system

Abstract

An object of the system according to the embodiment is to enable a creator to quickly generate a profit from his / her work and evaluate the market value.SOLUTION: A system according to an embodiment includes a generation unit, a selling unit, and an evaluation unit. The generation unit generates a moving image on the basis of an instruction of the creator. The exhibition unit exhibits the moving image generated by the generation unit in the NFT market. The evaluation unit evaluates the market value of the creator by analyzing the sales data of the video exhibited by the exhibition unit and the reaction of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology has limited the means for creators to quickly monetize their work and assess their market value.

[0005] The system according to the embodiment aims to enable creators to quickly monetize their works and evaluate their market value. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a listing unit, and an evaluation unit. The generation unit generates videos based on instructions from creators. The listing unit lists the videos generated by the generation unit on the NFT market. The evaluation unit analyzes sales data and user responses to the videos listed by the listing unit to evaluate the creator's market value. [Effects of the Invention]

[0007] The system according to the embodiment allows creators to quickly monetize and assess the market value of their work. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to enable creators to effectively express and quickly monetize their work. This system uses generative AI to generate videos, which can then be listed on the NFT market, allowing creators to discover their own market value. This allows creators to effectively express and quickly monetize their work.

[0029] The system according to the embodiment includes a generation unit, a listing unit, and an evaluation unit. The generation unit generates videos based on instructions from a creator. For example, when a creator inputs a prompt such as "Please create a video with a natural landscape theme," the generation unit analyzes the instructions and generates multiple videos. The generation unit can also generate high-quality videos according to the creator's instructions using a fine-tuned generation AI. The listing unit lists the videos generated by the generation unit on an NFT market. For example, the listing unit automatically generates metadata and descriptions for the videos generated by the generation AI to support the listing process. The listing unit can also list the videos on an NFT market (e.g., OpenSea or Rarible). The evaluation unit analyzes sales data and user responses for the videos listed by the listing unit to evaluate the creator's market value. For example, the evaluation unit collects and analyzes data such as which videos attracted the most attention and at what price range they sold. The evaluation unit can also calculate the creator's market value based on sales data and user responses using an algorithm for evaluating the creator's market value. As a result, the system according to the embodiment allows creators to effectively express their works and quickly monetize them. For example, creators can use the generation AI to generate high-quality videos and list them on the NFT market, thereby discovering their market value. Furthermore, by utilizing the data analysis function provided by the evaluation unit, creators can understand the value of their works and reflect this in their next works.

[0030] The generation unit can generate personalized videos by reflecting the user's past viewing history and preferences. For example, the generation unit analyzes the user's past viewing history and generates videos according to the user's preferences. For example, if the user likes action movies, the generation unit generates videos that include many action scenes. The generation unit can also generate personalized videos using an algorithm for customizing video content based on the user's preferences. For example, the generation AI can select optimal scenes and effects based on the user's viewing history data and reflect them in the video. This makes it possible to generate personalized videos based on the user's past viewing history and preferences.

[0031] The generation unit automatically adds music or sound effects to videos, engaging the user both visually and aurally. For example, when the generation AI generates a video, the generation unit automatically adds music and sound effects appropriate to the scene. For example, tense music is added to action scenes, and moving music is added to emotional scenes. The generation unit can also select optimal music and sound effects using an algorithm for automatically adding music and sound effects. For example, the generation AI selects optimal music and sound effects based on the content of the scene and the user's emotional data, and reflects them in the video. This makes it possible to generate videos that engage the user both visually and aurally.

[0032] The generation unit can optimize the generated video for different platforms. For example, when the generation AI generates a video, the generation unit optimizes it for the format and resolution of each platform. For example, it generates a video in 16:9 resolution for YouTube and 1:1 resolution for Instagram. The generation unit can also select the optimal format and resolution using an algorithm for generating videos optimized for different platforms. For example, the generation AI selects the optimal format and resolution based on the platform requirements and user viewing data and reflects them in the video. This makes it possible to generate videos optimized for different platforms.

[0033] The listing department can analyze sales data and propose optimal pricing. For example, the generation AI in the listing department analyzes past sales data and proposes optimal pricing. For example, it calculates an appropriate price based on the sales prices of videos in the same genre. The listing department can also select the optimal price using an algorithm for analyzing sales data and proposing pricing. For example, the generation AI calculates and proposes the optimal price based on sales data and market demand. This allows it to analyze past sales data and propose optimal pricing.

