system

Abstract

The system according to this embodiment aims to automate the process of monetizing experiences and maximize experiential value and opportunities. [Solution] The system according to the embodiment comprises a shooting unit, an editing unit, a publishing unit, an extraction unit, a provision unit, and a reward unit. The shooting unit shoots the experience. The editing unit edits the video shot by the shooting unit. The publishing unit publishes the video edited by the editing unit. The extraction unit extracts learning data from the video shot by the shooting unit. The provision unit provides the learning data extracted by the extraction unit. The reward unit provides rewards according to viewing.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 is a problem that a process of shooting, editing, and publishing is required to monetize an experience, which requires resources and damages the experience value and opportunities.

[0005] The system according to the embodiment aims to automate the process of monetizing an experience and maximize the experience value and opportunities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a filming unit, an editing unit, a publishing unit, an extraction unit, a provision unit, and a reward unit. The filming unit films the experience. The editing unit edits the video filmed by the filming unit. The publishing unit publishes the video edited by the editing unit. The extraction unit extracts learning data from the video filmed by the filming unit. The provision unit provides the learning data extracted by the extraction unit. The reward unit provides rewards according to the viewing. [Effects of the Invention]

[0007] The system according to this embodiment can automate the process of monetizing experiences and maximize experiential value and opportunities. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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) An agent system according to an embodiment of the present invention is a system for monetizing experiences. This agent system automatically handles everything from shooting and editing to publishing and providing learning data. Specifically, first, the experience is automatically filmed using a dedicated device or smartphone. Next, the agent automatically edits the filmed video and turns it into content. The content is published in a dedicated tab and viewed by viewers. The participant is rewarded based on the number of views. The agent also extracts learning data from the filmed video and provides it to the operating company. When the provided learning data is purchased, a portion of the proceeds is paid to the participant as compensation. This mechanism allows participants to automatically earn revenue simply by fully enjoying their favorite things and hobbies. For example, dedicated devices come in earphone, brooch, hairpin, earring, helmet-mounted, and necklace types, and filming starts simply by wearing them. When using a smartphone or digital camera, filming is done manually. Next, the agent automatically edits the filmed video and turns it into content. The agent checks the content of the video and edits it automatically. Edited videos are published in a dedicated tab, and participants are rewarded for each view. Furthermore, agents extract learning data from the filmed videos. Agents check the video content and automatically extract the learning data. The extracted learning data is presented to the participant for review (optional). The agent then provides the learning data to the operating company. When the provided learning data is purchased, a portion of the proceeds is paid to the participant as a reward. This system allows participants to automatically earn income simply by fully experiencing their favorite things and hobbies. Viewers can also see the participants' experiences immediately, and partner companies can purchase the necessary learning data. As a result, the agent system can automatically handle filming, editing, publishing, extracting, providing, and rewarding experiences.

[0029] The agent system according to this embodiment comprises a shooting unit, an editing unit, a publishing unit, an extraction unit, a provision unit, and a reward unit. The shooting unit shoots the experience. The shooting unit can shoot the experience using, for example, a dedicated device. Dedicated devices include earphone type, brooch type, hairpin type, earring type, helmet-attachable type, and necklace type. For example, with an earphone type device, shooting starts simply by putting it on the ear. With a brooch type device, shooting is performed by attaching it to clothing. With a hairpin type device, shooting is performed by attaching it to the hair. The editing unit edits the video shot by the shooting unit. With an editing unit, for example, checks the content of the video and performs automatic editing. For example, the editing unit performs cut editing to remove unnecessary parts. The editing unit can also add transitions to make the video flow smoothly. Furthermore, the editing unit can adjust the audio to optimize the volume and sound quality. The publishing unit publishes the video edited by the editing unit. With a publishing unit, for example, publishes the video in a dedicated tab. For example, the publishing unit can publish videos on a dedicated tab of the video sharing platform. When viewers watch the videos, the participant is rewarded according to the number of views. The extraction unit extracts learning data from videos shot by the shooting unit. The extraction unit checks the content of the videos and automatically extracts the learning data. For example, the extraction unit extracts specific scenes from the videos and provides them as learning data. The provision unit provides the learning data extracted by the extraction unit. For example, the provision unit provides the learning data to the operating company. When the provided learning data is purchased, a portion of it is paid to the participant as a reward. The reward unit rewards the participant according to the number of views. For example, the reward unit rewards based on the number of views. For example, the reward unit rewards higher rewards for more views. The reward unit can also reward based on viewing time. For example, the reward unit rewards higher rewards for longer viewing times. As a result, the agent system according to the embodiment can automatically shoot, edit, publish, extract, provide, and reward experiences.

[0030] The filming department films the experience. The filming department can film the experience using, for example, dedicated devices. These devices include earphone-type, brooch-type, hairpin-type, earring-type, helmet-mounted, and necklace-type devices. For example, an earphone-type device starts filming simply by being worn in the ear. A brooch-type device starts filming when attached to clothing. A hairpin-type device starts filming when attached to the hair. These devices can record the user's experience in a natural way and can be used as part of everyday life. The filming department acquires high-quality video through these devices and collects data in real time. For example, an earphone-type device starts filming simply by being worn in the ear and can record video from the user's point of view. A brooch-type device, when attached to clothing, films from the user's chest area. A hairpin-type device, when attached to the hair, films from the user's head area. This allows the filming department to record the user's experience from multiple angles and collect rich video data. Furthermore, the filming department can upload the video data collected through these devices to the cloud in real time and share the data in collaboration with other departments. This allows the imaging unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The editorial team edits the videos shot by the camera crew. For example, the editorial team checks the video content and performs automatic editing. For instance, they perform cut editing to remove unnecessary parts. They can also add transitions to smooth the video flow. Furthermore, they can adjust the audio to optimize volume and sound quality. Specifically, the editorial team uses AI to analyze the video content and automatically extract important scenes and highlights. For example, AI detects changes in movement and sound within the video to identify scenes that are interesting to viewers. This allows the editorial team to efficiently create engaging videos for viewers. The editorial team can also adjust the video length and format, providing videos in formats suitable for various platforms. For example, they can shorten videos to create short videos for social media or split longer videos into series for publication. This allows the editorial team to perform flexible video editing to meet viewer needs and improve the overall usability of the system.

[0032] The publishing department publishes videos edited by the editorial department. The publishing department can publish videos in a dedicated tab, for example. When viewers watch a video, the publishing department is rewarded based on the number of views. Specifically, the publishing department manages the video publishing platform and provides videos in an easily accessible format for viewers. For example, the publishing department organizes videos by category, making it easy for viewers to find content that interests them. The publishing department also manages video metadata and can increase video views by performing search engine optimization (SEO). Furthermore, the publishing department can collect viewer feedback and continuously improve the quality and content of videos. For example, it can analyze viewer comments and ratings and incorporate them into future video production. This allows the publishing department to provide engaging content for viewers and improve overall system engagement.

[0033] The extraction unit extracts training data from videos captured by the shooting unit. For example, the extraction unit checks the video content and automatically extracts training data. Specifically, it extracts specific scenes from the video and provides them as training data. More precisely, the extraction unit uses AI to analyze the video content and automatically identify specific scenes and events. For example, the AI ​​recognizes people and objects in the video and extracts specific actions and events. This allows the extraction unit to efficiently collect useful information as training data. Furthermore, the extraction unit organizes the extracted training data and stores it in a database. This allows the training data to be easily searched and used later. In addition, the extraction unit checks the quality of the training data and corrects or supplements the data as needed. This allows the extraction unit to provide high-quality training data and improve the overall learning efficiency of the system.

[0034] The provisioning department provides the training data extracted by the extraction department. For example, the provisioning department provides the training data to the operating company. When the provided training data is purchased, a portion of it is paid to the participants as compensation. Specifically, the provisioning department manages the recipients of the training data and provides the data at the appropriate time. For example, the provisioning department regularly provides training data to operating companies and research institutions and monitors data usage. The provisioning department also manages contracts and licenses related to the provision of training data and clarifies the conditions for data use. This allows the provisioning department to promote the appropriate use of training data and ensure that compensation is paid to participants. Furthermore, the provisioning department can collect feedback on the provision of training data and continuously improve the quality and method of data provision. This allows the provisioning department to optimize the training data provision process and improve the overall efficiency of the system.

[0035] The rewards department rewards participants based on their viewing. For example, the rewards department rewards participants based on the number of views. For example, the rewards department rewards participants with higher rewards for higher views. The rewards department can also reward participants based on viewing time. For example, the rewards department rewards participants with higher rewards for longer viewing times. Specifically, the rewards department collects viewing data in real time and accurately measures the number of views and viewing time. This allows the rewards department to provide participants with fair and transparent rewards based on viewing data. The rewards department can also automate the reward payment process and pay rewards quickly and efficiently. For example, the rewards department calculates the reward amount based on viewing data and automatically transfers the reward to the participant's account. Furthermore, the rewards department can collect feedback on rewards and improve the reward system. This allows the rewards department to provide participants with appropriate incentives and improve overall system engagement.

