system

Abstract

The system according to this embodiment aims to provide a platform for self-expression for individuals with specialized knowledge and unique perspectives. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a conversion unit, and a provision unit. The reception unit receives monologue movies from users. The analysis unit analyzes the content of the monologue movies received by the reception unit. The generation unit generates lyrics based on the content analyzed by the analysis unit. The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The provision unit provides the rap generated by the conversion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for people with specialized knowledge or unique opinions to find a place for self-expression.

[0005] The system according to the embodiment aims to enable people with specialized knowledge or unique opinions to find a place for self-expression.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a conversion unit, and a provision unit. The reception unit receives monologue videos from users. The analysis unit analyzes the content of the monologue videos received by the reception unit. The generation unit generates lyrics based on the content analyzed by the analysis unit. The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The provision unit provides the rap generated by the conversion unit. [Effects of the Invention]

[0007] The system according to this embodiment allows people with specialized knowledge and unique perspectives to find a platform for self-expression. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] [[ID=1,4]]As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The self-expression support system according to an embodiment of the present invention is a system for solving the problem that people with passionate and specialized knowledge and unique viewpoints cannot find a place to express themselves. This self-expression support system is a system in which, when a user posts a monologue video, a generating AI automatically converts the content into a superb rap with heartfelt, danceable, and sometimes sweet lyrics. The self-expression support system allows users to post monologue videos, which contain the user's passionate topics and specialized knowledge. For example, themes such as "The history of beautiful girl figurines" or "A consideration of the landing pose of the Scopedog" are possible. Next, the generating AI analyzes the content of the posted video and generates lyrics. Based on the content of the video, the generating AI spins heartfelt, danceable, and sometimes sweet lyrics. For example, based on the user's topic, the generating AI generates emotionally rich lyrics and converts them into a rap with a delicate yet powerful sound. The generated rap expresses the passionate feelings hidden within the user and captures the hearts of the audience. For example, the generated rap possesses both the delicate quality of a faint breath and the ferocity of a Ferrari engine. This system provides a new platform for self-expression, allowing individuals to share their passion and expertise in highly entertaining rap. This increases the chances of even niche topics, which were previously difficult to share, gaining widespread interest and potentially attracting global attention. For instance, users can share their generated raps on social media and video streaming services, instantly rising to stardom and spreading their message widely. Furthermore, the system automatically translates into various languages ​​and supports fast-paced rap. This allows users worldwide to express their passions and expertise through rap, expanding cultural and knowledge diversity. In short, this self-expression support system enables users to share their passionate topics and specialized knowledge in highly entertaining rap.

[0029] The self-expression support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a conversion unit, and a provision unit. The reception unit receives monologue videos from users. The monologue videos include, for example, topics the user is passionate about or specialized knowledge. The reception unit receives, for example, video files uploaded by users. The reception unit can also receive videos recorded by users in real time. Furthermore, the reception unit can also receive videos via URLs provided by users. The analysis unit analyzes the content of the monologue videos received by the reception unit. The analysis unit converts the audio in the video into text using, for example, natural language processing technology. The analysis unit can also analyze the audio in the video using speech recognition technology. Furthermore, the analysis unit can analyze the video to recognize the user's facial expressions and gestures. The generation unit generates lyrics based on the content analyzed by the analysis unit. The generation unit generates emotionally rich lyrics using, for example, a generation AI. The generation unit inputs the analyzed text data into the generation AI and causes it to output lyrics. The generation unit can further edit the lyrics generated by the generation AI to make them more emotionally expressive. The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The conversion unit generates sounds suitable for the lyrics, for example, using an AI that generates rhythms and beats. The conversion unit generates rap by setting the lyrics to the generated sound. The conversion unit can further edit the generated rap to make it more complete. The provision unit provides the rap generated by the conversion unit to the user. The provision unit saves the generated rap to the user's account, for example. The provision unit can also upload the generated rap to a platform specified by the user. Furthermore, the provision unit can send the generated rap to the user via email. In this way, the self-expression support system according to the embodiment can provide a platform for self-expression by analyzing the user's monologue video, generating lyrics, converting them into rap, and providing them.

[0030] The reception desk accepts monologue videos from users. These monologue videos may include, for example, topics the user is passionate about or their area of ​​expertise. The reception desk accepts video files uploaded by users, and can also accept videos recorded by users in real time. Furthermore, the reception desk can accept videos via URLs provided by users. Specifically, the reception desk provides an interface for users to upload video files directly from their devices. This interface is designed to allow users to easily select and upload videos. For real-time recordings, the reception desk provides a function that allows users to record directly through a browser or dedicated application. This allows users to record monologue videos on the spot and send them to the system immediately. In addition, the reception desk can incorporate videos that users have already uploaded to other platforms by entering the URL of the video. In this case, the reception desk retrieves the video data from the specified URL and converts it into a format that can be processed within the system. This allows users to provide monologue videos in a variety of ways, making the system easy to use. The reception unit also has a data management function that temporarily stores the received video data, making it accessible to the analysis and generation units. This allows the reception unit to efficiently receive video data from users and ensure smooth processing of the entire system.

[0031] The analysis unit analyzes the content of the monologue videos received by the reception unit. For example, the analysis unit converts the audio in the video into text using natural language processing technology. The analysis unit can also analyze the audio in the video using speech recognition technology. Furthermore, the analysis unit can analyze the video to recognize the user's facial expressions and gestures. Specifically, the analysis unit extracts the audio data from the video and converts it into text data using speech recognition technology. In this process, the speech recognition engine analyzes the audio waveform and outputs the spoken content as a string of characters. Next, natural language processing technology is used to analyze the converted text data and understand the context and emotions. For example, keywords and phrases in the text are extracted to identify the user's topic and emotional tone. The analysis unit also analyzes the video data and uses computer vision technology to recognize the user's facial expressions and gestures. This allows for a more accurate understanding of the user's emotions and intentions. For example, facial expression recognition technology is used to analyze the user's facial expressions and identify emotions such as joy, sadness, and anger. Furthermore, gesture recognition technology is used to analyze the user's hand and body movements to understand the emphasis and important points of the speech. This allows the analysis unit to integrate information obtained from both audio and video, enabling a comprehensive analysis of the user's monologue video. The analysis unit then provides these analysis results to the generation unit, which provides the information necessary for generating lyrics.

[0032] The generation unit generates lyrics based on the content analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate emotionally rich lyrics. The generation unit inputs the analyzed text data into the generation AI and outputs lyrics. The generation unit can also further edit the lyrics generated by the generation AI to make them more emotionally rich. Specifically, the generation unit inputs the text data provided by the analysis unit into the generation AI and generates lyrics. The generation AI uses natural language generation technology to generate emotionally rich lyrics based on the input text data. In this process, the generation AI understands the context and emotions of the text data and generates lyrics accordingly. For example, if the user is talking about a passionate topic, the generation AI will generate lyrics that reflect that passion. The generation unit also further edits the lyrics generated by the generation AI to make them more emotionally rich. For example, it can improve the quality of the lyrics by modifying parts of them or inserting additional phrases. The generation unit verifies that the generated lyrics accurately reflect the user's intentions and emotions and generates the final lyrics. This allows the generation unit to create emotionally rich and engaging lyrics based on the content of the user's monologue video.