[0034] The listing unit can simultaneously list the generated video on different NFT platforms. For example, when the generation AI generates a video, the listing unit provides the function to simultaneously list the video on different NFT platforms. For example, the video can be listed on OpenSea and Rarible at the same time. The listing unit can also select the optimal platform using an algorithm for simultaneously listing on different NFT platforms. For example, the generation AI selects the optimal platform based on the requirements of each platform and market demand, and lists the video simultaneously. This allows the video to be listed on different NFT platforms at the same time.

[0035] The listing department can automatically generate a creator's brand story and incorporate it into the listing process. In the listing department, for example, a generation AI automatically generates a creator's brand story and incorporates it into the listing process. For example, a story is generated that explains the creator's career and the background of the work. The listing department can also select the optimal story using an algorithm for automatically generating a brand story. For example, the generation AI generates the optimal brand story based on the creator's data and incorporates it into the listing process. In this way, by automatically generating a creator's brand story and incorporating it into the listing process, added value can be increased.

[0036] The evaluation unit can analyze video sales data in real time and update the creator's market value. The evaluation unit, for example, uses a generation AI to analyze video sales data in real time and update the creator's market value. For example, the market value is calculated based on the number of sales and price. The evaluation unit can also update the creator's market value using an algorithm for analyzing sales data in real time. For example, the generation AI collects sales data in real time and updates the market value based on the analysis results. In this way, video sales data can be analyzed in real time and the creator's market value can be updated.

[0037] The evaluation unit can analyze user reactions to videos and develop an algorithm that predicts a creator's market value. For example, the evaluation unit develops an algorithm in which a generation AI analyzes user reactions to videos and predicts a creator's market value. For example, the market value is calculated based on the user's viewing time and number of comments. The evaluation unit can also predict a creator's market value using an algorithm for analyzing user reactions and predicting market value. For example, the generation AI builds an optimal prediction model based on user reaction data and predicts market value. This makes it possible to develop an algorithm that analyzes user reactions to videos and predicts a creator's market value.

[0038] The evaluation unit can compare the market value of a video in different markets. For example, the generation AI in the evaluation unit compares the market value of a video in different markets. For example, the generation AI evaluates market value based on sales data in the art market and the entertainment market. The evaluation unit can also set optimal comparison standards using an algorithm for comparing market values ​​in different markets. For example, the generation AI sets optimal comparison standards based on data from each market and compares market values. This makes it possible to compare the market value of a video in different markets.

[0039] The evaluation unit can compare the market value of the video with other creators and perform a relative evaluation. For example, the generation AI can compare the market value of the video with other creators and perform a relative evaluation. For example, the market value can be evaluated based on sales data of creators in the same genre. The evaluation unit can also set optimal comparison standards using an algorithm for comparing market value with other creators. For example, the generation AI can set optimal comparison standards based on data from other creators and compare market value. This allows the market value of the video to be compared with other creators and perform a relative evaluation.

[0040] The evaluation unit can analyze past success stories for a promotion strategy and propose the optimal method. For example, the generation AI analyzes past success stories and proposes the optimal promotion method for the evaluation unit. For example, the generation AI can formulate a promotion strategy based on success stories in the same genre. The evaluation unit can also set the optimal promotion strategy using an algorithm that analyzes success stories and proposes the optimal method. For example, the generation AI can identify the optimal method based on data on past success stories and reflect it in the promotion strategy. This allows the generation AI to analyze past success stories for a promotion strategy and propose the optimal method.

[0041] The evaluation unit can optimize the promotion strategy for different platforms. For example, the generation AI in the evaluation unit optimizes the promotion strategy for different platforms. For example, promotions in the form of short clips or stories are performed for social media. The evaluation unit can also select the optimal strategy using an algorithm for setting promotion strategies optimized for different platforms. For example, the generation AI sets and implements the optimal promotion strategy based on the requirements of each platform and user viewing data. This allows the promotion strategy to be optimized for different platforms.

[0042] The evaluation unit can utilize the creator's past works to propose a promotion strategy that strengthens the brand story. For example, the generation AI utilizes the creator's past works to propose a promotion strategy that strengthens the brand story. For example, it can introduce past successful works. The evaluation unit can also set an optimal promotion strategy using an algorithm for utilizing past works to strengthen the brand story. For example, the generation AI identifies and proposes an optimal promotion strategy based on data on the creator's past works. This makes it possible to propose a promotion strategy that strengthens the brand story by utilizing the creator's past works.