[0036] The photography team can film experiences using specialized devices such as earphones, brooches, hairpins, earrings, helmet-mounted devices, and necklaces. For example, the photography team can start filming simply by attaching an earphone-type device to their ears. The photography team can also film by attaching a brooch-type device to clothing. The photography team can also film by attaching a hairpin-type device to their hair. This makes filming experiences easier by using specialized devices. These specialized devices include, for example, camera resolution, battery life, and attachment methods. For example, the earphone-type device has a built-in high-resolution camera and can film for extended periods. The brooch-type device is lightweight and comfortable to wear for long periods. The hairpin-type device allows for discreet filming. This allows the photography team to film experiences using a variety of specialized devices.

[0037] The editorial team can check the video content and perform automatic editing. For example, the editorial team can analyze the video content and automatically cut out unnecessary parts. The editorial team can also automatically add transitions to make the video flow more smoothly. The editorial team can also automatically adjust the audio to optimize volume and sound quality. This streamlines the editing process by allowing the editorial team to check the video content and perform automatic editing. Automatic editing includes, for example, cut editing, adding transitions, and adjusting audio. For example, cut editing automatically removes unnecessary parts of the video. Adding transitions makes scene changes smoother. Adjusting audio optimizes volume and sound quality. This allows the editorial team to check the video content and perform automatic editing.

[0038] The public section can publish edited videos in a dedicated tab for video sharing platforms. For example, the public section can publish edited videos in a dedicated tab for video sharing platforms, allowing viewers to view the videos. This makes it easier for viewers to view the videos by publishing them in a dedicated tab for video sharing platforms. The dedicated tab for video sharing platforms includes, for example, how the tab is displayed and how it is accessed. For example, the dedicated tab for video sharing platforms is designed to be easily accessible to viewers. The way the tab is displayed is designed to make it easy for viewers to find the videos. This allows the public section to publish edited videos in a dedicated tab for video sharing platforms.

[0039] The extraction unit can check the content of a video and automatically extract training data. For example, the extraction unit can analyze the content of a video and automatically extract specific scenes. For example, the extraction unit can automatically extract important information from a video. This makes the extraction of training data more efficient by checking the content of the video and automatically extracting training data. The extraction of training data includes, for example, data filtering methods and extraction algorithms. For example, data filtering methods remove unnecessary data. Extraction algorithms extract important data. This allows the extraction unit to check the content of a video and automatically extract training data.

[0040] The provider can provide the extracted training data to the LLM operating company. For example, the provider can provide the extracted training data to the LLM operating company, and the operating company can purchase the training data. This promotes the utilization of the training data by providing the extracted training data to the LLM operating company. The LLM operating company's requirements include, for example, the method of data transfer and the purpose of use. For example, the data transfer method should be secure. The purpose of use should relate to the utilization of the training data. This allows the provider to provide the extracted training data to the LLM operating company.

[0041] The rewards department can reward participants based on their viewing habits. For example, the rewards department can reward participants based on the number of views. The rewards department can also reward participants based on their viewing time. This increases participant motivation by rewarding them based on their viewing habits. Rewards include, for example, monetary rewards, point rewards, and perks. For example, monetary rewards are paid based on the number of views or viewing time. Point rewards are awarded based on the number of views or viewing time. Perks are offered based on the number of views or viewing time. This allows the rewards department to reward participants based on their viewing habits.

[0042] The camera unit can analyze the user's past experience history during shooting and select the optimal shooting method. For example, the camera unit prioritizes shooting angles that the user has preferred to use in the past. The camera unit can also refer to the style of videos the user has shot in the past. The camera unit can also automatically select the optimal shooting settings based on the user's past experience history. This allows the camera unit to select the optimal shooting method by analyzing the user's past experience history. Past experience history includes, for example, past video data and behavioral logs. For example, past video data shows the content and style of videos the user has shot in the past. Behavioral logs show the user's past behavior and preferences. This allows the camera unit to analyze the user's past experience history during shooting and select the optimal shooting method.

[0043] The camera unit can automatically adjust shooting settings based on the user's current activity and environment during shooting. For example, if the user is outdoors, the camera unit automatically adjusts brightness and exposure. If the user is indoors, the camera unit adjusts the white balance to match the lighting. If the user is moving, the camera unit enhances image stabilization. This enables optimal shooting by automatically adjusting shooting settings based on the user's current activity and environment. Current activity and environment include, for example, GPS data and sensor data. For example, GPS data indicates the user's current location. Sensor data indicates the user's movement and surrounding environment. This allows the camera unit to automatically adjust shooting settings based on the user's current activity and environment during shooting.

[0044] The camera crew can prioritize capturing scenes that are highly relevant to the user's geographical location during shooting. For example, if the user is in a tourist area, the camera crew will prioritize capturing tourist attractions. If the user is at an event venue, the camera crew will prioritize capturing the highlights of the event. If the user is in nature, the camera crew will prioritize capturing beautiful scenery. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location by considering the user's location. Geographical location information includes, for example, GPS data and location services. For example, GPS data shows the user's current location. Location services provide relevant information based on the user's location. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location during shooting.

[0045] The photography team can analyze the user's social media activity during filming and capture relevant scenes. For example, the photography team can prioritize filming content that the user wants to share on social media. For example, the photography team can prioritize filming scenes that the user's followers are likely to be interested in. For example, the photography team can select scenes to film by referring to content the user has previously posted. This allows the photography team to capture relevant scenes by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of comments. For example, the content of posts refers to what the user has posted on social media. The number of likes refers to the number of reactions to a post. The number of comments refers to the number of comments on a post. This allows the photography team to analyze the user's social media activity during filming and capture relevant scenes.

[0046] The editorial team can adjust the level of detail in editing based on the importance of the video. For example, the editorial team will edit important scenes in detail, while simplifying less important scenes. They can also adjust the editing time according to importance. This allows for more efficient editing by adjusting the level of detail based on the importance of the video. Factors contributing to video importance include, for example, the number of views, user ratings, and the importance of the content. For example, the number of views indicates how many times the video has been watched. User ratings indicate user reactions to the video. The importance of the content is assessed based on the content and context of the video. This allows the editorial team to adjust the level of detail in editing based on the importance of the video.

[0047] The editorial team can apply different editing algorithms depending on the video category during editing. For example, the editorial team will apply an action-oriented editing algorithm to action videos. For example, the editorial team will apply a documentary-oriented editing algorithm to documentary videos. For example, the editorial team will apply a comedy-oriented editing algorithm to comedy videos. This allows for optimal editing by applying different editing algorithms depending on the video category. Editing algorithms include, for example, cut editing algorithms and transition algorithms. For example, a cut editing algorithm automatically removes unnecessary parts of a video. A transition algorithm makes scene transitions smoother. This allows the editorial team to apply different editing algorithms depending on the video category during editing.

[0048] The publishing department can analyze the video's viewing history at the time of publication and select the optimal publishing method. For example, the publishing department prioritizes publishing methods preferred by viewers. For example, the publishing department can select the optimal publishing time based on viewing history. For example, the publishing department can adjust the publishing method by analyzing viewer reactions. In this way, the optimal publishing method can be selected by analyzing the video's viewing history. Viewing history includes, for example, the number of views, viewing time, and viewer reactions. For example, the number of views indicates how many times the video was watched. Viewing time indicates how long the video was watched. Viewer reactions indicate viewers' ratings and comments on the video. In this way, the publishing department can analyze the video's viewing history at the time of publication and select the optimal publishing method.

[0049] The publishing department can apply different publishing algorithms depending on the video category at the time of publication. For example, the publishing department applies an action-oriented publishing algorithm to action videos. For example, the publishing department applies a documentary-oriented publishing algorithm to documentary videos. For example, the publishing department applies a comedy-oriented publishing algorithm to comedy videos. This allows for optimal publishing by applying different publishing algorithms depending on the video category. Publishing algorithms include, for example, targeting algorithms and optimization algorithms. For example, a targeting algorithm publishes videos to a specific audience. An optimization algorithm adjusts the publishing method based on audience reactions. This allows the publishing department to apply different publishing algorithms depending on the video category at the time of publication.

[0050] The extraction unit can improve the accuracy of extraction by considering the interrelationships between videos during the extraction process. For example, the extraction unit can extract data from related videos in a batch. The extraction unit can analyze the interrelationships between videos and extract highly relevant data. The extraction unit can optimize the data extraction order by considering the interrelationships between videos. This improves the accuracy of extraction by considering the interrelationships between videos. Video interrelationships include, for example, related videos, series videos, and common themes. For example, related videos refer to videos related to the same theme or content. Series videos refer to videos that are published consecutively. Common themes refer to videos created based on the same theme. This allows the extraction unit to improve the accuracy of extraction by considering the interrelationships between videos during the extraction process.

[0051] The extraction unit can perform extraction while considering the attribute information of the video's photographer. For example, the extraction unit can extract data based on the photographer's age and gender. For example, the extraction unit can extract data based on the photographer's interests. For example, the extraction unit can extract data by referring to the photographer's past shooting history. This allows for the extraction of more relevant data by considering the attribute information of the video's photographer. The photographer's attribute information includes, for example, age, gender, and interests. For example, age indicates the photographer's age group. Gender indicates the photographer's gender. Interests indicate the photographer's interests. This allows the extraction unit to perform extraction while considering the attribute information of the video's photographer.