[0033] The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The conversion unit generates sounds suitable for the lyrics, for example, using AI that generates rhythms and beats. The conversion unit then generates rap by setting the lyrics to the generated sound. The conversion unit can also further edit the generated rap to make it more polished. Specifically, the conversion unit uses AI that generates rhythms and beats based on the lyrics provided by the generation unit to generate sounds suitable for the lyrics. In this process, the AI ​​generates the optimal rhythm and beat according to the tempo and emotion of the lyrics. For example, it generates sounds that correspond to the content of the lyrics, such as speeding up the tempo in emotionally charged parts and slowing down the tempo in calmer parts. Next, the conversion unit generates rap by setting the lyrics to the generated sound. In this process, it adjusts the sound and lyrics according to the pronunciation and rhythm of the lyrics to generate a natural-sounding rap. Furthermore, the conversion unit edits the generated rap, adjusting the sound quality and balance to produce a more polished rap. For example, it can adjust the volume or add effects to improve the quality of the rap. This allows the conversion unit to take the lyrics generated by the generation unit and set them to sound, creating an engaging rap.

[0034] The service provider delivers the rap generated by the conversion unit to the user. For example, the service provider saves the generated rap to the user's account. The service provider can also upload the generated rap to a platform specified by the user. Furthermore, the service provider can send the generated rap to the user via email. Specifically, the service provider provides a function to save the rap data provided by the conversion unit to the user's account. The user can view the generated rap at any time by logging into their account. The service provider also provides a function to upload the rap to a platform specified by the user. For example, if the user wants to share the rap on their social media account, the service provider can upload the rap directly to the user's social media account. Furthermore, the service provider also provides a function to send the generated rap to the user via email. This allows the user to easily receive the generated rap. Through these functions, the service provider can deliver the generated rap to the user quickly and reliably. In this way, the service provider can support the user's self-expression and deliver the generated rap in a variety of ways.

[0035] The analysis unit can analyze the content of a monologue movie using natural language processing and speech recognition technologies. For example, the analysis unit can convert the audio in the monologue movie into text using natural language processing technology. For example, the analysis unit can divide the audio data into words using morphological analysis and perform grammatical analysis. The analysis unit can also understand the meaning of the audio data using semantic analysis. Furthermore, the analysis unit can analyze the audio in the monologue movie using speech recognition technology. For example, the analysis unit can extract audio features and analyze the audio data using a speech model. This allows the analysis unit to accurately analyze the content of the monologue movie. Natural language processing technologies include, for example, morphological analysis, grammatical analysis, and semantic analysis. Speech recognition technologies include, for example, audio feature extraction and the use of speech models. This allows the analysis unit to accurately analyze the content of a monologue movie by using natural language processing and speech recognition technologies.

[0036] The generation unit can generate emotionally rich lyrics based on the information obtained by the analysis unit. For example, the generation unit can use a generation AI to generate lyrics based on the analyzed information. The generation unit inputs the analyzed text data into the generation AI and causes it to output emotionally rich lyrics. For example, the generation unit can input a prompt to the generation AI such as "Please express this content emotionally" and receive the generated lyrics. The generation unit can also further edit the lyrics generated by the generation AI to make them even more emotionally rich. In this way, the generation unit can generate emotionally rich lyrics based on the information obtained by the analysis unit. Emotionally rich lyrics include, for example, the type of emotion and the method of expression. In this way, by generating emotionally rich lyrics based on the information obtained by the analysis unit, it is possible to provide rap that resonates with the audience's hearts.

[0037] The conversion unit can transform lyrics generated by the generation unit into rap by setting them to a delicate yet powerful sound. The conversion unit generates sounds suitable for the lyrics, for example, using an AI that generates rhythms and beats. The conversion unit then sets the lyrics to the generated sound to create the rap. For example, the conversion unit prompts the generation AI with "Please generate a sound that suits these lyrics" and receives the generated sound. The conversion unit then sets the lyrics to the generated sound to create the rap. The conversion unit can also further edit the generated rap to make it more polished. In this way, the conversion unit can transform generated lyrics into rap by setting them to a delicate yet powerful sound. Delicate and powerful sounds include, for example, the instruments and genre of music used. In this way, by transforming generated lyrics into rap by setting them to a delicate yet powerful sound, it is possible to deliver rap that resonates with the audience.

[0038] The service provider can provide the user with the rap generated by the conversion unit. The service provider can, for example, save the generated rap to the user's account. The service provider can also upload the generated rap to a platform specified by the user. For example, the service provider can upload the generated rap to social media or video streaming services. The service provider can also send the generated rap to the user via email. In this way, the service provider can provide the user with the generated rap. By providing the user with the generated rap, the service provider can provide a platform for self-expression.

[0039] The service provider can automatically translate the generated raps into languages ​​corresponding to each country. For example, the service provider inputs the generated raps into an automatic translation system and translates them into languages ​​corresponding to each country. The service provider then provides the translated raps to the users. For example, the service provider saves the translated raps to the user's account. The service provider can also upload the translated raps to a platform specified by the user. This allows the service provider to automatically translate the generated raps into languages ​​corresponding to each country. These languages ​​include, for example, English, Japanese, and French. This makes the generated raps available to users all over the world by automatically translating them into languages ​​corresponding to each country.

[0040] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, the reception desk can analyze the times when a user frequently posted in the past and accept submissions during those times. The reception desk prioritizes suggesting posting methods (text, audio, etc.) that the user has used in the past. The reception desk analyzes the content of a user's past posts and prioritizes accepting monologue videos on related themes. In this way, the reception desk can select the most suitable reception method by analyzing a user's past posting history. The most suitable reception method includes, for example, the timing and type of reception. In this way, the reception desk can select the most suitable reception method by analyzing a user's past posting history.

[0041] The reception system can filter monologue videos based on the user's current areas of interest when receiving them. For example, the reception system can prioritize receiving monologue videos related to themes the user is currently interested in. The reception system can filter monologue videos that contain relevant keywords based on the user's areas of interest. The reception system can prioritize receiving monologue videos in specific categories based on the user's areas of interest. This allows the reception system to filter based on the user's current areas of interest. Current areas of interest include, for example, survey results and past behavioral history. By filtering based on the user's current areas of interest, the reception system can prioritize receiving highly relevant monologue videos.

[0042] The reception desk can prioritize receiving monologue videos that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving monologue videos related to that region. The reception desk will filter monologue videos related to local events and topics based on the user's geographical location. The reception desk will also prioritize receiving monologue videos related to local culture and customs based on the user's geographical location. This allows the reception desk to prioritize receiving monologue videos that are highly relevant, taking into account the user's geographical location. Geographical location information includes, for example, how location information is obtained and the criteria for evaluating relevance. This allows the reception desk to prioritize receiving monologue videos that are highly relevant by considering the user's geographical location.