[0043] The evaluation unit can analyze past feedback data and identify trends. For example, the generation AI analyzes past feedback data and identifies trends. For example, it identifies the themes and genres that users are most interested in. The evaluation unit can also set optimal trends using an algorithm for analyzing feedback data and identifying trends. For example, the generation AI identifies and suggests optimal trends based on past feedback data. This makes it possible to analyze past feedback data and identify trends.

[0044] The evaluation unit can automatically translate the feedback into different languages ​​and obtain feedback from an international perspective. For example, the evaluation unit can automatically translate the feedback collected by the generation AI into different languages ​​and obtain feedback from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. The evaluation unit can also set the optimal translation using an algorithm for automatically translating the feedback. For example, the generation AI can perform the optimal translation based on the feedback data and provide feedback from an international perspective. This allows the feedback to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0045] The evaluation unit can convert the feedback into visual notes or mind maps to make it easier to understand visually. For example, the evaluation unit converts the feedback collected by the generation AI into visual notes and displays them visually. For example, important points are indicated with diagrams or icons. The evaluation unit can also set the optimal display method using an algorithm for converting the feedback into visual notes or mind maps. For example, the generation AI generates optimal visual notes or mind maps based on the feedback data and displays them visually. This allows the feedback to be converted into visual notes or mind maps to make it easier to understand visually.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The generator can also obtain the user's real-time location information and generate videos related to that location. For example, if the user is at a tourist spot, it can generate a video introducing the tourist spot's famous sights and history. The generator can also select optimal scenes and effects using an algorithm to customize the content of the video based on location information. For example, the generator AI can select optimal tourist spots and events based on the user's location data and reflect them in the video. This allows it to generate videos based on the user's real-time location information.

[0048] The generation unit can also generate personalized videos based on data obtained from a user's social media account. For example, it can analyze photos and posts shared by the user on social media and generate videos based on that. The generation unit can also select optimal scenes and effects using an algorithm to customize the content of the video based on social media data. For example, the generation AI can select optimal scenes and effects based on the user's social media data and reflect them in the video. This allows for the generation of personalized videos based on the user's social media data.

[0049] The generation unit can also analyze handwritten sketches and illustrations provided by creators and generate videos based on them. For example, it can scan characters or landscapes drawn by creators and generate animated videos based on those sketches. The generation unit can also select optimal scenes and effects using an algorithm for analyzing handwritten sketches and generating videos. For example, the generation AI can select optimal animated scenes based on the creator's sketch data and reflect them in the video. This allows it to generate videos based on the creator's handwritten sketches.

[0050] The generation unit can also add interactive elements to the video it generates, generating videos in which the story changes depending on the options the user selects within the video. For example, it can generate videos in which the user can reach different endings depending on the options they select. The generation unit can also select optimal options and scenes using an algorithm to add interactive elements. For example, the generation AI can select the optimal story development based on the user's selection data and reflect it in the video. This makes it possible to generate interactive videos in which the story changes depending on the options the user selects.

[0051] The generation unit can also analyze text stories provided by users and generate videos based on them. For example, it can analyze short stories or essays written by users and generate scenes based on their content. The generation unit can also select optimal scenes and effects using an algorithm for analyzing text stories and generating videos. For example, the generation AI can select optimal scenes and effects based on the user's text data and reflect them in the video. This allows it to generate videos based on the user's text story.

[0052] The generation unit can also analyze photos and images provided by the user and generate videos based on them. For example, it can analyze photos taken by the user while traveling and generate a video that looks back on the memories of the trip based on those photos. The generation unit can also select optimal scenes and effects using an algorithm for analyzing photos and images to generate videos. For example, the generation AI can select optimal scenes and effects based on the user's photo data and reflect them in the video. This allows it to generate videos based on the user's photos and images.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The generator generates videos based on the creator's instructions. For example, if the creator inputs a prompt such as "Please create a video with a natural landscape theme," the generator analyzes the instructions and generates multiple videos. The generator can also use pre-fine-tuned generation AI to generate high-quality videos according to the creator's instructions. Step 2: The listing department lists the video generated by the generation department on the NFT market. For example, the listing department can automatically generate metadata and descriptions for the video generated by the generation AI to support the listing process. The listing department can also list the video on an NFT market (e.g., OpenSea or Rarible). Step 3: The evaluation unit analyzes sales data and user responses for the videos listed by the listing unit to evaluate the creator's market value. For example, the evaluation unit collects and analyzes data such as which videos attracted the most attention and at what price range they sold. The evaluation unit can also use an algorithm to evaluate the creator's market value and calculate the market value based on sales data and user responses.