[0052] The extraction unit can perform extraction while considering the geographical distribution of videos. For example, the extraction unit can prioritize extracting video data from a specific region. For example, the extraction unit can extract geographically related video data in bulk. For example, the extraction unit can optimize the data extraction order while considering geographical distribution. This allows for the extraction of more relevant data by considering the geographical distribution of videos. Geographical distribution includes, for example, data by region and data by country. For example, data by region indicates videos related to a specific region. Data by country indicates videos related to a specific country. This allows the extraction unit to perform extraction while considering the geographical distribution of videos.

[0053] The extraction unit can improve the accuracy of its extraction by referring to related literature for the video during the extraction process. For example, the extraction unit extracts data that complements the video content based on related literature. The extraction unit optimizes the data extraction method by referring to related literature, for example. The extraction unit adjusts the data extraction order based on related literature, for example. This improves the accuracy of the extraction by referring to related literature for the video. Related literature includes, for example, academic papers, technical reports, and patent documents. For example, academic papers provide information based on specific research or investigations. Technical reports provide information on specific technologies or methods. Patent documents provide information on specific inventions or technologies. This allows the extraction unit to improve the accuracy of its extraction by referring to related literature for the video during the extraction process.

[0054] The data provider can improve the accuracy of its delivery by considering the interrelationships between data. For example, the provider can provide related data in a single batch. The provider can, for example, analyze the interrelationships between data and provide highly relevant data. The provider can, for example, optimize the order of delivery by considering the interrelationships between data. This improves the accuracy of delivery by considering the interrelationships between data. Data interrelationships include, for example, related data and data dependencies. For example, related data refers to data related to the same theme or content. Data dependencies indicate that certain data depend on other data. This allows the provider to improve the accuracy of its delivery by considering the interrelationships between data when providing it.

[0055] The data provider can provide data while considering the data provider's attribute information. For example, the data provider can provide data based on the provider's age and gender. For example, the data provider can provide data based on the provider's interests and concerns. For example, the data provider can provide data while referring to the provider's past data provision history. This allows the data provider to provide more relevant data by considering the data provider's attribute information. The provider's attribute information includes, for example, age, gender, and field of expertise. For example, age indicates the provider's age group. Gender indicates the provider's gender. Field of expertise indicates the provider's professional knowledge and experience. This allows the data provider to provide data while considering the data provider's attribute information.

[0056] The data provider can provide data while considering its geographical distribution. For example, the provider can prioritize providing data from a specific region. For example, the provider can provide geographically related data in a group. For example, the provider can optimize the order in which data is provided while considering its geographical distribution. This allows the provider to provide more relevant data by considering the geographical distribution of the data. Geographical distribution includes, for example, regional data and country-specific data. For example, regional data represents data related to a specific region. Country-specific data represents data related to a specific country. This allows the provider to provide data while considering its geographical distribution.

[0057] The data provider can improve the accuracy of the data delivery by referring to relevant literature at the time of delivery. For example, the data provider can supplement the content of the data based on relevant literature. For example, the data provider can optimize the method of data delivery by referring to relevant literature. For example, the data provider can adjust the order of data delivery based on relevant literature. As a result, the accuracy of the data delivery is improved by referring to relevant literature. Relevant literature includes, for example, academic papers, technical reports, and patent documents. For example, academic papers provide information based on specific research or investigations. Technical reports provide information on specific technologies or methods. Patent documents provide information on specific inventions or technologies. As a result, the data provider can improve the accuracy of the data delivery by referring to relevant literature at the time of delivery.

[0058] The rewards department can analyze viewing history to select the optimal reward distribution method when distributing rewards. For example, the rewards department prioritizes reward distribution methods preferred by viewers. For example, the rewards department selects the optimal timing for reward distribution based on viewing history. For example, the rewards department adjusts reward distribution methods by analyzing viewer reactions. In this way, the optimal reward distribution method can be selected by analyzing viewing history. Viewing history includes, for example, the number of views, viewing time, and viewer reactions. For example, the number of views indicates how many times the video was watched. Viewing time indicates how long the video was watched. Viewer reactions indicate viewers' evaluations and comments on the video. In this way, the rewards department can analyze viewing history to select the optimal reward distribution method when distributing rewards.

[0059] The rewards unit can award rewards by considering viewer attribute information when awarding rewards. For example, the rewards unit can award rewards based on the viewer's age and gender. For example, the rewards unit can award rewards based on the viewer's interests and preferences. For example, the rewards unit can award rewards by referring to the viewer's past viewing history. This allows for the awarding of more relevant rewards by considering viewer attribute information. Viewer attribute information includes, for example, age, gender, and interests. For example, age indicates the viewer's age group. Gender indicates the viewer's gender. Interests indicate the viewer's interests and preferences. This allows the rewards unit to award rewards by considering viewer attribute information when awarding rewards.

[0060] The rewards department can select the optimal reward method when awarding rewards, taking into account the viewer's geographical location information. For example, the rewards department can award rewards that are aligned with trends in the viewer's region. For example, the rewards department can award rewards related to the viewer's geographical location. For example, the rewards department can award rewards that are aligned with events in the viewer's region. This allows the rewards department to select the optimal reward method by considering the viewer's geographical location information. Geographical location information includes, for example, GPS data and location services. For example, GPS data indicates the viewer's current location. Location services provide relevant information based on the viewer's location. This allows the rewards department to select the optimal reward method when awarding rewards, taking into account the viewer's geographical location information.

[0061] The rewards department can analyze viewers' social media activity and adjust reward distribution methods when awarding rewards. For example, the rewards department can award rewards tailored to content viewers want to share on social media. For example, the rewards department can award rewards that are likely to interest the viewer's followers. For example, the rewards department can award rewards based on the viewer's past social media activity. This allows the department to select the optimal reward distribution method by analyzing viewers' social media activity. Social media activity includes, for example, post content, number of likes, and number of comments. For example, post content refers to what viewers have posted on social media. The number of likes refers to the number of reactions to a post. The number of comments refers to the number of comments on a post. This allows the rewards department to analyze viewers' social media activity and adjust reward distribution methods when awarding rewards.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The camera unit can analyze the user's past experience history during shooting and select the optimal shooting method. For example, the camera unit prioritizes shooting angles that the user has preferred to use in the past. The camera unit can also refer to the style of videos the user has shot in the past. The camera unit can also automatically select the optimal shooting settings based on the user's past experience history. This allows the camera unit to select the optimal shooting method by analyzing the user's past experience history. Past experience history includes, for example, past video data and behavioral logs. For example, past video data shows the content and style of videos the user has shot in the past. Behavioral logs show the user's past behavior and preferences. This allows the camera unit to analyze the user's past experience history during shooting and select the optimal shooting method.

[0064] The camera unit can automatically adjust shooting settings based on the user's current activity and environment during shooting. For example, if the user is outdoors, the camera unit automatically adjusts brightness and exposure. If the user is indoors, the camera unit adjusts the white balance to match the lighting. If the user is moving, the camera unit enhances image stabilization. This enables optimal shooting by automatically adjusting shooting settings based on the user's current activity and environment. Current activity and environment include, for example, GPS data and sensor data. For example, GPS data indicates the user's current location. Sensor data indicates the user's movement and surrounding environment. This allows the camera unit to automatically adjust shooting settings based on the user's current activity and environment during shooting.

[0065] The camera crew can prioritize capturing scenes that are highly relevant to the user's geographical location during shooting. For example, if the user is in a tourist area, the camera crew will prioritize capturing tourist attractions. If the user is at an event venue, the camera crew will prioritize capturing the highlights of the event. If the user is in nature, the camera crew will prioritize capturing beautiful scenery. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location by considering the user's location. Geographical location information includes, for example, GPS data and location services. For example, GPS data shows the user's current location. Location services provide relevant information based on the user's location. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location during shooting.

[0066] The editorial team can adjust the level of detail in editing based on the importance of the video. For example, the editorial team will edit important scenes in detail, while simplifying less important scenes. They can also adjust the editing time according to importance. This allows for more efficient editing by adjusting the level of detail based on the importance of the video. Factors contributing to video importance include, for example, the number of views, user ratings, and the importance of the content. For example, the number of views indicates how many times the video has been watched. User ratings indicate user reactions to the video. The importance of the content is assessed based on the content and context of the video. This allows the editorial team to adjust the level of detail in editing based on the importance of the video.