[0043] The reception desk can analyze the user's social media activity when receiving a monologue video and accept relevant videos. For example, the reception desk can prioritize accepting monologue videos related to themes that the user frequently posts about on social media. The reception desk can filter monologue videos related to themes that the user's social media followers and friends are interested in. The reception desk can prioritize accepting relevant monologue videos based on the user's social media activity history. This allows the reception desk to analyze the user's social media activity when receiving a monologue video and accept relevant videos. Social media activity includes, for example, posts and follower reactions. By analyzing the user's social media activity, the reception desk can prioritize accepting relevant monologue videos.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie during the analysis. For example, the analysis unit performs a detailed analysis for high-importance monologue movies. For low-importance monologue movies, the analysis unit performs a simplified analysis. The analysis unit adjusts the depth and scope of the analysis according to the importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie. The importance of a monologue movie includes, for example, the depth of its content and the number of views. By adjusting the level of detail of the analysis based on the importance of the monologue movie, appropriate analysis results can be provided.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the monologue movie during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical monologue movies. For entertainment monologue movies, the analysis unit applies an analysis algorithm that emphasizes emotional analysis. For educational monologue movies, the analysis unit applies an analysis algorithm that evaluates the degree of comprehension of the content. This allows the analysis unit to apply different analysis algorithms depending on the category of the monologue movie. The categories of monologue movies include, for example, genre and theme. By applying different analysis algorithms depending on the category of the monologue movie, appropriate analysis results can be provided.

[0046] The analysis unit can determine the priority of analysis based on the submission date of the monologue movies. For example, the analysis unit will prioritize the analysis of recently submitted monologue movies. The analysis unit will postpone the analysis of older monologue movies. The analysis unit dynamically adjusts the analysis priority according to the submission date. This allows the analysis unit to determine the priority of analysis based on the submission date of the monologue movies. The submission date includes, for example, the submission date and time and the submission order. By determining the priority of analysis based on the submission date of the monologue movies, the analysis can be performed in an appropriate order.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the monologue movies during the analysis process. For example, the analysis unit prioritizes analyzing highly relevant monologue movies. The analysis unit postpones analyzing less relevant monologue movies. The analysis unit dynamically adjusts the order of analysis according to relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the monologue movies. Relevance includes, for example, the degree of content similarity and relevant keywords. By adjusting the order of analysis based on the relevance of the monologue movies, the analysis can be performed in an appropriate order.

[0048] The generation unit can adjust the level of detail in the lyrics based on the content of the monologue movie when generating lyrics. For example, the generation unit will generate detailed lyrics for a detailed monologue movie. For a concise monologue movie, the generation unit will generate concise lyrics. The generation unit adjusts the level of detail in the lyrics according to the complexity of the content. This allows the generation unit to adjust the level of detail in the lyrics based on the content of the monologue movie. The level of detail in the lyrics includes, for example, the depth of the content and the amount of information. By adjusting the level of detail in the lyrics based on the content of the monologue movie, appropriate lyrics can be generated.

[0049] The generation unit can apply different generation algorithms depending on the category of the monologue movie when generating lyrics. For example, the generation unit applies a specialized lyric generation algorithm to technical monologue movies. For entertainment monologue movies, it applies an emotionally rich lyric generation algorithm. For educational monologue movies, it applies a lyric generation algorithm that enhances comprehension of the content. This allows the generation unit to apply different generation algorithms depending on the category of the monologue movie. The categories of monologue movies include, for example, genre and theme. By applying different generation algorithms depending on the category of the monologue movie, appropriate lyrics can be generated.

[0050] The generation unit can determine the priority of lyrics based on the submission date of the monologue videos when generating lyrics. For example, the generation unit will prioritize converting recently submitted monologue videos into lyrics. The generation unit will postpone the generation of older monologue videos. The generation unit dynamically adjusts the priority of lyric generation according to the submission date. This allows the generation unit to determine the priority of lyrics based on the submission date of the monologue videos. The priority of lyrics includes, for example, the submission date and time and the importance of the content. This allows the lyrics to be generated in the appropriate order by determining the priority of lyrics based on the submission date of the monologue videos.

[0051] The generation unit can adjust the order of lyrics based on the relevance of the monologue videos when generating lyrics. For example, the generation unit prioritizes converting highly relevant monologue videos into lyrics. The generation unit postpones less relevant monologue videos. The generation unit dynamically adjusts the order of lyric generation according to relevance. This allows the generation unit to adjust the order of lyrics based on the relevance of the monologue videos. Lyric relevance includes, for example, the degree of content matching and relevant keywords. This allows the generation unit to generate lyrics in an appropriate order by adjusting the order of lyrics based on the relevance of the monologue videos.

[0052] The conversion unit can adjust the level of detail of the sound based on the content of the lyrics when converting to rap. For example, the conversion unit applies complex sounds to detailed lyrics. For concise lyrics, the conversion unit applies simple sounds. The conversion unit dynamically adjusts the level of detail of the sound according to the content of the lyrics. This allows the conversion unit to adjust the level of detail of the sound based on the content of the lyrics. The level of detail of the sound includes, for example, the volume and type of instrument. By adjusting the level of detail of the sound based on the content of the lyrics, it is possible to generate appropriate rap.

[0053] The conversion unit can apply different conversion algorithms depending on the category of the lyrics when converting them to rap. For example, the conversion unit applies a specialized sound conversion algorithm to technical lyrics. For entertainment lyrics, it applies an emotionally rich sound conversion algorithm. For educational lyrics, it applies a sound conversion algorithm that enhances comprehension of the content. This allows the conversion unit to apply different conversion algorithms depending on the category of the lyrics. Lyric categories include, for example, genre and theme. By applying different conversion algorithms depending on the category of the lyrics, appropriate rap can be generated.

[0054] The conversion unit can determine the priority of raps based on when the lyrics were submitted during rap conversion. For example, the conversion unit prioritizes converting recently submitted lyrics into raps. The conversion unit postpones older submitted lyrics. The conversion unit dynamically adjusts the priority of rap conversion according to the submission date. This allows the conversion unit to determine the priority of raps based on when the lyrics were submitted. The priority of raps includes, for example, the submission date and time and the importance of the content. This allows raps to be generated in the appropriate order by determining the priority of raps based on when the lyrics were submitted.

[0055] The conversion unit can adjust the order of raps based on the relevance of the lyrics during rap conversion. For example, the conversion unit prioritizes converting highly relevant lyrics into raps. The conversion unit postpones converting less relevant lyrics. The conversion unit dynamically adjusts the order of rap conversion according to relevance. This allows the conversion unit to adjust the order of raps based on the relevance of the lyrics. The relevance of raps includes, for example, the degree of content matching and relevant keywords. This allows the conversion unit to generate raps in an appropriate order by adjusting the order of raps based on the relevance of the lyrics.

[0056] The service provider can select the optimal delivery method when delivering raps by referring to the user's past viewing history. For example, the service provider can analyze the trends of raps the user has watched in the past and select the optimal delivery method. The service provider can prioritize delivering relevant raps based on the user's viewing history. The service provider can analyze the user's viewing history and select the most interesting delivery method. This allows the service provider to select the optimal delivery method when delivering raps by referring to the user's past viewing history. The optimal delivery method includes, for example, the timing and type of delivery. This allows the service provider to deliver raps in the most optimal way by referring to the user's past viewing history.

[0057] The service provider can customize the content offered when providing wraps based on the user's current areas of interest. For example, the service provider can prioritize providing wraps related to themes the user is currently interested in. The service provider can provide wraps that include relevant keywords based on the user's areas of interest. The service provider can prioritize providing wraps from specific categories based on the user's areas of interest. This allows the service provider to customize the content offered when providing wraps based on the user's current areas of interest. Customization of content includes, for example, methods for selecting content based on user interests. This allows the service provider to provide appropriate wraps by customizing the content based on the user's current areas of interest.