[0055] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to enable creators to effectively express and quickly monetize their work. This system uses generative AI to generate videos, which can then be listed on the NFT market, allowing creators to discover their own market value. This allows creators to effectively express and quickly monetize their work.

[0056] The system according to the embodiment includes a generation unit, a listing unit, and an evaluation unit. The generation unit generates videos based on instructions from a creator. For example, when a creator inputs a prompt such as "Please create a video with a natural landscape theme," the generation unit analyzes the instructions and generates multiple videos. The generation unit can also generate high-quality videos according to the creator's instructions using a fine-tuned generation AI. The listing unit lists the videos generated by the generation unit on an NFT market. For example, the listing unit automatically generates metadata and descriptions for the videos generated by the generation AI to support the listing process. The listing unit can also list the videos on an NFT market (e.g., OpenSea or Rarible). The evaluation unit analyzes sales data and user responses for the videos listed by the listing unit to evaluate the creator's market value. For example, the evaluation unit collects and analyzes data such as which videos attracted the most attention and at what price range they sold. The evaluation unit can also calculate the creator's market value based on sales data and user responses using an algorithm for evaluating the creator's market value. As a result, the system according to the embodiment allows creators to effectively express their works and quickly monetize them. For example, creators can use the generation AI to generate high-quality videos and list them on the NFT market, thereby discovering their market value. Furthermore, by utilizing the data analysis function provided by the evaluation unit, creators can understand the value of their works and reflect this in their next works.

[0057] The generation unit can analyze the user's emotions in real time and generate video variations according to those emotions. For example, when the generation AI generates a video, the generation unit analyzes the user's emotions in real time and adds scenes and effects according to those emotions. For example, if the user expresses joy, bright colors and cheerful music are added. The generation unit can also combine different scenes and effects using an algorithm to generate video variations according to the user's emotions. For example, the generation AI selects the optimal scenes and effects based on the user's emotional data and reflects them in the video. This makes it possible to generate video variations according to the user's emotions.

[0058] The generation unit can generate personalized videos by reflecting the user's past viewing history and preferences. For example, the generation unit analyzes the user's past viewing history and generates videos according to the user's preferences. For example, if the user likes action movies, the generation unit generates videos that include many action scenes. The generation unit can also generate personalized videos using an algorithm for customizing video content based on the user's preferences. For example, the generation AI can select optimal scenes and effects based on the user's viewing history data and reflect them in the video. This makes it possible to generate personalized videos based on the user's past viewing history and preferences.

[0059] The generation unit can analyze the creator's emotions and generate a video based on those emotions. For example, the generation unit can analyze the emotions the creator felt when creating a video and add scenes and effects based on those emotions. For example, if the creator expressed joy, it could add bright colors and cheerful music. The generation unit can also combine different scenes and effects using an algorithm to generate a video based on the creator's emotions. For example, the generation AI can select the most appropriate scenes and effects based on the creator's emotional data and reflect them in the video. This allows the generation of a video based on the creator's emotions.

[0060] The generation unit automatically adds music or sound effects to videos, engaging the user both visually and aurally. For example, when the generation AI generates a video, the generation unit automatically adds music and sound effects appropriate to the scene. For example, tense music is added to action scenes, and moving music is added to emotional scenes. The generation unit can also select optimal music and sound effects using an algorithm for automatically adding music and sound effects. For example, the generation AI selects optimal music and sound effects based on the content of the scene and the user's emotional data, and reflects them in the video. This makes it possible to generate videos that engage the user both visually and aurally.

[0061] The generation unit can optimize the generated video for different platforms. For example, when the generation AI generates a video, the generation unit optimizes it for the format and resolution of each platform. For example, it generates a video in 16:9 resolution for YouTube and 1:1 resolution for Instagram. The generation unit can also select the optimal format and resolution using an algorithm for generating videos optimized for different platforms. For example, the generation AI selects the optimal format and resolution based on the platform requirements and user viewing data and reflects them in the video. This makes it possible to generate videos optimized for different platforms.

[0062] The generation unit can analyze the emotions of a user when watching a video and automatically insert advertisements that correspond to those emotions. For example, the generation unit can analyze the emotions of a user when watching a video in real time and insert advertisements that correspond to those emotions. For example, if a user expresses joy, an advertisement with positive content is inserted. The generation unit can also select the optimal advertisement using an algorithm for automatically inserting advertisements that correspond to emotions. For example, the generation AI can select the optimal advertisement based on the user's emotional data and insert it into the video. This makes it possible to automatically insert advertisements that correspond to the user's emotions.