[0067] The editorial team can apply different editing algorithms depending on the video category during editing. For example, the editorial team will apply an action-oriented editing algorithm to action videos. For example, the editorial team will apply a documentary-oriented editing algorithm to documentary videos. For example, the editorial team will apply a comedy-oriented editing algorithm to comedy videos. This allows for optimal editing by applying different editing algorithms depending on the video category. Editing algorithms include, for example, cut editing algorithms and transition algorithms. For example, a cut editing algorithm automatically removes unnecessary parts of a video. A transition algorithm makes scene transitions smoother. This allows the editorial team to apply different editing algorithms depending on the video category during editing.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The filming team films the experience. The filming team can film the experience using, for example, a dedicated device. Dedicated devices include earphone type, brooch type, hairpin type, earring type, helmet-attachable type, and necklace type. For example, with an earphone type device, filming starts simply by putting it in the ear. With a brooch type device, filming starts when it is attached to clothing. With a hairpin type device, filming starts when it is attached to the hair. Step 2: The editorial team edits the video shot by the camera crew. The editorial team checks the video content and performs automatic editing. For example, the editorial team performs cut editing to remove unnecessary parts. The editorial team can also add transitions to make the video flow more smoothly. Furthermore, the editorial team can adjust the audio to optimize the volume and sound quality. Step 3: The publishing team publishes the video edited by the editorial team. The publishing team publishes the video, for example, in a dedicated tab. For example, the publishing team can publish the video in a dedicated tab on a video sharing platform. Step 4: The extraction unit extracts training data from the video captured by the shooting unit. The extraction unit checks the content of the video and automatically extracts training data. For example, the extraction unit extracts specific scenes from the video and provides them as training data. Step 5: The provisioning unit provides the training data extracted by the extraction unit. For example, the provisioning unit provides the training data to the operating company. When the provided training data is purchased, a portion of it is paid to the participant as compensation. Step 6: The rewards unit rewards participants based on their viewing. For example, the rewards unit rewards participants based on the number of views. For example, the rewards unit rewards participants with higher rewards for more views. The rewards unit can also reward participants based on their viewing time. For example, the rewards unit rewards participants with higher rewards for longer viewing times.

[0070] (Example of form 2) An agent system according to an embodiment of the present invention is a system for monetizing experiences. This agent system automatically handles everything from shooting and editing to publishing and providing learning data. Specifically, first, the experience is automatically filmed using a dedicated device or smartphone. Next, the agent automatically edits the filmed video and turns it into content. The content is published in a dedicated tab and viewed by viewers. The participant is rewarded based on the number of views. The agent also extracts learning data from the filmed video and provides it to the operating company. When the provided learning data is purchased, a portion of the proceeds is paid to the participant as compensation. This mechanism allows participants to automatically earn revenue simply by fully enjoying their favorite things and hobbies. For example, dedicated devices come in earphone, brooch, hairpin, earring, helmet-mounted, and necklace types, and filming starts simply by wearing them. When using a smartphone or digital camera, filming is done manually. Next, the agent automatically edits the filmed video and turns it into content. The agent checks the content of the video and edits it automatically. Edited videos are published in a dedicated tab, and participants are rewarded for each view. Furthermore, agents extract learning data from the filmed videos. Agents check the video content and automatically extract the learning data. The extracted learning data is presented to the participant for review (optional). The agent then provides the learning data to the operating company. When the provided learning data is purchased, a portion of the proceeds is paid to the participant as a reward. This system allows participants to automatically earn income simply by fully experiencing their favorite things and hobbies. Viewers can also see the participants' experiences immediately, and partner companies can purchase the necessary learning data. As a result, the agent system can automatically handle filming, editing, publishing, extracting, providing, and rewarding experiences.

[0071] The agent system according to this embodiment comprises a shooting unit, an editing unit, a publishing unit, an extraction unit, a provision unit, and a reward unit. The shooting unit shoots the experience. The shooting unit can shoot the experience using, for example, a dedicated device. Dedicated devices include earphone type, brooch type, hairpin type, earring type, helmet-attachable type, and necklace type. For example, with an earphone type device, shooting starts simply by putting it on the ear. With a brooch type device, shooting is performed by attaching it to clothing. With a hairpin type device, shooting is performed by attaching it to the hair. The editing unit edits the video shot by the shooting unit. With an editing unit, for example, checks the content of the video and performs automatic editing. For example, the editing unit performs cut editing to remove unnecessary parts. The editing unit can also add transitions to make the video flow smoothly. Furthermore, the editing unit can adjust the audio to optimize the volume and sound quality. The publishing unit publishes the video edited by the editing unit. With a publishing unit, for example, publishes the video in a dedicated tab. For example, the publishing unit can publish videos on a dedicated tab of the video sharing platform. When viewers watch the videos, the participant is rewarded according to the number of views. The extraction unit extracts learning data from videos shot by the shooting unit. The extraction unit checks the content of the videos and automatically extracts the learning data. For example, the extraction unit extracts specific scenes from the videos and provides them as learning data. The provision unit provides the learning data extracted by the extraction unit. For example, the provision unit provides the learning data to the operating company. When the provided learning data is purchased, a portion of it is paid to the participant as a reward. The reward unit rewards the participant according to the number of views. For example, the reward unit rewards based on the number of views. For example, the reward unit rewards higher rewards for more views. The reward unit can also reward based on viewing time. For example, the reward unit rewards higher rewards for longer viewing times. As a result, the agent system according to the embodiment can automatically shoot, edit, publish, extract, provide, and reward experiences.

[0072] The filming department films the experience. The filming department can film the experience using, for example, dedicated devices. These devices include earphone-type, brooch-type, hairpin-type, earring-type, helmet-mounted, and necklace-type devices. For example, an earphone-type device starts filming simply by being worn in the ear. A brooch-type device starts filming when attached to clothing. A hairpin-type device starts filming when attached to the hair. These devices can record the user's experience in a natural way and can be used as part of everyday life. The filming department acquires high-quality video through these devices and collects data in real time. For example, an earphone-type device starts filming simply by being worn in the ear and can record video from the user's point of view. A brooch-type device, when attached to clothing, films from the user's chest area. A hairpin-type device, when attached to the hair, films from the user's head area. This allows the filming department to record the user's experience from multiple angles and collect rich video data. Furthermore, the filming department can upload the video data collected through these devices to the cloud in real time and share the data in collaboration with other departments. This allows the imaging unit to collect data efficiently and effectively, improving the overall performance of the system.

[0073] The editorial team edits the videos shot by the camera crew. For example, the editorial team checks the video content and performs automatic editing. For instance, they perform cut editing to remove unnecessary parts. They can also add transitions to smooth the video flow. Furthermore, they can adjust the audio to optimize volume and sound quality. Specifically, the editorial team uses AI to analyze the video content and automatically extract important scenes and highlights. For example, AI detects changes in movement and sound within the video to identify scenes that are interesting to viewers. This allows the editorial team to efficiently create engaging videos for viewers. The editorial team can also adjust the video length and format, providing videos in formats suitable for various platforms. For example, they can shorten videos to create short videos for social media or split longer videos into series for publication. This allows the editorial team to perform flexible video editing to meet viewer needs and improve the overall usability of the system.

[0074] The publishing department publishes videos edited by the editorial department. The publishing department can publish videos in a dedicated tab, for example. When viewers watch a video, the publishing department is rewarded based on the number of views. Specifically, the publishing department manages the video publishing platform and provides videos in an easily accessible format for viewers. For example, the publishing department organizes videos by category, making it easy for viewers to find content that interests them. The publishing department also manages video metadata and can increase video views by performing search engine optimization (SEO). Furthermore, the publishing department can collect viewer feedback and continuously improve the quality and content of videos. For example, it can analyze viewer comments and ratings and incorporate them into future video production. This allows the publishing department to provide engaging content for viewers and improve overall system engagement.

[0075] The extraction unit extracts training data from videos captured by the shooting unit. For example, the extraction unit checks the video content and automatically extracts training data. Specifically, it extracts specific scenes from the video and provides them as training data. More precisely, the extraction unit uses AI to analyze the video content and automatically identify specific scenes and events. For example, the AI ​​recognizes people and objects in the video and extracts specific actions and events. This allows the extraction unit to efficiently collect useful information as training data. Furthermore, the extraction unit organizes the extracted training data and stores it in a database. This allows the training data to be easily searched and used later. In addition, the extraction unit checks the quality of the training data and corrects or supplements the data as needed. This allows the extraction unit to provide high-quality training data and improve the overall learning efficiency of the system.

[0076] The provisioning department provides the training data extracted by the extraction department. For example, the provisioning department provides the training data to the operating company. When the provided training data is purchased, a portion of it is paid to the participants as compensation. Specifically, the provisioning department manages the recipients of the training data and provides the data at the appropriate time. For example, the provisioning department regularly provides training data to operating companies and research institutions and monitors data usage. The provisioning department also manages contracts and licenses related to the provision of training data and clarifies the conditions for data use. This allows the provisioning department to promote the appropriate use of training data and ensure that compensation is paid to participants. Furthermore, the provisioning department can collect feedback on the provision of training data and continuously improve the quality and method of data provision. This allows the provisioning department to optimize the training data provision process and improve the overall efficiency of the system.

[0077] The rewards department rewards participants based on their viewing. For example, the rewards department rewards participants based on the number of views. For example, the rewards department rewards participants with higher rewards for higher views. The rewards department can also reward participants based on viewing time. For example, the rewards department rewards participants with higher rewards for longer viewing times. Specifically, the rewards department collects viewing data in real time and accurately measures the number of views and viewing time. This allows the rewards department to provide participants with fair and transparent rewards based on viewing data. The rewards department can also automate the reward payment process and pay rewards quickly and efficiently. For example, the rewards department calculates the reward amount based on viewing data and automatically transfers the reward to the participant's account. Furthermore, the rewards department can collect feedback on rewards and improve the reward system. This allows the rewards department to provide participants with appropriate incentives and improve overall system engagement.

