system

Abstract

The system according to this embodiment aims to automatically generate scripts and videos and easily monetize them simply by uploading daily schedules, photos, and thoughts. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit uploads daily schedules, photos, and thoughts. The generation unit analyzes the information uploaded by the reception unit and automatically generates scripts and videos. The confirmation unit allows the user to review and publish the videos generated by the generation unit.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that many people were unable to monetize due to the difficulty of video production and ended up being frustrated.

[0005] The system according to the embodiment aims to automatically generate a scenario and a video simply by uploading daily schedules, photos, and thoughts, and easily monetize them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit uploads daily schedules, photos, and thoughts. The generation unit analyzes the information uploaded by the reception unit and automatically generates scripts and videos. The confirmation unit allows the user to review and publish the videos generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate scripts and videos and easily monetize them simply by uploading daily schedules, photos, and thoughts. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated video production system according to an embodiment of the present invention is a system that automates video production using a generation AI and provides a service that allows anyone to easily monetize their work. This system can automatically generate scripts and videos simply by uploading daily schedules, photos, and thoughts. Specifically, first, the user uploads their daily schedule, photos, and thoughts. Next, the generation AI analyzes this information and automatically generates scripts and videos. The generated videos can be easily monetized simply by the user reviewing the content and publishing it. In addition, the personal data obtained is used to enhance the generation AI. For example, the user uploads their daily schedule, photos, and thoughts. At this time, the user does not need any special skills or knowledge to provide information. For example, they can simply upload photos taken with a smartphone or text written like a diary. Next, the generation AI analyzes the uploaded information. Based on the information provided by the user, the generation AI automatically generates a video script. For example, it analyzes the photos and text uploaded by the user and constructs a story based on that. Based on this story, the generation AI generates a video. The generated videos can be easily monetized simply by the user reviewing the content and publishing it. Users can review the generated videos and make modifications as needed. For example, they can change parts of the generated video or add additional information. Ultimately, users can earn advertising revenue by publishing their videos. The personal data collected is also used to enhance the generating AI. The generating AI uses the information provided by users as training data to perform more advanced analysis and generation. This improves the performance of the generating AI, enabling the creation of higher-quality videos. This system significantly lowers the barrier to video production, making it easy for anyone to monetize their content. For example, even beginners with no video production skills can generate professional-looking videos simply by uploading their daily schedules, photos, and thoughts. Furthermore, they can efficiently monetize their content without spending a lot of time on video production. In addition, the enhancement of the generating AI improves the quality of the service and increases user satisfaction.For example, by performing more advanced analysis, the generation AI can create videos that align with the user's intentions. This allows users to publish more satisfying videos, increasing their chances of successful monetization. As a result, automated video production systems allow users to easily monetize their content by simply uploading their daily schedules, photos, and thoughts, and automatically generating scripts and videos.

[0029] The automated video production system according to this embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit allows users to upload their daily schedules, photos, and thoughts. For example, the reception unit can upload photos taken by the user with their smartphone or text written like a diary. The reception unit allows users to easily provide information without requiring any special skills or knowledge. The generation unit uses a generation AI to analyze the information uploaded by the reception unit and automatically generates scripts and videos. For example, the generation unit analyzes photos and text uploaded by the user and constructs a story based on them. The generation unit uses the generation AI to generate a video based on the story. The generation unit can also modify parts of the generated video or add additional information. The generation unit uses the generation AI to utilize the information provided by the user as training data to perform more advanced analysis and generation. The generation unit improves the performance of the generation AI and generates higher quality videos. The confirmation unit allows the user to review and publish the video generated by the generation unit. For example, the confirmation unit can review the generated video and make corrections as needed. The verification unit can modify parts of the generated video or add additional information. This allows the automated video production system according to the embodiment to automatically generate scripts and videos and easily monetize them simply by the user uploading their daily schedule, photos, and thoughts. Some or all of the above-described processes in the reception unit, generation unit, and verification unit may be performed using AI, or not. For example, the reception unit can input photos and text uploaded by the user into the generation AI, which can then analyze this information to generate scripts and videos. The generation unit can use the information provided by the user as training data based on the scripts and videos generated by the generation AI to improve the performance of the generation AI. The verification unit allows the user to review the video generated by the generation AI and make modifications as needed.

[0030] The reception area allows users to upload their daily schedules, photos, and thoughts. For example, users can upload photos taken with their smartphones or text entries written like a diary. The reception area requires no special skills or knowledge, making it easy to provide information. Specifically, users can easily input daily events and thoughts through a dedicated application, attaching photos and video clips. The application features an intuitive interface, designed for user-friendliness. For example, users can use the calendar function to input their daily schedule and record the day's events along with photos. They can also use the voice input function to record their thoughts and feelings and convert them into text. This allows users to easily upload and provide information to the system. Furthermore, the reception area has a function to automatically categorize and tag uploaded information. For example, it can recognize people and places in photos and tag them accordingly, making them easier to search later. It can also analyze text content, extract important keywords, and tag them. This allows for efficient management of user-provided information and save it in a format easily usable by the generation and verification departments.