[0063] The listing unit can automatically add tags based on the user's emotional reactions to the video metadata, thereby improving searchability. For example, the listing unit has the generation AI automatically add tags based on the user's emotional reactions to the video metadata. For example, if the user expresses the emotion of joy, tags such as "joy" or "happy" are added. The listing unit can also select optimal tags using an algorithm for automatically adding tags based on emotional reactions. For example, the generation AI selects optimal tags based on the user's emotional data and adds them to the metadata. In this way, adding tags based on the user's emotional reactions can improve searchability.

[0064] The listing department can analyze sales data and propose optimal pricing. For example, the generation AI in the listing department analyzes past sales data and proposes optimal pricing. For example, it calculates an appropriate price based on the sales prices of videos in the same genre. The listing department can also select the optimal price using an algorithm for analyzing sales data and proposing pricing. For example, the generation AI calculates and proposes the optimal price based on sales data and market demand. This allows it to analyze past sales data and propose optimal pricing.

[0065] The listing unit can automatically list items at the time of day when the user is most interested. For example, the listing unit uses an emotion estimation function to identify the time of day when the user is most interested and automatically lists the item at that time. For example, if the user is most interested in the nighttime, the listing unit lists the item at night. The listing unit can also select the time of day that will attract the user's interest using an algorithm to identify the optimal listing time. For example, the generation AI identifies the optimal listing time based on the user's activity data and reflects this in the listing process. This allows the item to be automatically listed at the time of day when the user is most interested.

[0066] The listing unit can simultaneously list the generated video on different NFT platforms. For example, when the generation AI generates a video, the listing unit provides the function to simultaneously list the video on different NFT platforms. For example, the video can be listed on OpenSea and Rarible at the same time. The listing unit can also select the optimal platform using an algorithm for simultaneously listing on different NFT platforms. For example, the generation AI selects the optimal platform based on the requirements of each platform and market demand, and lists the video simultaneously. This allows the video to be listed on different NFT platforms at the same time.

[0067] The listing department can automatically generate a creator's brand story and incorporate it into the listing process. In the listing department, for example, a generation AI automatically generates a creator's brand story and incorporates it into the listing process. For example, a story is generated that explains the creator's career and the background of the work. The listing department can also select the optimal story using an algorithm for automatically generating a brand story. For example, the generation AI generates the optimal brand story based on the creator's data and incorporates it into the listing process. In this way, by automatically generating a creator's brand story and incorporating it into the listing process, added value can be increased.

[0068] The listing unit can automatically generate thumbnails for videos that evoke the most positive emotions in users. The listing unit can use, for example, an emotion estimation function to automatically generate thumbnails that evoke the most positive emotions in users. For example, a scene in which the user expresses joy is used as the thumbnail. The listing unit can also select the optimal thumbnail using an algorithm for automatically generating thumbnails based on positive emotions. For example, the generation AI can select the optimal scene based on the user's emotion data and reflect it in the thumbnail. This makes it possible to automatically generate thumbnails that evoke the most positive emotions in users.

[0069] The evaluation unit can analyze video sales data in real time and update the creator's market value. The evaluation unit, for example, uses a generation AI to analyze video sales data in real time and update the creator's market value. For example, the market value is calculated based on the number of sales and price. The evaluation unit can also update the creator's market value using an algorithm for analyzing sales data in real time. For example, the generation AI collects sales data in real time and updates the market value based on the analysis results. In this way, video sales data can be analyzed in real time and the creator's market value can be updated.

[0070] The evaluation unit can analyze user reactions to videos and develop an algorithm that predicts a creator's market value. For example, the evaluation unit develops an algorithm in which a generation AI analyzes user reactions to videos and predicts a creator's market value. For example, the market value is calculated based on the user's viewing time and number of comments. The evaluation unit can also predict a creator's market value using an algorithm for analyzing user reactions and predicting market value. For example, the generation AI builds an optimal prediction model based on user reaction data and predicts market value. This makes it possible to develop an algorithm that analyzes user reactions to videos and predicts a creator's market value.

[0071] The evaluation unit can evaluate the market value of a creator based on the user's emotional response. The evaluation unit, for example, uses an emotion estimation function to evaluate the market value of a creator based on the user's emotional response. For example, the evaluation unit may highly evaluate the market value of videos in which the user shows positive emotions. The evaluation unit can also evaluate the market value of a creator using an algorithm for evaluating market value based on emotional response. For example, the generation AI sets optimal evaluation criteria based on the user's emotional data and evaluates the market value. This allows the market value of a creator to be evaluated based on the user's emotional response.