[0078] The photography team can film experiences using specialized devices such as earphones, brooches, hairpins, earrings, helmet-mounted devices, and necklaces. For example, the photography team can start filming simply by attaching an earphone-type device to their ears. The photography team can also film by attaching a brooch-type device to clothing. The photography team can also film by attaching a hairpin-type device to their hair. This makes filming experiences easier by using specialized devices. These specialized devices include, for example, camera resolution, battery life, and attachment methods. For example, the earphone-type device has a built-in high-resolution camera and can film for extended periods. The brooch-type device is lightweight and comfortable to wear for long periods. The hairpin-type device allows for discreet filming. This allows the photography team to film experiences using a variety of specialized devices.

[0079] The editorial team can check the video content and perform automatic editing. For example, the editorial team can analyze the video content and automatically cut out unnecessary parts. The editorial team can also automatically add transitions to make the video flow more smoothly. The editorial team can also automatically adjust the audio to optimize volume and sound quality. This streamlines the editing process by allowing the editorial team to check the video content and perform automatic editing. Automatic editing includes, for example, cut editing, adding transitions, and adjusting audio. For example, cut editing automatically removes unnecessary parts of the video. Adding transitions makes scene changes smoother. Adjusting audio optimizes volume and sound quality. This allows the editorial team to check the video content and perform automatic editing.

[0080] The public section can publish edited videos in a dedicated tab for video sharing platforms. For example, the public section can publish edited videos in a dedicated tab for video sharing platforms, allowing viewers to view the videos. This makes it easier for viewers to view the videos by publishing them in a dedicated tab for video sharing platforms. The dedicated tab for video sharing platforms includes, for example, how the tab is displayed and how it is accessed. For example, the dedicated tab for video sharing platforms is designed to be easily accessible to viewers. The way the tab is displayed is designed to make it easy for viewers to find the videos. This allows the public section to publish edited videos in a dedicated tab for video sharing platforms.

[0081] The extraction unit can check the content of a video and automatically extract training data. For example, the extraction unit can analyze the content of a video and automatically extract specific scenes. For example, the extraction unit can automatically extract important information from a video. This makes the extraction of training data more efficient by checking the content of the video and automatically extracting training data. The extraction of training data includes, for example, data filtering methods and extraction algorithms. For example, data filtering methods remove unnecessary data. Extraction algorithms extract important data. This allows the extraction unit to check the content of a video and automatically extract training data.

[0082] The provider can provide the extracted training data to the LLM operating company. For example, the provider can provide the extracted training data to the LLM operating company, and the operating company can purchase the training data. This promotes the utilization of the training data by providing the extracted training data to the LLM operating company. The LLM operating company's requirements include, for example, the method of data transfer and the purpose of use. For example, the data transfer method should be secure. The purpose of use should relate to the utilization of the training data. This allows the provider to provide the extracted training data to the LLM operating company.

[0083] The rewards department can reward participants based on their viewing habits. For example, the rewards department can reward participants based on the number of views. The rewards department can also reward participants based on their viewing time. This increases participant motivation by rewarding them based on their viewing habits. Rewards include, for example, monetary rewards, point rewards, and perks. For example, monetary rewards are paid based on the number of views or viewing time. Point rewards are awarded based on the number of views or viewing time. Perks are offered based on the number of views or viewing time. This allows the rewards department to reward participants based on their viewing habits.

[0084] The camera unit can estimate the user's emotions and adjust the start time of shooting based on the estimated emotions. For example, if the camera unit is excited, it will start shooting immediately. If the camera unit is relaxed, it will start shooting at a natural timing. If the camera unit is tense, it will wait until the user is relaxed before shooting. By adjusting the start time of shooting based on the user's emotions, more natural shooting becomes possible. The user's emotions are estimated by methods such as facial recognition, voice analysis, and behavioral analysis. For example, facial recognition estimates emotions by analyzing the user's facial expressions. Voice analysis estimates emotions by analyzing the tone and speed of the user's voice. Behavioral analysis estimates emotions by analyzing the user's movements and posture. This allows the camera unit to estimate the user's emotions and adjust the start time of shooting based on the estimated emotions.

[0085] The camera unit can analyze the user's past experience history during shooting and select the optimal shooting method. For example, the camera unit prioritizes shooting angles that the user has preferred to use in the past. The camera unit can also refer to the style of videos the user has shot in the past. The camera unit can also automatically select the optimal shooting settings based on the user's past experience history. This allows the camera unit to select the optimal shooting method by analyzing the user's past experience history. Past experience history includes, for example, past video data and behavioral logs. For example, past video data shows the content and style of videos the user has shot in the past. Behavioral logs show the user's past behavior and preferences. This allows the camera unit to analyze the user's past experience history during shooting and select the optimal shooting method.

[0086] The camera unit can automatically adjust shooting settings based on the user's current activity and environment during shooting. For example, if the user is outdoors, the camera unit automatically adjusts brightness and exposure. If the user is indoors, the camera unit adjusts the white balance to match the lighting. If the user is moving, the camera unit enhances image stabilization. This enables optimal shooting by automatically adjusting shooting settings based on the user's current activity and environment. Current activity and environment include, for example, GPS data and sensor data. For example, GPS data indicates the user's current location. Sensor data indicates the user's movement and surrounding environment. This allows the camera unit to automatically adjust shooting settings based on the user's current activity and environment during shooting.

[0087] The filming team can estimate the user's emotions and prioritize scenes to film based on those estimates. For example, if the user is excited, the filming team will prioritize action scenes. If the user is relaxed, the filming team will prioritize landscapes and quiet scenes. If the user is moved, the filming team will prioritize scenes that evoke emotion. By prioritizing scenes based on the user's emotions, it becomes possible to film in a way that is more in line with those emotions. Scene priorities are determined based on factors such as the intensity of emotion and the importance of the scene. For example, the intensity of emotion indicates the strength of the user's emotions. The importance of a scene is evaluated based on the content and context of the scene. This allows the filming team to estimate the user's emotions and prioritize scenes to film based on those estimates.

[0088] The camera crew can prioritize capturing scenes that are highly relevant to the user's geographical location during shooting. For example, if the user is in a tourist area, the camera crew will prioritize capturing tourist attractions. If the user is at an event venue, the camera crew will prioritize capturing the highlights of the event. If the user is in nature, the camera crew will prioritize capturing beautiful scenery. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location by considering the user's location. Geographical location information includes, for example, GPS data and location services. For example, GPS data shows the user's current location. Location services provide relevant information based on the user's location. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location during shooting.

[0089] The photography team can analyze the user's social media activity during filming and capture relevant scenes. For example, the photography team can prioritize filming content that the user wants to share on social media. For example, the photography team can prioritize filming scenes that the user's followers are likely to be interested in. For example, the photography team can select scenes to film by referring to content the user has previously posted. This allows the photography team to capture relevant scenes by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of comments. For example, the content of posts refers to what the user has posted on social media. The number of likes refers to the number of reactions to a post. The number of comments refers to the number of comments on a post. This allows the photography team to analyze the user's social media activity during filming and capture relevant scenes.

[0090] The editorial team can estimate the user's emotions and adjust the editing style based on those estimates. For example, if the user is excited, the editorial team will use dynamic editing. If the user is relaxed, the editorial team will use calm editing. If the user is moved, the editorial team will use emotionally impactful editing. This allows for more emotionally resonant editing by adjusting the editing style based on the user's emotions. Editing styles include, for example, color correction, adding effects, and adjusting the audio. For example, color correction adjusts the color tone of the video. Adding effects adds special effects to the video. Audio adjustment optimizes the volume and sound quality. This allows the editorial team to estimate the user's emotions and adjust the editing style based on those estimates.

[0091] The editorial team can adjust the level of detail in editing based on the importance of the video. For example, the editorial team will edit important scenes in detail, while simplifying less important scenes. They can also adjust the editing time according to importance. This allows for more efficient editing by adjusting the level of detail based on the importance of the video. Factors contributing to video importance include, for example, the number of views, user ratings, and the importance of the content. For example, the number of views indicates how many times the video has been watched. User ratings indicate user reactions to the video. The importance of the content is assessed based on the content and context of the video. This allows the editorial team to adjust the level of detail in editing based on the importance of the video.

[0092] The editorial team can apply different editing algorithms depending on the video category during editing. For example, the editorial team will apply an action-oriented editing algorithm to action videos. For example, the editorial team will apply a documentary-oriented editing algorithm to documentary videos. For example, the editorial team will apply a comedy-oriented editing algorithm to comedy videos. This allows for optimal editing by applying different editing algorithms depending on the video category. Editing algorithms include, for example, cut editing algorithms and transition algorithms. For example, a cut editing algorithm automatically removes unnecessary parts of a video. A transition algorithm makes scene transitions smoother. This allows the editorial team to apply different editing algorithms depending on the video category during editing.