[0031] The generation unit uses a generation AI to analyze information uploaded by the reception unit and automatically generate scripts and videos. For example, the generation unit analyzes photos and text uploaded by the user and constructs a story based on them. Specifically, the generation AI uses natural language processing technology to understand the content of the text and image recognition technology to analyze the content of photos and video clips. This allows it to create a story outline based on the information provided by the user and generate a script accordingly. For example, the generation AI can construct a travelogue-like story based on photos taken by the user during a trip and their impressions of the time. The generated script is divided into scenes, and each scene includes corresponding video and narration. The generation unit uses the generation AI to generate a video based on the story. Specifically, the generation AI edits the video according to the script and adds narration and music. For example, it can display photos provided by the user in a slideshow format and read the text content aloud as narration. It can also select appropriate background music to create the overall atmosphere of the video. The generation unit can also modify parts of the generated video or add additional information. For example, if a user wants to emphasize a particular scene, they can extend the video of that scene or add special effects. Furthermore, the generation unit uses the information provided by the user as training data to improve the performance of the generation AI. This allows the generation AI to perform more advanced analysis and generation, enabling the creation of higher-quality videos.

[0032] The verification unit allows users to review and publish videos generated by the generation unit. For example, the verification unit can review the generated video and make modifications as needed. Specifically, users can preview the generated video and review the content of each scene, narration, and music. If a user wishes to make modifications, they can use a dedicated editing tool to change parts of the video or add additional information. For example, they can add new photos to specific scenes or change the narration. The verification unit can also modify parts of the generated video or add additional information. For example, if a user wants to emphasize a particular scene, they can extend the video of that scene or add special effects. Furthermore, the verification unit also has a function for publishing the generated video. Users can easily upload the generated video to social media or video sharing sites. This allows users to widely share their memories and daily events and potentially monetize them. The verification unit can improve the overall system performance by collecting user feedback and providing it to the generation and reception units. For example, if a user desires a specific function or effect, they can communicate that request to the generation unit, which can then use it as training data for the generation AI. This allows the verification unit to easily review the generated video, make corrections as needed, and then publish it.

[0033] The generation unit can analyze photos and text uploaded by users and construct a story based on them. For example, the generation unit can analyze photos and text uploaded by users and construct a story plot based on them. The generation unit can use a generation AI to construct a story based on the story plot. The generation unit can also use a generation AI to create character settings and construct a story. The generation unit can also use a generation AI to construct the flow of a story based on information uploaded by users. In this way, the generation unit can generate more concrete videos by analyzing photos and text uploaded by users and constructing a story based on them. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input photos and text uploaded by users into a generation AI, and the generation AI can analyze that information and construct a story.

[0034] The generation unit can modify parts of the generated video or add additional information. For example, the generation unit can add scenes to the generated video. The generation unit can also modify the text of the generated video. The generation unit can also add new images or sounds to the generated video. The generation unit can also modify parts of the generated video using a generation AI. The generation unit can also add additional information to the generated video using a generation AI. This allows the generation unit to generate videos that align with the user's intentions by modifying parts of the generated video or adding additional information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input parts of the generated video into a generation AI, which can then modify or add information based on that data.

[0035] The verification unit allows the user to review the generated video and make corrections as needed. For example, the verification unit can delete scenes from the generated video. The verification unit can also add effects to the generated video. The verification unit can also modify the text of the generated video. The verification unit can also modify the generated video using a generation AI. The verification unit can also add effects to the generated video using a generation AI. This allows the verification unit to publish a more satisfactory video by allowing the user to review the generated video and make corrections as needed. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the generated video into a generation AI, which can then make corrections or additions based on that information.

[0036] The generation unit can use user-provided information as training data to perform more advanced analysis and generation. For example, the generation unit can use photos and text uploaded by the user as training data. The generation unit uses a generation AI to perform analysis and generation based on the training data. The generation unit can also use the generation AI to improve its algorithm based on the training data. The generation unit can also use the generation AI to expand its dataset based on the training data. As a result, by using user-provided information as training data, the generation unit can improve the performance of its generation AI and generate higher quality videos. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user-provided information into the generation AI, which can then use that information as training data.

[0037] The generation unit can improve the performance of the generation AI and generate higher quality videos. For example, the generation unit can improve the algorithm of the generation AI. The generation unit can also expand the dataset of the generation AI. The generation unit can also improve the analytical capabilities of the generation AI. The generation unit uses the generation AI to generate higher quality videos. The generation unit can also use the generation AI to generate videos that align with the user's intentions. In this way, the generation unit can generate higher quality videos by improving the performance of the generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the algorithm of the generation AI into the generation AI, and the generation AI can improve its performance based on that information.

[0038] The reception desk analyzes the user's past upload history and selects the optimal upload method. The reception desk can analyze the user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has successfully used in the past. The reception desk may also suggest avoiding upload methods that the user has failed to use in the past. The reception desk may also suggest the most effective time of day based on the user's past upload history. In this way, the reception desk can select the optimal upload method by analyzing the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's past upload history into a generating AI, which can then select the optimal upload method based on that information.

[0039] The reception unit filters the uploaded data based on the user's current activities and areas of interest. The reception unit can filter the uploaded data based on the user's current activities and areas of interest. For example, it prioritizes uploading information related to the user's current activities. The reception unit can also filter and upload highly relevant information based on the user's areas of interest. The reception unit can also analyze the user's current activities in real time and upload the most relevant information. This allows the reception unit to provide highly relevant information by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current activities and areas of interest into a generating AI, which can then filter the data based on that information.

[0040] The reception desk prioritizes uploading highly relevant information based on the user's geographical location during the upload process. For example, the reception desk can prioritize uploading information related to the user's current location. It can also filter and upload region-specific information based on the user's geographical location. If the user is traveling, the reception desk can prioritize uploading information related to their travel destination. This allows the reception desk to provide more appropriate information by offering highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI, which can then upload highly relevant information based on that data.