[0072] The evaluation unit can compare the market value of a video in different markets. For example, the generation AI in the evaluation unit compares the market value of a video in different markets. For example, the generation AI evaluates market value based on sales data in the art market and the entertainment market. The evaluation unit can also set optimal comparison standards using an algorithm for comparing market values ​​in different markets. For example, the generation AI sets optimal comparison standards based on data from each market and compares market values. This makes it possible to compare the market value of a video in different markets.

[0073] The evaluation unit can compare the market value of the video with other creators and perform a relative evaluation. For example, the generation AI can compare the market value of the video with other creators and perform a relative evaluation. For example, the market value can be evaluated based on sales data of creators in the same genre. The evaluation unit can also set optimal comparison standards using an algorithm for comparing market value with other creators. For example, the generation AI can set optimal comparison standards based on data from other creators and compare market value. This allows the market value of the video to be compared with other creators and perform a relative evaluation.

[0074] The evaluation unit can identify the market value of the creator for whom the user feels the most positive emotions. The evaluation unit, for example, uses an emotion estimation function to identify the market value of the creator for whom the user feels the most positive emotions. For example, the evaluation unit highly evaluates the market value of a creator for whom the user expresses emotions of joy. The evaluation unit can also set optimal evaluation criteria using an algorithm for identifying market value based on positive emotions. For example, the generation AI sets optimal evaluation criteria and identifies market value based on the user's emotion data. This makes it possible to identify the market value of the creator for whom the user feels the most positive emotions.

[0075] The evaluation unit can propose optimal timing for a promotion strategy based on the user's emotional response. For example, the generation AI analyzes the user's emotional response and proposes optimal promotion timing. For example, the promotion can be carried out during the time of day when the user is most excited. The evaluation unit can also set an optimal promotion strategy using an algorithm for proposing optimal timing based on emotional response. For example, the generation AI identifies the optimal timing based on the user's emotional data and reflects this in the promotion strategy. This makes it possible to propose optimal timing for a promotion strategy based on the user's emotional response.

[0076] The evaluation unit can analyze past success stories for a promotion strategy and propose the optimal method. For example, the generation AI analyzes past success stories and proposes the optimal promotion method for the evaluation unit. For example, the generation AI can formulate a promotion strategy based on success stories in the same genre. The evaluation unit can also set the optimal promotion strategy using an algorithm that analyzes success stories and proposes the optimal method. For example, the generation AI can identify the optimal method based on data on past success stories and reflect it in the promotion strategy. This allows the generation AI to analyze past success stories for a promotion strategy and propose the optimal method.

[0077] The evaluation unit can analyze the creator's emotions and propose a promotion strategy based on those emotions. The evaluation unit can, for example, use an emotion estimation function to analyze the creator's emotions and propose a promotion strategy based on those emotions. For example, if the creator expresses joy, the evaluation unit can promote the creator with positive content. The evaluation unit can also set an optimal promotion strategy using an algorithm for proposing a promotion strategy based on emotions. For example, the generation AI can identify and propose an optimal promotion strategy based on the creator's emotion data. This makes it possible to propose a promotion strategy based on the creator's emotions.

[0078] The evaluation unit can optimize the promotion strategy for different platforms. For example, the generation AI in the evaluation unit optimizes the promotion strategy for different platforms. For example, promotions in the form of short clips or stories are performed for social media. The evaluation unit can also select the optimal strategy using an algorithm for setting promotion strategies optimized for different platforms. For example, the generation AI sets and implements the optimal promotion strategy based on the requirements of each platform and user viewing data. This allows the promotion strategy to be optimized for different platforms.

[0079] The evaluation unit can utilize the creator's past works to propose a promotion strategy that strengthens the brand story. For example, the generation AI utilizes the creator's past works to propose a promotion strategy that strengthens the brand story. For example, it can introduce past successful works. The evaluation unit can also set an optimal promotion strategy using an algorithm for utilizing past works to strengthen the brand story. For example, the generation AI identifies and proposes an optimal promotion strategy based on data on the creator's past works. This makes it possible to propose a promotion strategy that strengthens the brand story by utilizing the creator's past works.

[0080] The evaluation unit can automatically generate promotional content that evokes the most positive emotions in the user. The evaluation unit can use, for example, an emotion estimation function to automatically generate promotional content that evokes the most positive emotions in the user. For example, a scene in which the user expresses joy is used for promotion. The evaluation unit can also select optimal content using an algorithm for automatically generating promotional content based on positive emotions. For example, the generation AI can select optimal scenes based on the user's emotion data and reflect them in the promotional content. This makes it possible to automatically generate promotional content that evokes the most positive emotions in the user.