[0058] The service provider can select the optimal delivery method when providing wraps, taking into account the user's geographical location. For example, if a user is in a specific region, the service provider will prioritize providing wraps related to that region. Based on the user's geographical location, the service provider will provide wraps related to local events and topics. Based on the user's geographical location, the service provider will prioritize providing wraps related to local culture and customs. This allows the service provider to select the optimal delivery method when providing wraps, taking into account the user's geographical location. Geographical location information includes, for example, the method of acquiring location information and the criteria for evaluating relevance. This allows the service provider to deliver wraps in the most optimal way by considering the user's geographical location.

[0059] The service provider can analyze the user's social media activity and customize the content delivered when providing wraps. For example, the service provider can prioritize providing wraps related to themes the user frequently posts about on social media. The service provider can provide wraps related to themes that the user's social media followers and friends are interested in. The service provider can prioritize providing relevant wraps based on the user's social media activity history. This allows the service provider to analyze the user's social media activity and customize the content delivered when providing wraps. Social media activity includes, for example, posts and follower reactions. By analyzing the user's social media activity, the service provider can deliver appropriate wraps.

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

[0061] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, it can analyze the times when a user frequently posted in the past and accept submissions during those times. It can also prioritize suggesting posting methods (text, audio, etc.) that the user has used in the past. By analyzing the content of a user's past posts, it can prioritize accepting monologue videos on related themes. In this way, the reception desk can select the most suitable reception method by analyzing a user's past posting history. The optimal reception method includes the timing and type of reception. In this way, it can select the most suitable reception method by analyzing a user's past posting history.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie during the analysis. For example, a detailed analysis is performed for a highly important monologue movie. A simplified analysis is performed for a less important monologue movie. The depth and scope of the analysis are adjusted according to the importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie. The importance of a monologue movie includes the depth of its content and the number of views. By adjusting the level of detail of the analysis based on the importance of the monologue movie, appropriate analysis results can be provided.

[0063] The generation unit can adjust the level of detail in the lyrics based on the content of the monologue movie when generating lyrics. For example, it will generate detailed lyrics for a detailed monologue movie and concise lyrics for a concise monologue movie. It adjusts the level of detail in the lyrics according to the complexity of the content. This allows the generation unit to adjust the level of detail in the lyrics based on the content of the monologue movie. The level of detail in the lyrics includes the depth of the content and the amount of information. By adjusting the level of detail in the lyrics based on the content of the monologue movie, it is possible to generate appropriate lyrics.

[0064] The conversion unit can adjust the level of detail of the sound based on the content of the lyrics when converting to rap. For example, complex sounds are applied to detailed lyrics, and simple sounds are applied to concise lyrics. The level of detail of the sound is dynamically adjusted according to the content of the lyrics. This allows the conversion unit to adjust the level of detail of the sound based on the content of the lyrics. The level of detail of the sound includes the volume and type of instrument. By adjusting the level of detail of the sound based on the content of the lyrics, it is possible to generate appropriate rap.

[0065] The service provider can select the optimal delivery method when delivering raps by referring to the user's past viewing history. For example, it can analyze the trends of raps the user has watched in the past and select the optimal delivery method. Based on the user's viewing history, it can prioritize delivering relevant raps. It can analyze the user's viewing history and select the delivery method that will be most interesting to the user. In this way, the service provider can select the optimal delivery method when delivering raps by referring to the user's past viewing history. The optimal delivery method includes the timing and type of delivery. In this way, by referring to the user's past viewing history, it can deliver raps in the most optimal way.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk accepts monologue videos from users. These monologue videos can include, for example, topics the user is passionate about or their area of ​​expertise. The reception desk accepts video files uploaded by users. It can also accept videos recorded by users in real time or videos provided via URLs. Step 2: The analysis unit analyzes the content of the monologue video received by the reception unit. The analysis unit uses natural language processing and speech recognition technologies to convert the audio in the video into text, and can also analyze the video to recognize the user's facial expressions and gestures. Step 3: The generation unit generates lyrics based on the content analyzed by the analysis unit. The generation unit uses a generation AI to generate emotionally rich lyrics and outputs lyrics by inputting the analyzed text data. The generation unit can also further edit the lyrics generated by the generation AI to make them even more emotionally expressive. Step 4: The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The conversion unit uses AI to generate rhythms and beats, creating sounds suitable for the lyrics, and then sets the lyrics to the generated sounds to create the rap. The conversion unit can also further edit the generated rap to make it more polished. Step 5: The provider provides the user with the wrap generated by the conversion unit. The provider can save the generated wrap to the user's account, upload it to the platform specified by the user, or send it via email.

[0068] (Example of form 2) The self-expression support system according to an embodiment of the present invention is a system for solving the problem that people with passionate and specialized knowledge and unique viewpoints cannot find a place to express themselves. This self-expression support system is a system in which, when a user posts a monologue video, a generating AI automatically converts the content into a superb rap with heartfelt, danceable, and sometimes sweet lyrics. The self-expression support system allows users to post monologue videos, which contain the user's passionate topics and specialized knowledge. For example, themes such as "The history of beautiful girl figurines" or "A consideration of the landing pose of the Scopedog" are possible. Next, the generating AI analyzes the content of the posted video and generates lyrics. Based on the content of the video, the generating AI spins heartfelt, danceable, and sometimes sweet lyrics. For example, based on the user's topic, the generating AI generates emotionally rich lyrics and converts them into a rap with a delicate yet powerful sound. The generated rap expresses the passionate feelings hidden within the user and captures the hearts of the audience. For example, the generated rap possesses both the delicate quality of a faint breath and the ferocity of a Ferrari engine. This system provides a new platform for self-expression, allowing individuals to share their passion and expertise in highly entertaining rap. This increases the chances of even niche topics, which were previously difficult to share, gaining widespread interest and potentially attracting global attention. For instance, users can share their generated raps on social media and video streaming services, instantly rising to stardom and spreading their message widely. Furthermore, the system automatically translates into various languages ​​and supports fast-paced rap. This allows users worldwide to express their passions and expertise through rap, expanding cultural and knowledge diversity. In short, this self-expression support system enables users to share their passionate topics and specialized knowledge in highly entertaining rap.

[0069] The self-expression support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a conversion unit, and a provision unit. The reception unit receives monologue videos from users. The monologue videos include, for example, topics the user is passionate about or specialized knowledge. The reception unit receives, for example, video files uploaded by users. The reception unit can also receive videos recorded by users in real time. Furthermore, the reception unit can also receive videos via URLs provided by users. The analysis unit analyzes the content of the monologue videos received by the reception unit. The analysis unit converts the audio in the video into text using, for example, natural language processing technology. The analysis unit can also analyze the audio in the video using speech recognition technology. Furthermore, the analysis unit can analyze the video to recognize the user's facial expressions and gestures. The generation unit generates lyrics based on the content analyzed by the analysis unit. The generation unit generates emotionally rich lyrics using, for example, a generation AI. The generation unit inputs the analyzed text data into the generation AI and causes it to output lyrics. The generation unit can further edit the lyrics generated by the generation AI to make them more emotionally expressive. The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The conversion unit generates sounds suitable for the lyrics, for example, using an AI that generates rhythms and beats. The conversion unit generates rap by setting the lyrics to the generated sound. The conversion unit can further edit the generated rap to make it more complete. The provision unit provides the rap generated by the conversion unit to the user. The provision unit saves the generated rap to the user's account, for example. The provision unit can also upload the generated rap to a platform specified by the user. Furthermore, the provision unit can send the generated rap to the user via email. In this way, the self-expression support system according to the embodiment can provide a platform for self-expression by analyzing the user's monologue video, generating lyrics, converting them into rap, and providing them.

