adjust the level of detail in a story according to its importance, creating a well-balanced story. This allows for the creation of a balanced story by adjusting the level of detail in a story based on the importance of photos and videos.

[0122] The story generation unit can apply different story algorithms depending on the event category during story creation. For example, in the case of a wedding, the generating AI can apply an emotional story algorithm. In the case of a funeral, the generating AI can apply a solemn story algorithm. Furthermore, in the case of a self-introduction, the generating AI can apply a friendly story algorithm. By applying different story algorithms depending on the event category, a more appropriate story can be constructed.

[0123] The story generation unit can estimate the user's emotions and adjust the story length based on those emotions. For example, if the user is moved, the generative AI will create a longer story. If the user is relaxed, the generative AI can create a story of a moderate length. Furthermore, if the user is in a hurry, the generative AI can create a shorter story. By adjusting the story length according to the user's emotions, it is possible to create a story of a more appropriate length.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The generation unit creates a custom movie based on photos and videos provided by the user. For example, the generation unit analyzes the photos and videos provided by the user, selects the optimal scenes, and generates the movie. The generation unit can also use generation AI to analyze the content of photos and videos and generate a movie that follows a storyline. Furthermore, the generation unit can create a custom movie based on the user's wishes or specified theme. Step 2: The story building unit constructs an emotionally compelling story for the movie generated by the generation unit. For example, the story building unit uses generation AI to analyze the content of photos and videos and construct an emotionally moving and persuasive story. The story building unit can also construct a story based on the user's wishes or specified theme. Furthermore, the story building unit can combine photos and videos to express a consistent message. Step 3: The adjustment unit adjusts the music and effects to the story constructed by the story building unit. For example, the adjustment unit uses generative AI to select the most suitable music for the story and add effects. The adjustment unit can also make adjustments to match emotions and atmosphere. Furthermore, the adjustment unit can adjust the music and effects to make a strong impression on the viewer. Step 4: The language support unit makes the movie, which has been adjusted by the adjustment unit, multilingual. The language support unit adds subtitles and narration to the movie, for example, using generative AI. The language support unit can also produce content that is appropriate for different cultures and customs. Furthermore, the language support unit can support multiple languages ​​to accommodate international audiences. Step 5: The security department ensures the privacy and security of the movies handled by the language support department. The security department ensures data security by, for example, encrypting the provided information using generative AI. The security department can also implement access control to protect user privacy. Furthermore, the security department can handle the provided information with care. Step 6: The output unit outputs the movie secured by the security unit in a flexible format. For example, the output unit uses generation AI to convert the movie into a format suitable for online sharing. The output unit can also output in a format suitable for presentations at specific events. Furthermore, the output unit can output the movie in various formats according to the user's preferences.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the generation unit, story construction unit, adjustment unit, language support unit, security unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14, which analyzes photos and videos provided by the user, selects the optimal scenes, and generates a movie. The story construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs an emotionally compelling story for the generated movie. The adjustment unit is implemented by the control unit 46A of the smart device 14, which selects the most suitable music for the story and adds effects. The language support unit is implemented by the specific processing unit 290 of the data processing unit 12, which adds multilingual subtitles and narration to the movie. The security unit is implemented by the specific processing unit 290 of the data processing unit 12, which encrypts the provided information and ensures data security. The output unit is implemented by the control unit 46A of the smart device 14, which converts the movie into a format for online sharing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the generation unit, story construction unit, adjustment unit, language support unit, security unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214, which analyzes photos and videos provided by the user, selects the optimal scenes, and generates a movie. The story construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs an emotionally compelling story for the generated movie. The adjustment unit is implemented by the control unit 46A of the smart glasses 214, which selects the most suitable music for the story and adds effects. The language support unit is implemented by the specific processing unit 290 of the data processing unit 12, which adds multilingual subtitles and narration to the movie. The security unit is implemented by the specific processing unit 290 of the data processing unit 12, which encrypts the provided information and ensures data security. The output unit is implemented by the control unit 46A of the smart glasses 214, which converts the movie into a format for online sharing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the generation unit, story construction unit, adjustment unit, language support unit, security unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314, which analyzes photos and videos provided by the user, selects the optimal scenes, and generates a movie. The story construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs an emotionally compelling story for the generated movie. The adjustment unit is implemented by the control unit 46A of the headset terminal 314, which selects the most suitable music for the story and adds effects. The language support unit is implemented by the specific processing unit 290 of the data processing unit 12, which adds multilingual subtitles and narration to the movie. The security unit is implemented by the specific processing unit 290 of the data processing unit 12, which encrypts the provided information and ensures data security. The output unit is implemented by the control unit 46A of the headset terminal 314, which converts the movie into a format for online sharing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the generation unit, story construction unit, adjustment unit, language support unit, security unit, and output unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414, which analyzes photos and videos provided by the user, selects the optimal scenes, and generates a movie. The story construction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which constructs an emotionally compelling story for the generated movie. The adjustment unit is implemented, for example, by the control unit 46A of the robot 414, which selects the most suitable music for the story and adds effects. The language support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which adds multilingual subtitles and narration to the movie. The security unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which encrypts the provided information and ensures data security. The output unit is implemented, for example, by the control unit 46A of the robot 414, which converts the movie into a format for online sharing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) The