[0081] The evaluation unit can analyze the user's emotional response and make improvement suggestions based on the emotion. For example, the evaluation unit uses a generation AI to analyze the user's emotional response and make improvement suggestions based on the emotion. For example, the evaluation unit can make a suggestion to increase the number of scenes in which the user expresses joy. The evaluation unit can also set optimal suggestions using an algorithm for making improvement suggestions based on the emotional response. For example, the generation AI can identify and make optimal improvement suggestions based on the user's emotional data. This makes it possible to analyze the user's emotional response and make improvement suggestions based on the emotion.

[0082] The evaluation unit can analyze past feedback data and identify trends. For example, the generation AI analyzes past feedback data and identifies trends. For example, it identifies the themes and genres that users are most interested in. The evaluation unit can also set optimal trends using an algorithm for analyzing feedback data and identifying trends. For example, the generation AI identifies and suggests optimal trends based on past feedback data. This makes it possible to analyze past feedback data and identify trends.

[0083] The evaluation unit can analyze the creator's emotions and provide feedback based on those emotions. The evaluation unit can, for example, use an emotion estimation function to analyze the creator's emotions and provide feedback based on those emotions. For example, if the creator expresses joy, positive feedback can be provided. The evaluation unit can also set optimal feedback using an algorithm for providing emotion-based feedback. For example, the generation AI can identify and provide optimal feedback based on the creator's emotion data. This makes it possible to provide feedback based on the creator's emotions.

[0084] The evaluation unit can automatically translate the feedback into different languages ​​and obtain feedback from an international perspective. For example, the evaluation unit can automatically translate the feedback collected by the generation AI into different languages ​​and obtain feedback from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. The evaluation unit can also set the optimal translation using an algorithm for automatically translating the feedback. For example, the generation AI can perform the optimal translation based on the feedback data and provide feedback from an international perspective. This allows the feedback to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0085] The evaluation unit can convert the feedback into visual notes or mind maps to make it easier to understand visually. For example, the evaluation unit converts the feedback collected by the generation AI into visual notes and displays them visually. For example, important points are indicated with diagrams or icons. The evaluation unit can also set the optimal display method using an algorithm for converting the feedback into visual notes or mind maps. For example, the generation AI generates optimal visual notes or mind maps based on the feedback data and displays them visually. This allows the feedback to be converted into visual notes or mind maps to make it easier to understand visually.

[0086] The evaluation unit can identify feedback that evokes the most positive emotions in the user and make improvement suggestions based on that feedback. The evaluation unit can, for example, use an emotion estimation function to identify feedback that evokes the most positive emotions in the user and make improvement suggestions based on that feedback. For example, improvement suggestions are made based on feedback in which the user expresses joy. The evaluation unit can also set optimal suggestions using an algorithm for making improvement suggestions based on positive emotions. For example, the generation AI can identify optimal feedback and make improvement suggestions based on the user's emotion data. This makes it possible to identify feedback that evokes the most positive emotions in the user and make improvement suggestions based on that feedback.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The generation unit can also analyze the voice narration provided by the creator and automatically generate video scenes based on the content. For example, if a creator provides audio talking about a natural landscape, the content of the audio can be analyzed and a corresponding scene can be generated. The generation unit can also analyze the emotional tone of the voice narration and add effects and music according to the emotion. For example, it can add emotional music to audio with an emotional tone and energetic music to audio with an excited tone. This allows for the generation of videos based on the creator's voice narration.

[0089] The generator can also obtain the user's real-time location information and generate videos related to that location. For example, if the user is at a tourist spot, it can generate a video introducing the tourist spot's famous sights and history. The generator can also select optimal scenes and effects using an algorithm to customize the content of the video based on location information. For example, the generator AI can select optimal tourist spots and events based on the user's location data and reflect them in the video. This allows it to generate videos based on the user's real-time location information.

[0090] The generation unit can also generate personalized videos based on data obtained from a user's social media account. For example, it can analyze photos and posts shared by the user on social media and generate videos based on that. The generation unit can also select optimal scenes and effects using an algorithm to customize the content of the video based on social media data. For example, the generation AI can select optimal scenes and effects based on the user's social media data and reflect them in the video. This allows for the generation of personalized videos based on the user's social media data.