[0070] The reception desk accepts monologue videos from users. These monologue videos may include, for example, topics the user is passionate about or their area of ​​expertise. The reception desk accepts video files uploaded by users, and can also accept videos recorded by users in real time. Furthermore, the reception desk can accept videos via URLs provided by users. Specifically, the reception desk provides an interface for users to upload video files directly from their devices. This interface is designed to allow users to easily select and upload videos. For real-time recordings, the reception desk provides a function that allows users to record directly through a browser or dedicated application. This allows users to record monologue videos on the spot and send them to the system immediately. In addition, the reception desk can incorporate videos that users have already uploaded to other platforms by entering the URL of the video. In this case, the reception desk retrieves the video data from the specified URL and converts it into a format that can be processed within the system. This allows users to provide monologue videos in a variety of ways, making the system easy to use. The reception unit also has a data management function that temporarily stores the received video data, making it accessible to the analysis and generation units. This allows the reception unit to efficiently receive video data from users and ensure smooth processing of the entire system.

[0071] The analysis unit analyzes the content of the monologue videos received by the reception unit. For example, the analysis unit converts the audio in the video into text using natural language processing technology. The analysis unit can also analyze the audio in the video using speech recognition technology. Furthermore, the analysis unit can analyze the video to recognize the user's facial expressions and gestures. Specifically, the analysis unit extracts the audio data from the video and converts it into text data using speech recognition technology. In this process, the speech recognition engine analyzes the audio waveform and outputs the spoken content as a string of characters. Next, natural language processing technology is used to analyze the converted text data and understand the context and emotions. For example, keywords and phrases in the text are extracted to identify the user's topic and emotional tone. The analysis unit also analyzes the video data and uses computer vision technology to recognize the user's facial expressions and gestures. This allows for a more accurate understanding of the user's emotions and intentions. For example, facial expression recognition technology is used to analyze the user's facial expressions and identify emotions such as joy, sadness, and anger. Furthermore, gesture recognition technology is used to analyze the user's hand and body movements to understand the emphasis and important points of the speech. This allows the analysis unit to integrate information obtained from both audio and video, enabling a comprehensive analysis of the user's monologue video. The analysis unit then provides these analysis results to the generation unit, which provides the information necessary for generating lyrics.

[0072] The generation unit generates lyrics based on the content analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate emotionally rich lyrics. The generation unit inputs the analyzed text data into the generation AI and outputs lyrics. The generation unit can also further edit the lyrics generated by the generation AI to make them more emotionally rich. Specifically, the generation unit inputs the text data provided by the analysis unit into the generation AI and generates lyrics. The generation AI uses natural language generation technology to generate emotionally rich lyrics based on the input text data. In this process, the generation AI understands the context and emotions of the text data and generates lyrics accordingly. For example, if the user is talking about a passionate topic, the generation AI will generate lyrics that reflect that passion. The generation unit also further edits the lyrics generated by the generation AI to make them more emotionally rich. For example, it can improve the quality of the lyrics by modifying parts of them or inserting additional phrases. The generation unit verifies that the generated lyrics accurately reflect the user's intentions and emotions and generates the final lyrics. This allows the generation unit to create emotionally rich and engaging lyrics based on the content of the user's monologue video.

[0073] The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The conversion unit generates sounds suitable for the lyrics, for example, using AI that generates rhythms and beats. The conversion unit then generates rap by setting the lyrics to the generated sound. The conversion unit can also further edit the generated rap to make it more polished. Specifically, the conversion unit uses AI that generates rhythms and beats based on the lyrics provided by the generation unit to generate sounds suitable for the lyrics. In this process, the AI ​​generates the optimal rhythm and beat according to the tempo and emotion of the lyrics. For example, it generates sounds that correspond to the content of the lyrics, such as speeding up the tempo in emotionally charged parts and slowing down the tempo in calmer parts. Next, the conversion unit generates rap by setting the lyrics to the generated sound. In this process, it adjusts the sound and lyrics according to the pronunciation and rhythm of the lyrics to generate a natural-sounding rap. Furthermore, the conversion unit edits the generated rap, adjusting the sound quality and balance to produce a more polished rap. For example, it can adjust the volume or add effects to improve the quality of the rap. This allows the conversion unit to take the lyrics generated by the generation unit and set them to sound, creating an engaging rap.

[0074] The service provider delivers the rap generated by the conversion unit to the user. For example, the service provider saves the generated rap to the user's account. The service provider can also upload the generated rap to a platform specified by the user. Furthermore, the service provider can send the generated rap to the user via email. Specifically, the service provider provides a function to save the rap data provided by the conversion unit to the user's account. The user can view the generated rap at any time by logging into their account. The service provider also provides a function to upload the rap to a platform specified by the user. For example, if the user wants to share the rap on their social media account, the service provider can upload the rap directly to the user's social media account. Furthermore, the service provider also provides a function to send the generated rap to the user via email. This allows the user to easily receive the generated rap. Through these functions, the service provider can deliver the generated rap to the user quickly and reliably. In this way, the service provider can support the user's self-expression and deliver the generated rap in a variety of ways.

[0075] The analysis unit can analyze the content of a monologue movie using natural language processing and speech recognition technologies. For example, the analysis unit can convert the audio in the monologue movie into text using natural language processing technology. For example, the analysis unit can divide the audio data into words using morphological analysis and perform grammatical analysis. The analysis unit can also understand the meaning of the audio data using semantic analysis. Furthermore, the analysis unit can analyze the audio in the monologue movie using speech recognition technology. For example, the analysis unit can extract audio features and analyze the audio data using a speech model. This allows the analysis unit to accurately analyze the content of the monologue movie. Natural language processing technologies include, for example, morphological analysis, grammatical analysis, and semantic analysis. Speech recognition technologies include, for example, audio feature extraction and the use of speech models. This allows the analysis unit to accurately analyze the content of a monologue movie by using natural language processing and speech recognition technologies.

[0076] The generation unit can generate emotionally rich lyrics based on the information obtained by the analysis unit. For example, the generation unit can use a generation AI to generate lyrics based on the analyzed information. The generation unit inputs the analyzed text data into the generation AI and causes it to output emotionally rich lyrics. For example, the generation unit can input a prompt to the generation AI such as "Please express this content emotionally" and receive the generated lyrics. The generation unit can also further edit the lyrics generated by the generation AI to make them even more emotionally rich. In this way, the generation unit can generate emotionally rich lyrics based on the information obtained by the analysis unit. Emotionally rich lyrics include, for example, the type of emotion and the method of expression. In this way, by generating emotionally rich lyrics based on the information obtained by the analysis unit, it is possible to provide rap that resonates with the audience's hearts.