generation unit creates custom movies based on photos and videos provided by the user, A story construction unit constructs an emotionally moving story for the movie generated by the generation unit, The adjustment unit adjusts music and effects to the story constructed by the aforementioned story construction unit, A language support unit that makes the movie adjusted by the adjustment unit compatible with multiple languages, A security unit that ensures the privacy and security of movies handled by the language support unit, The system includes an output unit that outputs the movie secured by the security unit in a flexible format. A system characterized by the following features. (Note 2) The generating unit is It estimates the user's emotions and adjusts the timing of custom movie generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The system analyzes the user's past photo and video usage history to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is When generating custom movies, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It estimates the user's emotions and determines the priority of the movies to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is When generating custom movies, the system prioritizes the use of relevant photos and videos, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating custom movies, we analyze the user's social media activity and use relevant photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned story construction unit, It estimates the user's emotions and adjusts how the story is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned story construction unit, When building a story, adjust the level of detail based on the importance of the photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned story construction unit, When constructing a story, different story algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned story construction unit, It estimates the user's emotions and adjusts the story length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned story construction unit, When constructing a story, prioritize the story based on when the photos and videos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned story construction unit, When constructing the story, adjust the order of the story based on the relevance of photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, It estimates the user's emotions and adjusts the selection of music and effects based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, When adjusting music and effects, the system selects the optimal adjustment method based on the content of the photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, When adjusting music and effects, different adjustment algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, It estimates the user's emotions and adjusts the length of music and effects based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, When adjusting music and effects, prioritize adjustments based on when the photos and videos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, When adjusting music and effects, adjust the order of adjustments based on the relevance of photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 20) The language support unit is, It estimates the user's emotions and adjusts the way subtitles and narration are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The language support unit is, When implementing language support, the system analyzes the user's language history to select the most suitable language support method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The language support unit is, When implementing language support, different language support algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The language support unit is, It estimates the user's emotions and adjusts the length of subtitles and narration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The language support unit is, When implementing language support, the optimal language support method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The language support unit is, When implementing language support, we analyze users' social media activity to select the most suitable language support method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned security unit is It estimates user sentiment and adjusts the level of data security based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned security unit is When ensuring data security, the system analyzes the user's past security history to select the most suitable security method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned security unit is When ensuring data security, different security algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned security unit is It estimates user sentiment and determines data security priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned security unit is When ensuring data security, the optimal security method should be selected while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned security unit is When ensuring data security, we analyze users' social media activity to select the most suitable security method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The output unit is, It estimates the user's emotions and adjusts the output format based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The output unit is, When selecting an output format, the system analyzes the user's past output history to select the optimal output method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The output unit is, When selecting the output format, different output algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The output unit is, It estimates the user's emotions and determines the priority of the output format based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The output unit is, When selecting an output format, the optimal output method is chosen by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The output unit is, When selecting the output format, the system analyzes the user's social media activity to determine the optimal output method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0198] 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. The generation unit creates custom movies based on photos and videos provided by the user, A story construction unit constructs an emotionally moving story for the movie generated by the generation unit, The adjustment unit adjusts music and effects to the story constructed by the aforementioned story construction unit, A language support unit that makes the movie adjusted by the adjustment unit compatible with multiple languages, A security unit that ensures the privacy and security of movies handled by the language support unit, The system includes an output unit that outputs the movie secured by the security unit in a flexible format. A system characterized by the following features.

2. The generating unit is It estimates the user's emotions and adjusts the timing of custom movie generation based on the estimated user emotions. The system according to feature 1.

3. The generating unit is The system analyzes the user's past photo and video usage history to select the optimal generation method. The system according to feature 1.

4. The generating unit is When generating custom movies, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The generating unit is It estimates the user's emotions and determines the priority of the movies to generate based on the estimated user emotions. The system according to feature 1.

6. The generating unit is When generating custom movies, the system prioritizes the use of relevant photos and videos, taking into account the user's geographical location. The system according to feature 1.

7. The generating unit is When generating custom movies, we analyze the user's social media activity and use relevant photos and videos. The system according to feature 1.

8. The aforementioned story construction unit, It estimates the user's emotions and adjusts how the story is presented based on those estimated emotions. The system according to feature 1.

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