[0041] The reception unit analyzes the user's social media activity and uploads relevant information during the upload process. The reception unit can analyze the user's social media activity and upload relevant information during the upload process. For example, the reception unit can upload relevant information based on information the user has shared on social media. The reception unit can also analyze the user's social media activity history and upload information that might be of interest. The reception unit can also upload relevant information based on information about accounts the user follows. This allows the reception unit to provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generating AI, which can then upload relevant information based on that data.

[0042] The generation unit adjusts the level of detail in the script and video based on the importance of the uploaded information during generation. The generation unit can adjust the level of detail in the script and video based on the importance of the uploaded information during generation. For example, the generation unit can add detailed explanations to important information and generate the script and video. The generation unit can also add concise explanations to less important information and generate the script and video. The generation unit can also add special effects to information that the user particularly wants to emphasize when generating the script and video. In this way, the generation unit can generate more appropriate videos by adjusting the level of detail in the script and video based on the importance of the uploaded information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the importance of the uploaded information into the generation AI, and the generation AI can adjust the level of detail in the script and video based on that information.

[0043] The generation unit applies different generation algorithms depending on the category of the uploaded information during generation. The generation unit can apply different generation algorithms depending on the category of the uploaded information during generation. For example, the generation unit can apply an image analysis algorithm to the photo category to generate scripts and videos. The generation unit can also apply a natural language processing algorithm to the text category to generate scripts and videos. The generation unit can also apply a time management algorithm to the schedule category to generate scripts and videos. In this way, the generation unit can generate more appropriate videos by applying different generation algorithms depending on the category of the uploaded information. 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 category of the uploaded information into a generation AI, and the generation AI can apply different generation algorithms based on that information.

[0044] The generation unit determines the generation priority based on the submission date of the uploaded information during generation. The generation unit can determine the generation priority based on the submission date of the uploaded information during generation. For example, the generation unit will prioritize reflecting the latest information in the script and video. The generation unit can also postpone the generation of scripts and videos for information that is submitted at a later date and is related to a specific event. In this way, the generation unit can generate more appropriate videos by determining the generation priority based on the submission date of the uploaded information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the submission date of the uploaded information into a generation AI, and the generation AI can determine the generation priority based on that information.

[0045] The generation unit adjusts the generation order based on the relevance of the uploaded information during generation. The generation unit can adjust the generation order based on the relevance of the uploaded information during generation. For example, the generation unit prioritizes reflecting highly relevant information in the script and video. The generation unit can also generate the script and video by postponing less relevant information. The generation unit can also build the story flow based on highly relevant information and generate the script and video. In this way, the generation unit can generate more appropriate videos by adjusting the generation order based on the relevance of the uploaded information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the relevance of the uploaded information into a generation AI, and the generation AI can adjust the generation order based on that information.

[0046] The verification unit selects the optimal verification method by referring to the user's past revision history during verification. The verification unit can select the optimal verification method by referring to the user's past revision history during verification. For example, the verification unit proposes the optimal verification method based on the revisions the user has made in the past. The verification unit can also highlight and verify frequently modified parts from the user's past revision history. The verification unit can also analyze the user's past revision history and propose an efficient verification method. In this way, the verification unit can provide the optimal verification method by referring to the user's past revision history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's past revision history into a generating AI, and the generating AI can select the optimal verification method based on that information.

[0047] The verification unit customizes the means of verification based on the user's current activity status during verification. The verification unit can customize the means of verification based on the user's current activity status during verification. For example, if the user is on the move, the verification unit may provide an audio verification method. If the user is doing desk work, the verification unit may also provide a visual verification method. If the user is relaxed, the verification unit may also provide a detailed verification method. In this way, the verification unit can provide a more appropriate verification method by customizing the means of verification based on the user's current activity status. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit may input the user's current activity status into a generating AI, and the generating AI may customize the means of verification based on that information.

[0048] The verification unit selects the optimal verification method based on the user's geographical location information during verification. The verification unit can select the optimal verification method based on the user's geographical location information during verification. For example, the verification unit prioritizes checking information related to the user's current location. The verification unit can also filter and check region-specific information based on the user's geographical location information. If the user is traveling, the verification unit can also prioritize checking information related to the travel destination. In this way, the verification unit can provide a more appropriate verification method by providing the optimal verification method based on the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal verification method based on that information.

[0049] The verification unit analyzes the user's social media activity and proposes verification methods during the verification process. The verification unit can analyze the user's social media activity and propose verification methods during the verification process. For example, the verification unit can verify relevant information based on information shared by the user on social media. The verification unit can also analyze the user's social media activity history and verify information that might be of interest to the user. The verification unit can also verify relevant information based on information from accounts the user follows. In this way, the verification unit can provide a more appropriate verification method by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media activity into a generating AI, and the generating AI can propose verification methods based on that information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can analyze the user's past uploads and suggest the optimal upload format. For example, it can prioritize suggesting upload formats that the user has successfully used in the past. It can also suggest avoiding upload formats that the user has failed to use in the past. It can also suggest the most effective format based on the user's past uploads. In this way, the reception desk can select the optimal upload format by analyzing the user's past uploads. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past uploads into a generating AI, which can then select the optimal upload format based on that information.

[0052] The reception desk can suggest the optimal upload format based on the user's current activities and areas of interest. For example, it can prioritize uploading information related to the user's current activities. It can also filter and upload highly relevant information based on the user's areas of interest. It can also analyze the user's current activities in real time and upload the most relevant information. In this way, the reception desk can provide highly relevant information by suggesting the optimal upload format based on the user's current activities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current activities and areas of interest into a generating AI, which can then suggest the optimal upload format based on that information.