[0093] The publishing function can estimate the user's emotions and adjust the publishing timing based on those emotions. For example, if the publishing function is excited, it will publish immediately. If the publishing function is relaxed, it will publish at the optimal time. If the publishing function is tense, it will wait until the user is relaxed before publishing. This allows for optimal publishing timing by adjusting the publishing timing based on the user's emotions. Publishing timing includes, for example, the audience's activity time and the timing of events. For example, the audience's activity time indicates the time when the audience is most active. The timing of events indicates the timing to publish in conjunction with a specific event. This allows the publishing function to estimate the user's emotions and adjust the publishing timing based on those emotions.

[0094] The publishing department can analyze the video's viewing history at the time of publication and select the optimal publishing method. For example, the publishing department prioritizes publishing methods preferred by viewers. For example, the publishing department can select the optimal publishing time based on viewing history. For example, the publishing department can adjust the publishing method by analyzing viewer reactions. In this way, the optimal publishing method can be selected by analyzing the video's viewing history. Viewing history includes, for example, the number of views, viewing time, and viewer reactions. For example, the number of views indicates how many times the video was watched. Viewing time indicates how long the video was watched. Viewer reactions indicate viewers' ratings and comments on the video. In this way, the publishing department can analyze the video's viewing history at the time of publication and select the optimal publishing method.

[0095] The publishing department can apply different publishing algorithms depending on the video category at the time of publication. For example, the publishing department applies an action-oriented publishing algorithm to action videos. For example, the publishing department applies a documentary-oriented publishing algorithm to documentary videos. For example, the publishing department applies a comedy-oriented publishing algorithm to comedy videos. This allows for optimal publishing by applying different publishing algorithms depending on the video category. Publishing algorithms include, for example, targeting algorithms and optimization algorithms. For example, a targeting algorithm publishes videos to a specific audience. An optimization algorithm adjusts the publishing method based on audience reactions. This allows the publishing department to apply different publishing algorithms depending on the video category at the time of publication.

[0096] The extraction unit can estimate the user's emotions and determine the priority of data to extract based on the estimated emotions. For example, if the user is excited, the extraction unit will prioritize extracting data from action scenes. If the user is relaxed, the extraction unit will prioritize extracting data from landscape scenes. If the user is moved, the extraction unit will prioritize extracting data from emotional scenes. This allows for more emotionally relevant data extraction by determining the priority of data to extract based on the user's emotions. Data priority is determined based on factors such as data importance, relevance, and emotional intensity. For example, data importance is evaluated based on the content and context of the data. Relevance indicates how closely the data relates to other data. Emotional intensity indicates the strength of the user's emotions. This allows the extraction unit to estimate the user's emotions and determine the priority of data to extract based on the estimated emotions.

[0097] The extraction unit can improve the accuracy of extraction by considering the interrelationships between videos during the extraction process. For example, the extraction unit can extract data from related videos in a batch. The extraction unit can analyze the interrelationships between videos and extract highly relevant data. The extraction unit can optimize the data extraction order by considering the interrelationships between videos. This improves the accuracy of extraction by considering the interrelationships between videos. Video interrelationships include, for example, related videos, series videos, and common themes. For example, related videos refer to videos related to the same theme or content. Series videos refer to videos that are published consecutively. Common themes refer to videos created based on the same theme. This allows the extraction unit to improve the accuracy of extraction by considering the interrelationships between videos during the extraction process.

[0098] The extraction unit can perform extraction while considering the attribute information of the video's photographer. For example, the extraction unit can extract data based on the photographer's age and gender. For example, the extraction unit can extract data based on the photographer's interests. For example, the extraction unit can extract data by referring to the photographer's past shooting history. This allows for the extraction of more relevant data by considering the attribute information of the video's photographer. The photographer's attribute information includes, for example, age, gender, and interests. For example, age indicates the photographer's age group. Gender indicates the photographer's gender. Interests indicate the photographer's interests. This allows the extraction unit to perform extraction while considering the attribute information of the video's photographer.

[0099] The extraction unit can estimate the user's emotions and adjust how the extracted data is displayed based on those estimated emotions. For example, if the user is excited, the extraction unit provides a dynamic display. If the user is relaxed, the extraction unit provides a calm display. If the user is moved, the extraction unit provides an emotionally evocative display. By adjusting the data display based on the user's emotions, a more emotionally resonant display becomes possible. Data display methods include, for example, graphs, lists, and highlights. For example, graphs visually represent the data. Lists show the data in a list. Highlights highlight important data. This allows the extraction unit to estimate the user's emotions and adjust how the extracted data is displayed based on those estimated emotions.

[0100] The extraction unit can perform extraction while considering the geographical distribution of videos. For example, the extraction unit can prioritize extracting video data from a specific region. For example, the extraction unit can extract geographically related video data in bulk. For example, the extraction unit can optimize the data extraction order while considering geographical distribution. This allows for the extraction of more relevant data by considering the geographical distribution of videos. Geographical distribution includes, for example, data by region and data by country. For example, data by region indicates videos related to a specific region. Data by country indicates videos related to a specific country. This allows the extraction unit to perform extraction while considering the geographical distribution of videos.

[0101] The extraction unit can improve the accuracy of its extraction by referring to related literature for the video during the extraction process. For example, the extraction unit extracts data that complements the video content based on related literature. The extraction unit optimizes the data extraction method by referring to related literature, for example. The extraction unit adjusts the data extraction order based on related literature, for example. This improves the accuracy of the extraction by referring to related literature for the video. Related literature includes, for example, academic papers, technical reports, and patent documents. For example, academic papers provide information based on specific research or investigations. Technical reports provide information on specific technologies or methods. Patent documents provide information on specific inventions or technologies. This allows the extraction unit to improve the accuracy of its extraction by referring to related literature for the video during the extraction process.

[0102] The data provider can estimate the user's emotions and prioritize the data to be provided based on those emotions. For example, if the user is excited, the provider will prioritize action data. If the user is relaxed, the provider will prioritize scenery data. If the user is moved, the provider will prioritize emotionally moving data. By prioritizing the data to be provided based on the user's emotions, it becomes possible to provide data that is more aligned with those emotions. Data provision includes, for example, API provision, database provision, and file provision. For example, API provision makes the data available to programs. Database provision provides the data by storing it in a database. File provision provides the data in file format. This allows the data provider to estimate the user's emotions and prioritize the data to be provided based on those emotions.

[0103] The data provider can improve the accuracy of its delivery by considering the interrelationships between data. For example, the provider can provide related data in a single batch. The provider can, for example, analyze the interrelationships between data and provide highly relevant data. The provider can, for example, optimize the order of delivery by considering the interrelationships between data. This improves the accuracy of delivery by considering the interrelationships between data. Data interrelationships include, for example, related data and data dependencies. For example, related data refers to data related to the same theme or content. Data dependencies indicate that certain data depend on other data. This allows the provider to improve the accuracy of its delivery by considering the interrelationships between data when providing it.

[0104] The data provider can provide data while considering the data provider's attribute information. For example, the data provider can provide data based on the provider's age and gender. For example, the data provider can provide data based on the provider's interests and concerns. For example, the data provider can provide data while referring to the provider's past data provision history. This allows the data provider to provide more relevant data by considering the data provider's attribute information. The provider's attribute information includes, for example, age, gender, and field of expertise. For example, age indicates the provider's age group. Gender indicates the provider's gender. Field of expertise indicates the provider's professional knowledge and experience. This allows the data provider to provide data while considering the data provider's attribute information.

[0105] The service provider can estimate the user's emotions and adjust how the data is displayed based on those emotions. For example, if the user is excited, the service provider will provide a dynamic display. If the user is relaxed, the service provider will provide a calm display. If the user is moved, the service provider will provide an emotionally evocative display. By adjusting the data display based on the user's emotions, a more emotionally resonant display becomes possible. Data display methods include, for example, graphs, lists, and highlights. For example, graphs visually represent the data. Lists show the data in a list. Highlights highlight important data. This allows the service provider to estimate the user's emotions and adjust how the data is displayed based on those emotions.

[0106] The data provider can provide data while considering its geographical distribution. For example, the provider can prioritize providing data from a specific region. For example, the provider can provide geographically related data in a group. For example, the provider can optimize the order in which data is provided while considering its geographical distribution. This allows the provider to provide more relevant data by considering the geographical distribution of the data. Geographical distribution includes, for example, regional data and country-specific data. For example, regional data represents data related to a specific region. Country-specific data represents data related to a specific country. This allows the provider to provide data while considering its geographical distribution.

[0107] The data provider can improve the accuracy of the data delivery by referring to relevant literature at the time of delivery. For example, the data provider can supplement the content of the data based on relevant literature. For example, the data provider can optimize the method of data delivery by referring to relevant literature. For example, the data provider can adjust the order of data delivery based on relevant literature. As a result, the accuracy of the data delivery is improved by referring to relevant literature. Relevant literature includes, for example, academic papers, technical reports, and patent documents. For example, academic papers provide information based on specific research or investigations. Technical reports provide information on specific technologies or methods. Patent documents provide information on specific inventions or technologies. As a result, the data provider can improve the accuracy of the data delivery by referring to relevant literature at the time of delivery.