[0077] The conversion unit can transform lyrics generated by the generation unit into rap by setting them to a delicate yet powerful sound. The conversion unit generates sounds suitable for the lyrics, for example, using an AI that generates rhythms and beats. The conversion unit then sets the lyrics to the generated sound to create the rap. For example, the conversion unit prompts the generation AI with "Please generate a sound that suits these lyrics" and receives the generated sound. The conversion unit then sets the lyrics to the generated sound to create the rap. The conversion unit can also further edit the generated rap to make it more polished. In this way, the conversion unit can transform generated lyrics into rap by setting them to a delicate yet powerful sound. Delicate and powerful sounds include, for example, the instruments and genre of music used. In this way, by transforming generated lyrics into rap by setting them to a delicate yet powerful sound, it is possible to deliver rap that resonates with the audience.

[0078] The service provider can provide the user with the rap generated by the conversion unit. The service provider can, for example, save the generated rap to the user's account. The service provider can also upload the generated rap to a platform specified by the user. For example, the service provider can upload the generated rap to social media or video streaming services. The service provider can also send the generated rap to the user via email. In this way, the service provider can provide the user with the generated rap. By providing the user with the generated rap, the service provider can provide a platform for self-expression.

[0079] The service provider can automatically translate the generated raps into languages ​​corresponding to each country. For example, the service provider inputs the generated raps into an automatic translation system and translates them into languages ​​corresponding to each country. The service provider then provides the translated raps to the users. For example, the service provider saves the translated raps to the user's account. The service provider can also upload the translated raps to a platform specified by the user. This allows the service provider to automatically translate the generated raps into languages ​​corresponding to each country. These languages ​​include, for example, English, Japanese, and French. This makes the generated raps available to users all over the world by automatically translating them into languages ​​corresponding to each country.

[0080] The reception system can estimate the user's emotions and adjust the timing of the monologue video reception based on the estimated emotions. For example, if the user is excited, the reception system will immediately start the monologue video reception. If the user is calm, the reception system will reception the monologue video at an appropriate time. If the user is stressed, the reception system will reception the monologue video when a relaxing environment is established. In this way, the reception system can adjust the timing of the monologue video reception according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows the reception to be performed at an appropriate time by adjusting the timing of the monologue video reception according to the user's emotions.

[0081] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, the reception desk can analyze the times when a user frequently posted in the past and accept submissions during those times. The reception desk prioritizes suggesting posting methods (text, audio, etc.) that the user has used in the past. The reception desk analyzes the content of a user's past posts and prioritizes accepting monologue videos on related themes. In this way, the reception desk can select the most suitable reception method by analyzing a user's past posting history. The most suitable reception method includes, for example, the timing and type of reception. In this way, the reception desk can select the most suitable reception method by analyzing a user's past posting history.

[0082] The reception system can filter monologue videos based on the user's current areas of interest when receiving them. For example, the reception system can prioritize receiving monologue videos related to themes the user is currently interested in. The reception system can filter monologue videos that contain relevant keywords based on the user's areas of interest. The reception system can prioritize receiving monologue videos in specific categories based on the user's areas of interest. This allows the reception system to filter based on the user's current areas of interest. Current areas of interest include, for example, survey results and past behavioral history. By filtering based on the user's current areas of interest, the reception system can prioritize receiving highly relevant monologue videos.

[0083] The reception desk can estimate the user's emotions and determine the priority of the monologue videos to be received based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize receiving that monologue video. If the user is calm, the reception desk will receive it with the same priority as other monologue videos. If the user is stressed, the reception desk will postpone receiving that monologue video. In this way, the reception desk can determine the priority of monologue videos according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows the reception desk to process requests in an appropriate order by determining the priority of monologue videos according to the user's emotions.

[0084] The reception desk can prioritize receiving monologue videos that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving monologue videos related to that region. The reception desk will filter monologue videos related to local events and topics based on the user's geographical location. The reception desk will also prioritize receiving monologue videos related to local culture and customs based on the user's geographical location. This allows the reception desk to prioritize receiving monologue videos that are highly relevant, taking into account the user's geographical location. Geographical location information includes, for example, how location information is obtained and the criteria for evaluating relevance. This allows the reception desk to prioritize receiving monologue videos that are highly relevant by considering the user's geographical location.

[0085] The reception desk can analyze the user's social media activity when receiving a monologue video and accept relevant videos. For example, the reception desk can prioritize accepting monologue videos related to themes that the user frequently posts about on social media. The reception desk can filter monologue videos related to themes that the user's social media followers and friends are interested in. The reception desk can prioritize accepting relevant monologue videos based on the user's social media activity history. This allows the reception desk to analyze the user's social media activity when receiving a monologue video and accept relevant videos. Social media activity includes, for example, posts and follower reactions. By analyzing the user's social media activity, the reception desk can prioritize accepting relevant monologue videos.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit will visually highlight the analysis results. If the user is calm, the analysis unit will display the results simply. If the user is stressed, the analysis unit will display the results in a relaxing design. In this way, the analysis unit can adjust the presentation of the analysis according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows the system to provide appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie during the analysis. For example, the analysis unit performs a detailed analysis for high-importance monologue movies. For low-importance monologue movies, the analysis unit performs a simplified analysis. The analysis unit adjusts the depth and scope of the analysis according to the importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie. The importance of a monologue movie includes, for example, the depth of its content and the number of views. By adjusting the level of detail of the analysis based on the importance of the monologue movie, appropriate analysis results can be provided.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the monologue movie during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical monologue movies. For entertainment monologue movies, the analysis unit applies an analysis algorithm that emphasizes emotional analysis. For educational monologue movies, the analysis unit applies an analysis algorithm that evaluates the degree of comprehension of the content. This allows the analysis unit to apply different analysis algorithms depending on the category of the monologue movie. The categories of monologue movies include, for example, genre and theme. By applying different analysis algorithms depending on the category of the monologue movie, appropriate analysis results can be provided.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit will perform a short, concise analysis. If the user is calm, the analysis unit will perform a detailed analysis. If the user is stressed, the analysis unit will perform a short, relaxing analysis. In this way, the analysis unit can adjust the length of the analysis according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows for the provision of appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0090] The analysis unit can determine the priority of analysis based on the submission date of the monologue movies. For example, the analysis unit will prioritize the analysis of recently submitted monologue movies. The analysis unit will postpone the analysis of older monologue movies. The analysis unit dynamically adjusts the analysis priority according to the submission date. This allows the analysis unit to determine the priority of analysis based on the submission date of the monologue movies. The submission date includes, for example, the submission date and time and the submission order. By determining the priority of analysis based on the submission date of the monologue movies, the analysis can be performed in an appropriate order.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the monologue movies during the analysis process. For example, the analysis unit prioritizes analyzing highly relevant monologue movies. The analysis unit postpones analyzing less relevant monologue movies. The analysis unit dynamically adjusts the order of analysis according to relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the monologue movies. Relevance includes, for example, the degree of content similarity and relevant keywords. By adjusting the order of analysis based on the relevance of the monologue movies, the analysis can be performed in an appropriate order.

[0092] The generation unit can estimate the user's emotions and adjust the lyric generation method based on the estimated emotions. For example, if the user is excited, the generation unit will generate energetic lyrics. If the user is calm, the generation unit will generate calm lyrics. If the user is stressed, the generation unit will generate relaxing lyrics. In this way, the generation unit can adjust the lyric generation method according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. This allows for the generation of appropriate lyrics by adjusting the lyric generation method according to the user's emotions.