[0053] The reception desk can suggest the optimal upload format based on the user's geographical location. For example, it can prioritize uploading information relevant to the user's current location. It can also filter and upload region-specific information based on the user's geographical location. If the user is traveling, it can prioritize uploading information relevant to their travel destination. This allows the reception desk to provide more appropriate information by suggesting the optimal upload format based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into a generating AI, which can then suggest the optimal upload format based on that information.

[0054] The reception desk can analyze a user's social media activity and suggest the optimal upload format. For example, it can upload relevant information based on what the user has shared on social media. It can also analyze a user's social media activity history and upload information that might be of interest to them. It can also upload relevant information based on the accounts the user follows. In this way, the reception desk can suggest the optimal upload format by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI, which can then suggest the optimal upload format based on that information.

[0055] The reception desk can analyze the user's past upload history and suggest the optimal upload time. For example, it can prioritize suggesting upload times when the user has previously succeeded. It can also suggest avoiding upload times when the user has previously failed. It can also suggest the most effective time slot based on the user's past upload history. In this way, the reception desk can select the optimal upload time by analyzing the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past upload history into a generating AI, which can then select the optimal upload time based on that information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk uploads daily schedules, photos, and thoughts. For example, users can upload photos taken with their smartphones or text they've written like a diary. The reception desk can easily provide information without requiring any special skills or knowledge. Step 2: The generation unit uses a generation AI to analyze the information uploaded by the reception unit and automatically generate scripts and videos. For example, it analyzes photos and text uploaded by the user and constructs a story based on them. The generation unit uses the generation AI to generate a video based on the story. The generation unit can also modify parts of the generated video or add additional information. The generation unit uses the information provided by the user as training data to perform more advanced analysis and generation using the generation AI. The generation unit improves the performance of the generation AI and generates higher quality videos. Step 3: The verification unit allows the user to review and publish the video generated by the generation unit. For example, the user can review the generated video and make corrections as needed. The verification unit can change parts of the generated video or add additional information.

[0058] (Example of form 2) The automated video production system according to an embodiment of the present invention is a system that automates video production using a generation AI and provides a service that allows anyone to easily monetize their work. This system can automatically generate scripts and videos simply by uploading daily schedules, photos, and thoughts. Specifically, first, the user uploads their daily schedule, photos, and thoughts. Next, the generation AI analyzes this information and automatically generates scripts and videos. The generated videos can be easily monetized simply by the user reviewing the content and publishing it. In addition, the personal data obtained is used to enhance the generation AI. For example, the user uploads their daily schedule, photos, and thoughts. At this time, the user does not need any special skills or knowledge to provide information. For example, they can simply upload photos taken with a smartphone or text written like a diary. Next, the generation AI analyzes the uploaded information. Based on the information provided by the user, the generation AI automatically generates a video script. For example, it analyzes the photos and text uploaded by the user and constructs a story based on that. Based on this story, the generation AI generates a video. The generated videos can be easily monetized simply by the user reviewing the content and publishing it. Users can review the generated videos and make modifications as needed. For example, they can change parts of the generated video or add additional information. Ultimately, users can earn advertising revenue by publishing their videos. The personal data collected is also used to enhance the generating AI. The generating AI uses the information provided by users as training data to perform more advanced analysis and generation. This improves the performance of the generating AI, enabling the creation of higher-quality videos. This system significantly lowers the barrier to video production, making it easy for anyone to monetize their content. For example, even beginners with no video production skills can generate professional-looking videos simply by uploading their daily schedules, photos, and thoughts. Furthermore, they can efficiently monetize their content without spending a lot of time on video production. In addition, the enhancement of the generating AI improves the quality of the service and increases user satisfaction.For example, by performing more advanced analysis, the generation AI can create videos that align with the user's intentions. This allows users to publish more satisfying videos, increasing their chances of successful monetization. As a result, automated video production systems allow users to easily monetize their content by simply uploading their daily schedules, photos, and thoughts, and automatically generating scripts and videos.

[0059] The automated video production system according to this embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit allows users to upload their daily schedules, photos, and thoughts. For example, the reception unit can upload photos taken by the user with their smartphone or text written like a diary. The reception unit allows users to easily provide information without requiring any special skills or knowledge. The generation unit uses a generation AI to analyze the information uploaded by the reception unit and automatically generates scripts and videos. For example, the generation unit analyzes photos and text uploaded by the user and constructs a story based on them. The generation unit uses the generation AI to generate a video based on the story. The generation unit can also modify parts of the generated video or add additional information. The generation unit uses the generation AI to utilize the information provided by the user as training data to perform more advanced analysis and generation. The generation unit improves the performance of the generation AI and generates higher quality videos. The confirmation unit allows the user to review and publish the video generated by the generation unit. For example, the confirmation unit can review the generated video and make corrections as needed. The verification unit can modify parts of the generated video or add additional information. This allows the automated video production system according to the embodiment to automatically generate scripts and videos and easily monetize them simply by the user uploading their daily schedule, photos, and thoughts. Some or all of the above-described processes in the reception unit, generation unit, and verification unit may be performed using AI, or not. For example, the reception unit can input photos and text uploaded by the user into the generation AI, which can then analyze this information to generate scripts and videos. The generation unit can use the information provided by the user as training data based on the scripts and videos generated by the generation AI to improve the performance of the generation AI. The verification unit allows the user to review the video generated by the generation AI and make modifications as needed.