[0091] The generation unit can also analyze handwritten sketches and illustrations provided by creators and generate videos based on them. For example, it can scan characters or landscapes drawn by creators and generate animated videos based on those sketches. The generation unit can also select optimal scenes and effects using an algorithm for analyzing handwritten sketches and generating videos. For example, the generation AI can select optimal animated scenes based on the creator's sketch data and reflect them in the video. This allows it to generate videos based on the creator's handwritten sketches.

[0092] The generation unit can also analyze the user's emotions and customize the video's ending based on those emotions. For example, if the user expresses emotion, it can add an emotional ending. The generation unit can also select optimal scenes and effects using an algorithm to customize the ending based on emotion. For example, the generation AI can select the optimal ending scene based on the user's emotional data and reflect it in the video. This allows the ending to be customized based on the user's emotions.

[0093] The generation unit can also add interactive elements to the video it generates, generating videos in which the story changes depending on the options the user selects within the video. For example, it can generate videos in which the user can reach different endings depending on the options they select. The generation unit can also select optimal options and scenes using an algorithm to add interactive elements. For example, the generation AI can select the optimal story development based on the user's selection data and reflect it in the video. This makes it possible to generate interactive videos in which the story changes depending on the options the user selects.

[0094] The generation unit can also analyze the user's emotions and customize the video narration based on those emotions. For example, if the user expresses excitement, it can add an energetic narration. The generation unit can also select the optimal narration using an algorithm for customizing narration based on emotions. For example, the generation AI can select the optimal narration based on the user's emotional data and reflect it in the video. This allows the narration to be customized based on the user's emotions.

[0095] The generation unit can also analyze text stories provided by users and generate videos based on them. For example, it can analyze short stories or essays written by users and generate scenes based on their content. The generation unit can also select optimal scenes and effects using an algorithm for analyzing text stories and generating videos. For example, the generation AI can select optimal scenes and effects based on the user's text data and reflect them in the video. This allows it to generate videos based on the user's text story.

[0096] The generation unit can also analyze the user's emotions and adjust the tempo of the video based on those emotions. For example, if the user expresses a feeling of relaxation, a video with a slow tempo is generated. The generation unit can also select the optimal tempo using an algorithm for adjusting the tempo based on emotions. For example, the generation AI can select the optimal tempo based on the user's emotional data and reflect it in the video. This allows the tempo to be adjusted based on the user's emotions.

[0097] The generation unit can also analyze photos and images provided by the user and generate videos based on them. For example, it can analyze photos taken by the user while traveling and generate a video that looks back on the memories of the trip based on those photos. The generation unit can also select optimal scenes and effects using an algorithm for analyzing photos and images to generate videos. For example, the generation AI can select optimal scenes and effects based on the user's photo data and reflect them in the video. This allows it to generate videos based on the user's photos and images.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The generator generates videos based on the creator's instructions. For example, if the creator inputs a prompt such as "Please create a video with a natural landscape theme," the generator analyzes the instructions and generates multiple videos. The generator can also use pre-fine-tuned generation AI to generate high-quality videos according to the creator's instructions. Step 2: The listing department lists the video generated by the generation department on the NFT market. For example, the listing department can automatically generate metadata and descriptions for the video generated by the generation AI to support the listing process. The listing department can also list the video on an NFT market (e.g., OpenSea or Rarible). Step 3: The evaluation unit analyzes sales data and user responses for the videos listed by the listing unit to evaluate the creator's market value. For example, the evaluation unit collects and analyzes data such as which videos attracted the most attention and at what price range they sold. The evaluation unit can also use an algorithm to evaluate the creator's market value and calculate the market value based on sales data and user responses.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates videos based on instructions from creators; A listing unit that lists the video generated by the generation unit on an NFT market; an evaluation unit that analyzes sales data and user reactions of the video put up for sale by the selling unit to evaluate the market value of the creator. A system characterized by:

2. The generation unit Analyzing the user's emotions in real time and generating variations of the video in accordance with the emotions 2. The system of claim 1.

3. The generation unit Automatically add music or sound effects to the video, engaging the user both visually and aurally.

2. The system of claim 1.

4. The exhibit section: Automatically add tags based on the user's emotional reactions to the video metadata to improve searchability 2. The system of claim 1.

5. The evaluation unit Analyzing the sales data of the videos in real time and updating the market value of the creators 2. The system of claim 1.

6. The evaluation unit Identifying the market value of the creator for whom the user has the most positive feelings 2. The system of claim 1.

7. The evaluation unit Analyze the creator's emotions and propose a promotion strategy based on those emotions.

2. The system of claim 1.

8. The evaluation unit Analyzing the user's emotional response and making improvement suggestions based on the user's emotions 2. The system of claim 1.

Images