[0108] The rewards unit can estimate the user's emotions and adjust the reward distribution method based on those estimated emotions. For example, if the user is excited, the rewards unit will immediately award a reward. If the user is relaxed, the rewards unit will award a reward at an appropriate time. If the user is moved, the rewards unit will award a reward that enhances that emotion. By adjusting the reward distribution method based on the user's emotions, it becomes possible to provide rewards that are more aligned with those emotions. Reward distribution methods include, for example, monetary rewards, point rewards, and perks. For example, monetary rewards are paid according to the number of views or viewing time. Point rewards are awarded according to the number of views or viewing time. Perks are offered according to the number of views or viewing time. This allows the rewards unit to estimate the user's emotions and adjust the reward distribution method based on those estimated emotions.

[0109] The rewards department can analyze viewing history to select the optimal reward distribution method when distributing rewards. For example, the rewards department prioritizes reward distribution methods preferred by viewers. For example, the rewards department selects the optimal timing for reward distribution based on viewing history. For example, the rewards department adjusts reward distribution methods by analyzing viewer reactions. In this way, the optimal reward distribution method can be selected by analyzing viewing history. Viewing history includes, for example, the number of views, viewing time, and viewer reactions. For example, the number of views indicates how many times the video was watched. Viewing time indicates how long the video was watched. Viewer reactions indicate viewers' evaluations and comments on the video. In this way, the rewards department can analyze viewing history to select the optimal reward distribution method when distributing rewards.

[0110] The rewards unit can award rewards by considering viewer attribute information when awarding rewards. For example, the rewards unit can award rewards based on the viewer's age and gender. For example, the rewards unit can award rewards based on the viewer's interests and preferences. For example, the rewards unit can award rewards by referring to the viewer's past viewing history. This allows for the awarding of more relevant rewards by considering viewer attribute information. Viewer attribute information includes, for example, age, gender, and interests. For example, age indicates the viewer's age group. Gender indicates the viewer's gender. Interests indicate the viewer's interests and preferences. This allows the rewards unit to award rewards by considering viewer attribute information when awarding rewards.

[0111] The reward unit can estimate the user's emotions and determine the priority of rewards based on those estimated emotions. For example, if the user is excited, the reward unit will immediately grant a reward. If the user is relaxed, the reward unit will grant a reward at an appropriate time. If the user is moved, the reward unit will prioritize rewards that enhance that emotion. This allows for more emotionally aligned rewards by determining the priority of rewards based on the user's emotions. The priority of rewards is determined based on factors such as the importance of the reward, its relevance, and the intensity of the emotion. For example, the importance of a reward is evaluated based on its content and context. Relevance indicates how closely a reward relates to other rewards. The intensity of the emotion indicates the strength of the user's emotions. This allows the reward unit to estimate the user's emotions and determine the priority of rewards based on those estimated emotions.

[0112] The rewards department can select the optimal reward method when awarding rewards, taking into account the viewer's geographical location information. For example, the rewards department can award rewards that are aligned with trends in the viewer's region. For example, the rewards department can award rewards related to the viewer's geographical location. For example, the rewards department can award rewards that are aligned with events in the viewer's region. This allows the rewards department to select the optimal reward method by considering the viewer's geographical location information. Geographical location information includes, for example, GPS data and location services. For example, GPS data indicates the viewer's current location. Location services provide relevant information based on the viewer's location. This allows the rewards department to select the optimal reward method when awarding rewards, taking into account the viewer's geographical location information.

[0113] The rewards department can analyze viewers' social media activity and adjust reward distribution methods when awarding rewards. For example, the rewards department can award rewards tailored to content viewers want to share on social media. For example, the rewards department can award rewards that are likely to interest the viewer's followers. For example, the rewards department can award rewards based on the viewer's past social media activity. This allows the department to select the optimal reward distribution method by analyzing viewers' social media activity. Social media activity includes, for example, post content, number of likes, and number of comments. For example, post content refers to what viewers have posted on social media. The number of likes refers to the number of reactions to a post. The number of comments refers to the number of comments on a post. This allows the rewards department to analyze viewers' social media activity and adjust reward distribution methods when awarding rewards.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The camera unit can estimate the user's emotions and adjust the start time of shooting based on the estimated emotions. For example, if the camera unit is excited, it will start shooting immediately. If the camera unit is relaxed, it will start shooting at a natural timing. If the camera unit is tense, it will wait until the user is relaxed before shooting. By adjusting the start time of shooting based on the user's emotions, more natural shooting becomes possible. The user's emotions are estimated by methods such as facial recognition, voice analysis, and behavioral analysis. For example, facial recognition estimates emotions by analyzing the user's facial expressions. Voice analysis estimates emotions by analyzing the tone and speed of the user's voice. Behavioral analysis estimates emotions by analyzing the user's movements and posture. This allows the camera unit to estimate the user's emotions and adjust the start time of shooting based on the estimated emotions.

[0116] The editorial team can estimate the user's emotions and adjust the editing style based on those estimates. For example, if the user is excited, the editorial team will use dynamic editing. If the user is relaxed, the editorial team will use calm editing. If the user is moved, the editorial team will use emotionally impactful editing. This allows for more emotionally resonant editing by adjusting the editing style based on the user's emotions. Editing styles include, for example, color correction, adding effects, and adjusting the audio. For example, color correction adjusts the color tone of the video. Adding effects adds special effects to the video. Audio adjustment optimizes the volume and sound quality. This allows the editorial team to estimate the user's emotions and adjust the editing style based on those estimates.

[0117] The publishing function can estimate the user's emotions and adjust the publishing timing based on those emotions. For example, if the publishing function is excited, it will publish immediately. If the publishing function is relaxed, it will publish at the optimal time. If the publishing function is tense, it will wait until the user is relaxed before publishing. This allows for optimal publishing timing by adjusting the publishing timing based on the user's emotions. Publishing timing includes, for example, the audience's activity time and the timing of events. For example, the audience's activity time indicates the time when the audience is most active. The timing of events indicates the timing to publish in conjunction with a specific event. This allows the publishing function to estimate the user's emotions and adjust the publishing timing based on those emotions.

[0118] The extraction unit can estimate the user's emotions and determine the priority of data to extract based on the estimated emotions. For example, if the user is excited, the extraction unit will prioritize extracting data from action scenes. If the user is relaxed, the extraction unit will prioritize extracting data from landscape scenes. If the user is moved, the extraction unit will prioritize extracting data from emotional scenes. This allows for more emotionally relevant data extraction by determining the priority of data to extract based on the user's emotions. Data priority is determined based on factors such as data importance, relevance, and emotional intensity. For example, data importance is evaluated based on the content and context of the data. Relevance indicates how closely the data relates to other data. Emotional intensity indicates the strength of the user's emotions. This allows the extraction unit to estimate the user's emotions and determine the priority of data to extract based on the estimated emotions.

[0119] The data provider can estimate the user's emotions and prioritize the data to be provided based on those emotions. For example, if the user is excited, the provider will prioritize action data. If the user is relaxed, the provider will prioritize scenery data. If the user is moved, the provider will prioritize emotionally moving data. By prioritizing the data to be provided based on the user's emotions, it becomes possible to provide data that is more aligned with those emotions. Data provision includes, for example, API provision, database provision, and file provision. For example, API provision makes the data available to programs. Database provision provides the data by storing it in a database. File provision provides the data in file format. This allows the data provider to estimate the user's emotions and prioritize the data to be provided based on those emotions.

[0120] The camera unit can analyze the user's past experience history during shooting and select the optimal shooting method. For example, the camera unit prioritizes shooting angles that the user has preferred to use in the past. The camera unit can also refer to the style of videos the user has shot in the past. The camera unit can also automatically select the optimal shooting settings based on the user's past experience history. This allows the camera unit to select the optimal shooting method by analyzing the user's past experience history. Past experience history includes, for example, past video data and behavioral logs. For example, past video data shows the content and style of videos the user has shot in the past. Behavioral logs show the user's past behavior and preferences. This allows the camera unit to analyze the user's past experience history during shooting and select the optimal shooting method.

[0121] The camera unit can automatically adjust shooting settings based on the user's current activity and environment during shooting. For example, if the user is outdoors, the camera unit automatically adjusts brightness and exposure. If the user is indoors, the camera unit adjusts the white balance to match the lighting. If the user is moving, the camera unit enhances image stabilization. This enables optimal shooting by automatically adjusting shooting settings based on the user's current activity and environment. Current activity and environment include, for example, GPS data and sensor data. For example, GPS data indicates the user's current location. Sensor data indicates the user's movement and surrounding environment. This allows the camera unit to automatically adjust shooting settings based on the user's current activity and environment during shooting.

[0122] The camera crew can prioritize capturing scenes that are highly relevant to the user's geographical location during shooting. For example, if the user is in a tourist area, the camera crew will prioritize capturing tourist attractions. If the user is at an event venue, the camera crew will prioritize capturing the highlights of the event. If the user is in nature, the camera crew will prioritize capturing beautiful scenery. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location by considering the user's location. Geographical location information includes, for example, GPS data and location services. For example, GPS data shows the user's current location. Location services provide relevant information based on the user's location. This allows the camera crew to prioritize capturing scenes that are highly relevant to the user's geographical location during shooting.