[0060] The reception area allows users to upload their daily schedules, photos, and thoughts. For example, users can upload photos taken with their smartphones or text entries written like a diary. The reception area requires no special skills or knowledge, making it easy to provide information. Specifically, users can easily input daily events and thoughts through a dedicated application, attaching photos and video clips. The application features an intuitive interface, designed for user-friendliness. For example, users can use the calendar function to input their daily schedule and record the day's events along with photos. They can also use the voice input function to record their thoughts and feelings and convert them into text. This allows users to easily upload and provide information to the system. Furthermore, the reception area has a function to automatically categorize and tag uploaded information. For example, it can recognize people and places in photos and tag them accordingly, making them easier to search later. It can also analyze text content, extract important keywords, and tag them. This allows for efficient management of user-provided information and save it in a format easily usable by the generation and verification departments.

[0061] The generation unit uses a generation AI to analyze information uploaded by the reception unit and automatically generate scripts and videos. For example, the generation unit analyzes photos and text uploaded by the user and constructs a story based on them. Specifically, the generation AI uses natural language processing technology to understand the content of the text and image recognition technology to analyze the content of photos and video clips. This allows it to create a story outline based on the information provided by the user and generate a script accordingly. For example, the generation AI can construct a travelogue-like story based on photos taken by the user during a trip and their impressions of the time. The generated script is divided into scenes, and each scene includes corresponding video and narration. The generation unit uses the generation AI to generate a video based on the story. Specifically, the generation AI edits the video according to the script and adds narration and music. For example, it can display photos provided by the user in a slideshow format and read the text content aloud as narration. It can also select appropriate background music to create the overall atmosphere of the video. The generation unit can also modify parts of the generated video or add additional information. For example, if a user wants to emphasize a particular scene, they can extend the video of that scene or add special effects. Furthermore, the generation unit uses the information provided by the user as training data to improve the performance of the generation AI. This allows the generation AI to perform more advanced analysis and generation, enabling the creation of higher-quality videos.

[0062] The verification unit allows users to review and publish videos generated by the generation unit. For example, the verification unit can review the generated video and make modifications as needed. Specifically, users can preview the generated video and review the content of each scene, narration, and music. If a user wishes to make modifications, they can use a dedicated editing tool to change parts of the video or add additional information. For example, they can add new photos to specific scenes or change the narration. The verification unit can also modify parts of the generated video or add additional information. For example, if a user wants to emphasize a particular scene, they can extend the video of that scene or add special effects. Furthermore, the verification unit also has a function for publishing the generated video. Users can easily upload the generated video to social media or video sharing sites. This allows users to widely share their memories and daily events and potentially monetize them. The verification unit can improve the overall system performance by collecting user feedback and providing it to the generation and reception units. For example, if a user desires a specific function or effect, they can communicate that request to the generation unit, which can then use it as training data for the generation AI. This allows the verification unit to easily review the generated video, make corrections as needed, and then publish it.

[0063] The generation unit can analyze photos and text uploaded by users and construct a story based on them. For example, the generation unit can analyze photos and text uploaded by users and construct a story plot based on them. The generation unit can use a generation AI to construct a story based on the story plot. The generation unit can also use a generation AI to create character settings and construct a story. The generation unit can also use a generation AI to construct the flow of a story based on information uploaded by users. In this way, the generation unit can generate more concrete videos by analyzing photos and text uploaded by users and constructing a story based on them. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input photos and text uploaded by users into a generation AI, and the generation AI can analyze that information and construct a story.

[0064] The generation unit can modify parts of the generated video or add additional information. For example, the generation unit can add scenes to the generated video. The generation unit can also modify the text of the generated video. The generation unit can also add new images or sounds to the generated video. The generation unit can also modify parts of the generated video using a generation AI. The generation unit can also add additional information to the generated video using a generation AI. This allows the generation unit to generate videos that align with the user's intentions by modifying parts of the generated video or adding additional information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input parts of the generated video into a generation AI, which can then modify or add information based on that data.

[0065] The verification unit allows the user to review the generated video and make corrections as needed. For example, the verification unit can delete scenes from the generated video. The verification unit can also add effects to the generated video. The verification unit can also modify the text of the generated video. The verification unit can also modify the generated video using a generation AI. The verification unit can also add effects to the generated video using a generation AI. This allows the verification unit to publish a more satisfactory video by allowing the user to review the generated video and make corrections as needed. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the generated video into a generation AI, which can then make corrections or additions based on that information.

[0066] The generation unit can use user-provided information as training data to perform more advanced analysis and generation. For example, the generation unit can use photos and text uploaded by the user as training data. The generation unit uses a generation AI to perform analysis and generation based on the training data. The generation unit can also use the generation AI to improve its algorithm based on the training data. The generation unit can also use the generation AI to expand its dataset based on the training data. As a result, by using user-provided information as training data, the generation unit can improve the performance of its generation AI and generate higher quality videos. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user-provided information into the generation AI, which can then use that information as training data.

[0067] The generation unit can improve the performance of the generation AI and generate higher quality videos. For example, the generation unit can improve the algorithm of the generation AI. The generation unit can also expand the dataset of the generation AI. The generation unit can also improve the analytical capabilities of the generation AI. The generation unit uses the generation AI to generate higher quality videos. The generation unit can also use the generation AI to generate videos that align with the user's intentions. In this way, the generation unit can generate higher quality videos by improving the performance of the generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the algorithm of the generation AI into the generation AI, and the generation AI can improve its performance based on that information.

[0068] The reception desk estimates the user's emotions and adjusts the upload timing based on the estimated emotions. The reception desk can estimate the user's emotions and adjust the upload timing based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may encourage them to upload during a time when they can relax. If the user is excited, the reception desk may also encourage them to upload immediately and recommend providing information while their emotions are heightened. If the user is tired, the reception desk may also send a notification encouraging them to upload after resting. This allows the reception desk to provide information at a more appropriate time by adjusting the upload timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can then adjust the upload timing based on that information.