[0093] The generation unit can adjust the level of detail in the lyrics based on the content of the monologue movie when generating lyrics. For example, the generation unit will generate detailed lyrics for a detailed monologue movie. For a concise monologue movie, the generation unit will generate concise lyrics. The generation unit adjusts the level of detail in the lyrics according to the complexity of the content. This allows the generation unit to adjust the level of detail in the lyrics based on the content of the monologue movie. The level of detail in the lyrics includes, for example, the depth of the content and the amount of information. By adjusting the level of detail in the lyrics based on the content of the monologue movie, appropriate lyrics can be generated.

[0094] The generation unit can apply different generation algorithms depending on the category of the monologue movie when generating lyrics. For example, the generation unit applies a specialized lyric generation algorithm to technical monologue movies. For entertainment monologue movies, it applies an emotionally rich lyric generation algorithm. For educational monologue movies, it applies a lyric generation algorithm that enhances comprehension of the content. This allows the generation unit to apply different generation algorithms depending on the category of the monologue movie. The categories of monologue movies include, for example, genre and theme. By applying different generation algorithms depending on the category of the monologue movie, appropriate lyrics can be generated.

[0095] The generation unit can estimate the user's emotions and adjust the length of the lyrics based on the estimated emotions. For example, if the user is excited, the generation unit will generate short, energetic lyrics. If the user is calm, the generation unit will generate long, gentle lyrics. If the user is stressed, the generation unit will generate short, relaxing lyrics. In this way, the generation unit can adjust the length of the lyrics according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. This allows for the generation of appropriate lyrics by adjusting the length of the lyrics according to the user's emotions.

[0096] The generation unit can determine the priority of lyrics based on the submission date of the monologue videos when generating lyrics. For example, the generation unit will prioritize converting recently submitted monologue videos into lyrics. The generation unit will postpone the generation of older monologue videos. The generation unit dynamically adjusts the priority of lyric generation according to the submission date. This allows the generation unit to determine the priority of lyrics based on the submission date of the monologue videos. The priority of lyrics includes, for example, the submission date and time and the importance of the content. This allows the lyrics to be generated in the appropriate order by determining the priority of lyrics based on the submission date of the monologue videos.

[0097] The generation unit can adjust the order of lyrics based on the relevance of the monologue videos when generating lyrics. For example, the generation unit prioritizes converting highly relevant monologue videos into lyrics. The generation unit postpones less relevant monologue videos. The generation unit dynamically adjusts the order of lyric generation according to relevance. This allows the generation unit to adjust the order of lyrics based on the relevance of the monologue videos. Lyric relevance includes, for example, the degree of content matching and relevant keywords. This allows the generation unit to generate lyrics in an appropriate order by adjusting the order of lyrics based on the relevance of the monologue videos.

[0098] The conversion unit can estimate the user's emotions and adjust the rap conversion method based on the estimated emotions. For example, if the user is excited, the conversion unit converts to an energetic sound. If the user is calm, the conversion unit converts to a calm sound. If the user is stressed, the conversion unit converts to a relaxing sound. In this way, the conversion unit can adjust the rap conversion method according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows for the generation of appropriate rap by adjusting the rap conversion method according to the user's emotions.

[0099] The conversion unit can adjust the level of detail of the sound based on the content of the lyrics when converting to rap. For example, the conversion unit applies complex sounds to detailed lyrics. For concise lyrics, the conversion unit applies simple sounds. The conversion unit dynamically adjusts the level of detail of the sound according to the content of the lyrics. This allows the conversion unit to adjust the level of detail of the sound based on the content of the lyrics. The level of detail of the sound includes, for example, the volume and type of instrument. By adjusting the level of detail of the sound based on the content of the lyrics, it is possible to generate appropriate rap.

[0100] The conversion unit can apply different conversion algorithms depending on the category of the lyrics when converting them to rap. For example, the conversion unit applies a specialized sound conversion algorithm to technical lyrics. For entertainment lyrics, it applies an emotionally rich sound conversion algorithm. For educational lyrics, it applies a sound conversion algorithm that enhances comprehension of the content. This allows the conversion unit to apply different conversion algorithms depending on the category of the lyrics. Lyric categories include, for example, genre and theme. By applying different conversion algorithms depending on the category of the lyrics, appropriate rap can be generated.

[0101] The transformation unit can estimate the user's emotions and adjust the length of the rap based on the estimated emotions. For example, if the user is excited, the transformation unit will generate a short, energetic rap. If the user is calm, the transformation unit will generate a long, gentle rap. If the user is stressed, the transformation unit will generate a short, relaxing rap. In this way, the transformation unit can adjust the length of the rap according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows for the generation of an appropriate rap by adjusting the length of the rap according to the user's emotions.

[0102] The conversion unit can determine the priority of raps based on when the lyrics were submitted during rap conversion. For example, the conversion unit prioritizes converting recently submitted lyrics into raps. The conversion unit postpones older submitted lyrics. The conversion unit dynamically adjusts the priority of rap conversion according to the submission date. This allows the conversion unit to determine the priority of raps based on when the lyrics were submitted. The priority of raps includes, for example, the submission date and time and the importance of the content. This allows raps to be generated in the appropriate order by determining the priority of raps based on when the lyrics were submitted.

[0103] The conversion unit can adjust the order of raps based on the relevance of the lyrics during rap conversion. For example, the conversion unit prioritizes converting highly relevant lyrics into raps. The conversion unit postpones converting less relevant lyrics. The conversion unit dynamically adjusts the order of rap conversion according to relevance. This allows the conversion unit to adjust the order of raps based on the relevance of the lyrics. The relevance of raps includes, for example, the degree of content matching and relevant keywords. This allows the conversion unit to generate raps in an appropriate order by adjusting the order of raps based on the relevance of the lyrics.

[0104] The delivery unit can estimate the user's emotions and adjust the way the rap is delivered based on the estimated emotions. For example, if the user is excited, the delivery unit will deliver the rap in an energetic way. If the user is calm, the delivery unit will deliver the rap in a gentle way. If the user is stressed, the delivery unit will deliver the rap in a relaxing way. In this way, the delivery unit can adjust the way the rap is delivered according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows the rap to be delivered in an appropriate way by adjusting the way the rap is delivered according to the user's emotions.

[0105] The service provider can select the optimal delivery method when delivering raps by referring to the user's past viewing history. For example, the service provider can analyze the trends of raps the user has watched in the past and select the optimal delivery method. The service provider can prioritize delivering relevant raps based on the user's viewing history. The service provider can analyze the user's viewing history and select the most interesting delivery method. This allows the service provider to select the optimal delivery method when delivering raps by referring to the user's past viewing history. The optimal delivery method includes, for example, the timing and type of delivery. This allows the service provider to deliver raps in the most optimal way by referring to the user's past viewing history.

[0106] The service provider can customize the content offered when providing wraps based on the user's current areas of interest. For example, the service provider can prioritize providing wraps related to themes the user is currently interested in. The service provider can provide wraps that include relevant keywords based on the user's areas of interest. The service provider can prioritize providing wraps from specific categories based on the user's areas of interest. This allows the service provider to customize the content offered when providing wraps based on the user's current areas of interest. Customization of content includes, for example, methods for selecting content based on user interests. This allows the service provider to provide appropriate wraps by customizing the content based on the user's current areas of interest.