[0123] The editorial team can adjust the level of detail in editing based on the importance of the video. For example, the editorial team will edit important scenes in detail, while simplifying less important scenes. They can also adjust the editing time according to importance. This allows for more efficient editing by adjusting the level of detail based on the importance of the video. Factors contributing to video importance include, for example, the number of views, user ratings, and the importance of the content. For example, the number of views indicates how many times the video has been watched. User ratings indicate user reactions to the video. The importance of the content is assessed based on the content and context of the video. This allows the editorial team to adjust the level of detail in editing based on the importance of the video.

[0124] The editorial team can apply different editing algorithms depending on the video category during editing. For example, the editorial team will apply an action-oriented editing algorithm to action videos. For example, the editorial team will apply a documentary-oriented editing algorithm to documentary videos. For example, the editorial team will apply a comedy-oriented editing algorithm to comedy videos. This allows for optimal editing by applying different editing algorithms depending on the video category. Editing algorithms include, for example, cut editing algorithms and transition algorithms. For example, a cut editing algorithm automatically removes unnecessary parts of a video. A transition algorithm makes scene transitions smoother. This allows the editorial team to apply different editing algorithms depending on the video category during editing.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The filming team films the experience. The filming team can film the experience using, for example, a dedicated device. Dedicated devices include earphone type, brooch type, hairpin type, earring type, helmet-attachable type, and necklace type. For example, with an earphone type device, filming starts simply by putting it in the ear. With a brooch type device, filming starts when it is attached to clothing. With a hairpin type device, filming starts when it is attached to the hair. Step 2: The editorial team edits the video shot by the camera crew. The editorial team checks the video content and performs automatic editing. For example, the editorial team performs cut editing to remove unnecessary parts. The editorial team can also add transitions to make the video flow more smoothly. Furthermore, the editorial team can adjust the audio to optimize the volume and sound quality. Step 3: The publishing team publishes the video edited by the editorial team. The publishing team publishes the video, for example, in a dedicated tab. For example, the publishing team can publish the video in a dedicated tab on a video sharing platform. Step 4: The extraction unit extracts training data from the video captured by the shooting unit. The extraction unit checks the content of the video and automatically extracts training data. For example, the extraction unit extracts specific scenes from the video and provides them as training data. Step 5: The provisioning unit provides the training data extracted by the extraction unit. For example, the provisioning unit provides the training data to the operating company. When the provided training data is purchased, a portion of it is paid to the participant as compensation. Step 6: The rewards unit rewards participants based on their viewing. For example, the rewards unit rewards participants based on the number of views. For example, the rewards unit rewards participants with higher rewards for more views. The rewards unit can also reward participants based on their viewing time. For example, the rewards unit rewards participants with higher rewards for longer viewing times.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the shooting unit, editing unit, publishing unit, extraction unit, provision unit, and reward unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit shoots the experience using the camera 42 of the smart device 14 or a dedicated device. The editing unit automatically edits the captured video by, for example, the specific processing unit 290 of the data processing unit 12. The publishing unit publishes the edited video in a dedicated tab by, for example, the control unit 46A of the smart device 14. The extraction unit extracts learning data from the video by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the learning data extracted by, for example, the specific processing unit 290 of the data processing unit 12 to the operating company. The reward unit provides rewards to the participant according to their viewing by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the shooting unit, editing unit, publishing unit, extraction unit, provision unit, and reward unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit shoots the experience using the camera 42 of the smart glasses 214 or a dedicated device. The editing unit automatically edits the captured video using, for example, the specific processing unit 290 of the data processing unit 12. The publishing unit publishes the edited video using, for example, the control unit 46A of the smart glasses 214 in a dedicated tab. The extraction unit extracts learning data from the video using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the learning data extracted by, for example, the specific processing unit 290 of the data processing unit 12 to the operating company. The reward unit provides rewards to the participant based on their viewing using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the shooting unit, editing unit, publishing unit, extraction unit, provision unit, and reward unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the shooting unit shoots the experience using the camera 42 of the headset terminal 314 or a dedicated device. The editing unit automatically edits the captured video using, for example, the specific processing unit 290 of the data processing unit 12. The publishing unit publishes the edited video using, for example, the control unit 46A of the headset terminal 314 in a dedicated tab. The extraction unit extracts learning data from the video using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the learning data extracted by, for example, the specific processing unit 290 of the data processing unit 12 to the operating company. The reward unit provides rewards to the participant according to their viewing using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the filming unit, editing unit, publishing unit, extraction unit, provision unit, and reward unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the filming unit films the experience using the camera 42 of the robot 414 or a dedicated device. The editing unit automatically edits the filmed video by, for example, the specific processing unit 290 of the data processing unit 12. The publishing unit publishes the edited video by, for example, the control unit 46A of the robot 414 in a dedicated tab. The extraction unit extracts learning data from the video by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the learning data extracted by, for example, the specific processing unit 290 of the data processing unit 12 to the operating company. The reward unit provides rewards to the participant according to their viewing by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) The film crew will be in charge of filming the experience, The editing department edits the videos shot by the aforementioned filming department, The publishing department publishes videos edited by the aforementioned editorial department, An extraction unit extracts learning data from the video captured by the aforementioned shooting unit, A providing unit that provides the learning data extracted by the extraction unit, It comprises a reward unit that provides rewards based on viewing. A system characterized by the following features. (Note 2) The aforementioned imaging unit is The experience is filmed using dedicated devices in the form of earphones, brooches, hairpins, earrings, helmets, and necklaces. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned editorial department, Check the video content and edit it automatically. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned public section is, Publish the edited video in a dedicated tab on the video sharing platform. The system described in Appendix 1, characterized by the features described herein. (Note 5) The extraction unit is The system checks the video content and automatically extracts training data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The extracted training data will be provided to the LLM operating company. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned compensation unit is, Rewards will be given to participants based on their viewing experience. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the start of shooting based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is During shooting, the system analyzes the user's past experience history to select the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is During shooting, the system automatically adjusts shooting settings based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of scenes to film based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is During shooting, the system prioritizes capturing scenes that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned imaging unit is During filming, the system analyzes the user's social media activity and captures relevant scenes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editorial department, It estimates the user's emotions and adjusts the editing style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editorial department, During editing, adjust the level of detail based on the importance of the video. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editorial department, During editing, different editing algorithms are applied depending on the video category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned public section is, We estimate user sentiment and adjust the timing of publication based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned public section is, When publishing, the video's viewing history is analyzed to select the optimal publishing method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned public section is, When publishing, different publishing algorithms are applied depending on the video category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is It estimates the user's emotions and determines the priority of data to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is During extraction, the accuracy of the extraction is improved by considering the interrelationships between videos. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is During extraction, the video's creator's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is We estimate the user's emotions and adjust how the data extracted based on those estimated emotions is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is During extraction, the geographical distribution of the videos is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The extraction unit is During extraction, we improve the accuracy of the extraction by referring to related literature for the video. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the data to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing data, we improve the accuracy of the data by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing data, the data provider's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing data, we will take into account its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing data, we refer to relevant literature to improve the accuracy of the data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned compensation unit is, The system estimates the user's emotions and adjusts the reward system based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned compensation unit is, When awarding rewards, the system analyzes viewing history to select the most suitable reward method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned compensation unit is, When awarding rewards, the rewards will be determined considering the viewer's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned compensation unit is, The system estimates the user's emotions and prioritizes rewards based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned compensation unit is, When awarding rewards, the optimal reward method will be selected considering the viewer's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned compensation unit is, When awarding rewards, we analyze the audience's social media activity and adjust the reward distribution method accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 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 film crew will be in charge of filming the experience, The editing department edits the videos shot by the aforementioned filming department, The publishing department publishes videos edited by the aforementioned editorial department, An extraction unit extracts learning data from the video captured by the aforementioned shooting unit, A providing unit that provides the learning data extracted by the extraction unit, It comprises a reward unit that provides rewards based on viewing. A system characterized by the following features.

2. The aforementioned imaging unit is The experience is filmed using dedicated devices in the form of earphones, brooches, hairpins, earrings, helmets, and necklaces. The system according to feature 1.

3. The aforementioned editorial department, Check the video content and edit it automatically. The system according to feature 1.

4. The aforementioned public section is, Publish the edited video in a dedicated tab on the video sharing platform. The system according to feature 1.

5. The extraction unit is The system checks the video content and automatically extracts training data. The system according to feature 1.

6. The aforementioned supply unit is, The extracted training data will be provided to the LLM operating company. The system according to feature 1.

7. The aforementioned compensation unit is, Rewards will be given to participants based on their viewing experience. The system according to feature 1.

8. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the start of shooting based on the estimated user emotions. The system according to feature 1.

9. The aforementioned imaging unit is During shooting, the system analyzes the user's past experience history to select the optimal shooting method. The system according to feature 1.

10. The aforementioned imaging unit is During shooting, the system automatically adjusts shooting settings based on the user's current activity status and environment. The system according to feature 1.

Images