[0069] The reception desk analyzes the user's past upload history and selects the optimal upload method. The reception desk can analyze the user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has successfully used in the past. The reception desk may also suggest avoiding upload methods that the user has failed to use in the past. The reception desk may also suggest the most effective time of day based on the user's past upload history. In this way, the reception desk can select the optimal upload method by analyzing the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's past upload history into a generating AI, which can then select the optimal upload method based on that information.

[0070] The reception unit filters the uploaded data based on the user's current activities and areas of interest. The reception unit can filter the uploaded data based on the user's current activities and areas of interest. For example, it prioritizes uploading information related to the user's current activities. The reception unit can also filter and upload highly relevant information based on the user's areas of interest. The reception unit can also analyze the user's current activities in real time and upload the most relevant information. This allows the reception unit to provide highly relevant information by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current activities and areas of interest into a generating AI, which can then filter the data based on that information.

[0071] The reception unit estimates the user's emotions and determines the priority of information to upload based on the estimated emotions. The reception unit can estimate the user's emotions and determine the priority of information to upload based on the estimated emotions. For example, if the user is excited, the reception unit may prioritize uploading information that evokes heightened emotions. If the user is relaxed, the reception unit may also prioritize uploading calming information. If the user is stressed, the reception unit may also prioritize uploading information that helps reduce stress. This allows the reception unit to provide more appropriate information by prioritizing information to upload based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI, which can then determine the priority of information to upload based on that information.

[0072] The reception desk prioritizes uploading highly relevant information based on the user's geographical location during the upload process. For example, the reception desk can prioritize uploading information related to the user's current location. It can also filter and upload region-specific information based on the user's geographical location. If the user is traveling, the reception desk can prioritize uploading information related to their travel destination. This allows the reception desk to provide more appropriate information by offering highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI, which can then upload highly relevant information based on that data.

[0073] The reception unit analyzes the user's social media activity and uploads relevant information during the upload process. The reception unit can analyze the user's social media activity and upload relevant information during the upload process. For example, the reception unit can upload relevant information based on information the user has shared on social media. The reception unit can also analyze the user's social media activity history and upload information that might be of interest. The reception unit can also upload relevant information based on information about accounts the user follows. This allows the reception unit to provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generating AI, which can then upload relevant information based on that data.

[0074] The generation unit estimates the user's emotions and adjusts the expression of the script and video based on the estimated emotions. The generation unit can estimate the user's emotions and adjust the expression of the script and video based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a script and video in a calm tone. If the user is excited, the generation unit can also generate a script and video using energetic expressions. If the user is sad, the generation unit can also generate a script and video using expressions that are sensitive to those emotions. In this way, the generation unit can generate more appropriate videos by adjusting the expression of the script and video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit inputs user emotion data into the generation AI, which can then adjust the script and video's presentation based on that information.

[0075] The generation unit adjusts the level of detail in the script and video based on the importance of the uploaded information during generation. The generation unit can adjust the level of detail in the script and video based on the importance of the uploaded information during generation. For example, the generation unit can add detailed explanations to important information and generate the script and video. The generation unit can also add concise explanations to less important information and generate the script and video. The generation unit can also add special effects to information that the user particularly wants to emphasize when generating the script and video. In this way, the generation unit can generate more appropriate videos by adjusting the level of detail in the script and video based on the importance of the uploaded information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the importance of the uploaded information into the generation AI, and the generation AI can adjust the level of detail in the script and video based on that information.

[0076] The generation unit applies different generation algorithms depending on the category of the uploaded information during generation. The generation unit can apply different generation algorithms depending on the category of the uploaded information during generation. For example, the generation unit can apply an image analysis algorithm to the photo category to generate scripts and videos. The generation unit can also apply a natural language processing algorithm to the text category to generate scripts and videos. The generation unit can also apply a time management algorithm to the schedule category to generate scripts and videos. In this way, the generation unit can generate more appropriate videos by applying different generation algorithms depending on the category of the uploaded information. 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 category of the uploaded information into a generation AI, and the generation AI can apply different generation algorithms based on that information.

[0077] The generation unit estimates the user's emotions and adjusts the length of the script or video based on the estimated emotions. The generation unit can estimate the user's emotions and adjust the length of the script or video based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise script or video. If the user is relaxed, the generation unit can also generate a longer script or video with detailed explanations. If the user is excited, the generation unit can also generate a script or video with visually stimulating effects. This allows the generation unit to generate more appropriate videos by adjusting the length of the script or video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit inputs user emotion data into the generation AI, which can then adjust the length of the script or video based on that information.

[0078] The generation unit determines the generation priority based on the submission date of the uploaded information during generation. The generation unit can determine the generation priority based on the submission date of the uploaded information during generation. For example, the generation unit will prioritize reflecting the latest information in the script and video. The generation unit can also postpone the generation of scripts and videos for information that is submitted at a later date and is related to a specific event. In this way, the generation unit can generate more appropriate videos by determining the generation priority based on the submission date of the uploaded information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the submission date of the uploaded information into a generation AI, and the generation AI can determine the generation priority based on that information.

[0079] The generation unit adjusts the generation order based on the relevance of the uploaded information during generation. The generation unit can adjust the generation order based on the relevance of the uploaded information during generation. For example, the generation unit prioritizes reflecting highly relevant information in the script and video. The generation unit can also generate the script and video by postponing less relevant information. The generation unit can also build the story flow based on highly relevant information and generate the script and video. In this way, the generation unit can generate more appropriate videos by adjusting the generation order based on the relevance of the uploaded information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the relevance of the uploaded information into a generation AI, and the generation AI can adjust the generation order based on that information.

[0080] The verification unit estimates the user's emotions and adjusts the video review method based on the estimated emotions. The verification unit can estimate the user's emotions and adjust the video review method based on the estimated emotions. For example, if the user is nervous, the verification unit provides a simple and highly visible review method. If the user is relaxed, the verification unit can also provide a review method that includes detailed information. If the user is in a hurry, the verification unit can also provide a review method that gets straight to the point. In this way, the verification unit can provide a more appropriate review method by adjusting the video review method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative 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 verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into a generative AI, and the generative AI can adjust the video review method based on that information.

[0081] The verification unit selects the optimal verification method by referring to the user's past revision history during verification. The verification unit can select the optimal verification method by referring to the user's past revision history during verification. For example, the verification unit proposes the optimal verification method based on the revisions the user has made in the past. The verification unit can also highlight and verify frequently modified parts from the user's past revision history. The verification unit can also analyze the user's past revision history and propose an efficient verification method. In this way, the verification unit can provide the optimal verification method by referring to the user's past revision history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's past revision history into a generating AI, and the generating AI can select the optimal verification method based on that information.

[0082] The verification unit customizes the means of verification based on the user's current activity status during verification. The verification unit can customize the means of verification based on the user's current activity status during verification. For example, if the user is on the move, the verification unit may provide an audio verification method. If the user is doing desk work, the verification unit may also provide a visual verification method. If the user is relaxed, the verification unit may also provide a detailed verification method. In this way, the verification unit can provide a more appropriate verification method by customizing the means of verification based on the user's current activity status. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit may input the user's current activity status into a generating AI, and the generating AI may customize the means of verification based on that information.

[0083] The verification unit estimates the user's emotions and determines the priority of video viewing based on the estimated user emotions. The verification unit can estimate the user's emotions and determine the priority of video viewing based on the estimated user emotions. For example, if the user is excited, the verification unit will prioritize viewing information that evokes heightened emotions. If the user is relaxed, the verification unit may also prioritize viewing calming information. If the user is stressed, the verification unit may also prioritize viewing information that helps reduce stress. In this way, the verification unit can provide a more appropriate viewing method by determining the priority of video viewing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative 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 verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into a generative AI, and the generative AI can determine the priority of video viewing based on that information.

[0084] The verification unit selects the optimal verification method based on the user's geographical location information during verification. The verification unit can select the optimal verification method based on the user's geographical location information during verification. For example, the verification unit prioritizes checking information related to the user's current location. The verification unit can also filter and check region-specific information based on the user's geographical location information. If the user is traveling, the verification unit can also prioritize checking information related to the travel destination. In this way, the verification unit can provide a more appropriate verification method by providing the optimal verification method based on the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal verification method based on that information.

[0085] The verification unit analyzes the user's social media activity and proposes verification methods during the verification process. The verification unit can analyze the user's social media activity and propose verification methods during the verification process. For example, the verification unit can verify relevant information based on information shared by the user on social media. The verification unit can also analyze the user's social media activity history and verify information that might be of interest to the user. The verification unit can also verify relevant information based on information from accounts the user follows. In this way, the verification unit can provide a more appropriate verification method by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media activity into a generating AI, and the generating AI can propose verification methods based on that information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The generation unit can estimate the user's emotions and adjust the video's music and sound effects based on those estimated emotions. For example, if the user is relaxed, calm music can be selected. If the user is excited, energetic music and sound effects can be added. If the user is sad, music that aligns with their emotions can be selected. In this way, the generation unit can create more emotionally resonant videos by adjusting the music and sound effects based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the music and sound effects based on that information.

[0088] The reception desk can analyze the user's past uploads and suggest the optimal upload format. For example, it can prioritize suggesting upload formats that the user has successfully used in the past. It can also suggest avoiding upload formats that the user has failed to use in the past. It can also suggest the most effective format based on the user's past uploads. In this way, the reception desk can select the optimal upload format by analyzing the user's past uploads. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past uploads into a generating AI, which can then select the optimal upload format based on that information.

[0089] The generation unit can estimate the user's emotions and adjust the video's color tone and filters based on the estimated emotions. For example, if the user is relaxed, it can select calm colors and filters. If the user is excited, it can add vibrant colors and filters. If the user is sad, it can select colors and filters that reflect that emotion. In this way, the generation unit can generate more emotionally resonant videos by adjusting the colors and filters based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the colors and filters based on that information.

[0090] The reception desk can suggest the optimal upload format based on the user's current activities and areas of interest. For example, it can prioritize uploading information related to the user's current activities. It can also filter and upload highly relevant information based on the user's areas of interest. It can also analyze the user's current activities in real time and upload the most relevant information. In this way, the reception desk can provide highly relevant information by suggesting the optimal upload format based on the user's current activities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current activities and areas of interest into a generating AI, which can then suggest the optimal upload format based on that information.

[0091] The generation unit can estimate the user's emotions and adjust the video's tempo and rhythm based on those estimated emotions. For example, if the user is relaxed, a calm tempo and rhythm can be selected. If the user is excited, a faster tempo and rhythm can be added. If the user is sad, a tempo and rhythm that aligns with that emotion can be selected. In this way, the generation unit can generate more emotionally resonant videos by adjusting the tempo and rhythm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, 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 using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the tempo and rhythm based on that information.

[0092] The reception desk can suggest the optimal upload format based on the user's geographical location. For example, it can prioritize uploading information relevant to the user's current location. It can also filter and upload region-specific information based on the user's geographical location. If the user is traveling, it can prioritize uploading information relevant to their travel destination. This allows the reception desk to provide more appropriate information by suggesting the optimal upload format based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into a generating AI, which can then suggest the optimal upload format based on that information.

[0093] The generation unit can estimate the user's emotions and adjust the video's narration and subtitles based on the estimated emotions. For example, if the user is relaxed, it can generate narration and subtitles in a calm tone. If the user is excited, it can generate narration and subtitles in an energetic tone. If the user is sad, it can generate narration and subtitles in an emotionally resonant tone. In this way, the generation unit can create more emotionally appealing videos by adjusting the narration and subtitles based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the narration and subtitles based on that information.

[0094] The reception desk can analyze a user's social media activity and suggest the optimal upload format. For example, it can upload relevant information based on what the user has shared on social media. It can also analyze a user's social media activity history and upload information that might be of interest to them. It can also upload relevant information based on the accounts the user follows. In this way, the reception desk can suggest the optimal upload format by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI, which can then suggest the optimal upload format based on that information.

[0095] The generation unit can estimate the user's emotions and adjust the frequency of scene changes in the video based on the estimated user emotions. For example, if the user is relaxed, the scene change frequency can be set low. If the user is excited, the scene change frequency can be set high. If the user is sad, the scene change frequency can be set to reflect that emotion. In this way, the generation unit can generate more emotionally appealing videos by adjusting the scene change frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can adjust the scene change frequency based on that information.

[0096] The reception desk can analyze the user's past upload history and suggest the optimal upload time. For example, it can prioritize suggesting upload times when the user has previously succeeded. It can also suggest avoiding upload times when the user has previously failed. It can also suggest the most effective time slot based on the user's past upload history. In this way, the reception desk can select the optimal upload time by analyzing the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past upload history into a generating AI, which can then select the optimal upload time based on that information.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk uploads daily schedules, photos, and thoughts. For example, users can upload photos taken with their smartphones or text they've written like a diary. The reception desk can easily provide information without requiring any special skills or knowledge. Step 2: The generation unit uses a generation AI to analyze the information uploaded by the reception unit and automatically generate scripts and videos. For example, it analyzes photos and text uploaded by the user and constructs a story based on them. The generation unit uses the generation AI to generate a video based on the story. The generation unit can also modify parts of the generated video or add additional information. The generation unit uses the information provided by the user as training data to perform more advanced analysis and generation using the generation AI. The generation unit improves the performance of the generation AI and generates higher quality videos. Step 3: The verification unit allows the user to review and publish the video generated by the generation unit. For example, the user can review the generated video and make corrections as needed. The verification unit can change parts of the generated video or add additional information.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and confirmation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user uploads their daily schedule, photos, and thoughts. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where it analyzes the uploaded information using a generation AI and automatically generates a script or video. The confirmation unit is implemented by the control unit 46A of the smart device 14, where the user confirms and publishes the generated video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and confirmation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user uploads their daily schedule, photos, and thoughts. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the uploaded information using a generation AI and automatically generates a script or video. The confirmation unit is implemented by the control unit 46A of the smart glasses 214, where the user reviews and publishes the generated video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and confirmation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user uploads their daily schedule, photos, and thoughts. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the uploaded information using a generation AI and automatically generates scripts and videos. The confirmation unit is implemented by the control unit 46A of the headset terminal 314, where the user confirms and publishes the generated video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and confirmation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user uploads their daily schedule, photos, and thoughts. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the uploaded information using a generation AI and automatically generates a script or video. The confirmation unit is implemented by, for example, the control unit 46A of the robot 414, where the user confirms and publishes the generated video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The reception area is where you upload your daily schedule, photos, and thoughts, The aforementioned reception unit analyzes the information uploaded and automatically generates scripts and videos, The system includes a verification unit that allows the user to review and publish the video generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The system analyzes user-uploaded photos and text and builds a story based on them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Modify parts of the generated video or add additional information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned verification unit is The user reviews the generated video and makes corrections as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The information provided by the user is used as training data to perform more advanced analysis and generation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Improve the performance of the generation AI to produce higher quality videos. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the upload timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During the upload process, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During uploads, the system prioritizes uploading highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During the upload process, the system analyzes the user's social media activity and uploads relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the script and video's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in the script and video is adjusted based on the importance of the uploaded information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the category of the uploaded information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the script and video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation priority is determined based on when the uploaded information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation order is adjusted based on the relevance of the uploaded information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned verification unit is It estimates the user's emotions and adjusts how the video is viewed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is During verification, the system will refer to the user's past revision history to select the most appropriate verification method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is During verification, customize the verification method based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is It estimates the user's emotions and determines the priority for viewing videos based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is During verification, the system selects the most suitable verification method based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is During verification, we analyze the user's social media activity and suggest verification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 reception area is where you upload your daily schedule, photos, and thoughts, The aforementioned reception unit analyzes the information uploaded and automatically generates scripts and videos, The system includes a verification unit that allows the user to review and publish the video generated by the generation unit. A system characterized by the following features.

2. The generating unit is The system analyzes user-uploaded photos and text and builds a story based on them. The system according to feature 1.

3. The generating unit is Modify parts of the generated video or add additional information. The system according to feature 1.

4. The aforementioned verification unit is The user reviews the generated video and makes corrections as needed. The system according to feature 1.

5. The generating unit is The information provided by the user is used as training data to perform more advanced analysis and generation. The system according to feature 1.

6. The generating unit is Improve the performance of the generation AI to produce higher quality videos. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the upload timing based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system according to feature 1.

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