[0107] The service provider can estimate the user's emotions and determine the priority of rap delivery based on the estimated emotions. For example, if the user is excited, the service provider will prioritize that rap. If the user is calm, the service provider will deliver that rap with the same priority as other raps. If the user is stressed, the service provider will postpone that rap. In this way, the service provider can determine the priority of rap delivery according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. This allows the service provider to deliver raps in an appropriate order by determining the priority of rap delivery according to the user's emotions.

[0108] The service provider can select the optimal delivery method when providing wraps, taking into account the user's geographical location. For example, if a user is in a specific region, the service provider will prioritize providing wraps related to that region. Based on the user's geographical location, the service provider will provide wraps related to local events and topics. Based on the user's geographical location, the service provider will prioritize providing wraps related to local culture and customs. This allows the service provider to select the optimal delivery method when providing wraps, taking into account the user's geographical location. Geographical location information includes, for example, the method of acquiring location information and the criteria for evaluating relevance. This allows the service provider to deliver wraps in the most optimal way by considering the user's geographical location.

[0109] The service provider can analyze the user's social media activity and customize the content delivered when providing wraps. For example, the service provider can prioritize providing wraps related to themes the user frequently posts about on social media. The service provider can provide wraps related to themes that the user's social media followers and friends are interested in. The service provider can prioritize providing relevant wraps based on the user's social media activity history. This allows the service provider to analyze the user's social media activity and customize the content delivered when providing wraps. Social media activity includes, for example, posts and follower reactions. By analyzing the user's social media activity, the service provider can deliver appropriate wraps.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception unit can estimate the user's emotions and adjust the timing of the monologue video reception based on the estimated emotions. For example, if the user is excited, the monologue video reception will start immediately. If the user is calm, the monologue video reception will be performed at an appropriate time. If the user is stressed, the monologue video reception will be performed when a relaxing environment is created. In this way, the reception unit can adjust the timing of the monologue video reception according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows the reception to be performed at an appropriate time by adjusting the timing of the monologue video reception according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is excited, the analysis results are visually highlighted. If the user is calm, the results are displayed simply. If the user is stressed, the results are displayed in a relaxing design. This allows the analysis unit to adjust the presentation of the analysis according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows the system to provide appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0113] The generation unit can estimate the user's emotions and adjust the lyric generation method based on the estimated emotions. For example, if the user is excited, it will generate energetic lyrics. If the user is calm, it will generate gentle lyrics. If the user is stressed, it will generate relaxing lyrics. In this way, the generation unit can adjust the lyric generation method according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. This allows for the generation of appropriate lyrics by adjusting the lyric generation method according to the user's emotions.

[0114] The conversion unit can estimate the user's emotions and adjust the rap conversion method based on the estimated emotions. For example, if the user is excited, it converts to an energetic sound. If the user is calm, it converts to a gentle sound. If the user is stressed, it converts to a relaxing sound. In this way, the conversion unit can adjust the rap conversion method according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows for the generation of appropriate rap by adjusting the rap conversion method according to the user's emotions.

[0115] The delivery unit can estimate the user's emotions and adjust the way the rap is delivered based on those emotions. For example, if the user is excited, the rap will be delivered in an energetic way. If the user is calm, the rap will be delivered in a gentle way. If the user is stressed, the rap will be delivered in a relaxing way. In this way, the delivery unit can adjust the way the rap is delivered according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows the rap to be delivered in an appropriate way by adjusting the delivery method according to the user's emotions.

[0116] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, it can analyze the times when a user frequently posted in the past and accept submissions during those times. It can also prioritize suggesting posting methods (text, audio, etc.) that the user has used in the past. By analyzing the content of a user's past posts, it can prioritize accepting monologue videos on related themes. In this way, the reception desk can select the most suitable reception method by analyzing a user's past posting history. The optimal reception method includes the timing and type of reception. In this way, it can select the most suitable reception method by analyzing a user's past posting history.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie during the analysis. For example, a detailed analysis is performed for a highly important monologue movie. A simplified analysis is performed for a less important monologue movie. The depth and scope of the analysis are adjusted according to the importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the monologue movie. The importance of a monologue movie includes the depth of its content and the number of views. By adjusting the level of detail of the analysis based on the importance of the monologue movie, appropriate analysis results can be provided.

[0118] The generation unit can adjust the level of detail in the lyrics based on the content of the monologue movie when generating lyrics. For example, it will generate detailed lyrics for a detailed monologue movie and concise lyrics for a concise monologue movie. It adjusts the level of detail in the lyrics according to the complexity of the content. This allows the generation unit to adjust the level of detail in the lyrics based on the content of the monologue movie. The level of detail in the lyrics includes the depth of the content and the amount of information. By adjusting the level of detail in the lyrics based on the content of the monologue movie, it is possible to generate appropriate lyrics.

[0119] The conversion unit can adjust the level of detail of the sound based on the content of the lyrics when converting to rap. For example, complex sounds are applied to detailed lyrics, and simple sounds are applied to concise lyrics. The level of detail of the sound is dynamically adjusted according to the content of the lyrics. This allows the conversion unit to adjust the level of detail of the sound based on the content of the lyrics. The level of detail of the sound includes the volume and type of instrument. By adjusting the level of detail of the sound based on the content of the lyrics, it is possible to generate appropriate rap.

[0120] The service provider can select the optimal delivery method when delivering raps by referring to the user's past viewing history. For example, it can analyze the trends of raps the user has watched in the past and select the optimal delivery method. Based on the user's viewing history, it can prioritize delivering relevant raps. It can analyze the user's viewing history and select the delivery method that will be most interesting to the user. In this way, the service provider can select the optimal delivery method when delivering raps by referring to the user's past viewing history. The optimal delivery method includes the timing and type of delivery. In this way, by referring to the user's past viewing history, it can deliver raps in the most optimal way.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk accepts monologue videos from users. These monologue videos can include, for example, topics the user is passionate about or their area of ​​expertise. The reception desk accepts video files uploaded by users. It can also accept videos recorded by users in real time or videos provided via URLs. Step 2: The analysis unit analyzes the content of the monologue video received by the reception unit. The analysis unit uses natural language processing and speech recognition technologies to convert the audio in the video into text, and can also analyze the video to recognize the user's facial expressions and gestures. Step 3: The generation unit generates lyrics based on the content analyzed by the analysis unit. The generation unit uses a generation AI to generate emotionally rich lyrics and outputs lyrics by inputting the analyzed text data. The generation unit can also further edit the lyrics generated by the generation AI to make them even more emotionally expressive. Step 4: The conversion unit converts the lyrics generated by the generation unit into rap by setting them to sound. The conversion unit uses AI to generate rhythms and beats, creating sounds suitable for the lyrics, and then sets the lyrics to the generated sounds to create the rap. The conversion unit can also further edit the generated rap to make it more polished. Step 5: The provider provides the user with the wrap generated by the conversion unit. The provider can save the generated wrap to the user's account, upload it to the platform specified by the user, or send it via email.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversion unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives a monologue movie from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the monologue movie. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates lyrics based on the analyzed content. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the generated lyrics into a rap by setting them to sound. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated rap to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversion unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives a monologue movie from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the monologue movie. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates lyrics based on the analyzed content. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and converts the generated lyrics into a rap by setting them to sound. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated rap to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversion unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives a monologue movie from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the monologue movie. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates lyrics based on the analyzed content. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the generated lyrics into a rap by setting them to sound. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated rap to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversion unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives a monologue movie from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the monologue movie. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates lyrics based on the analyzed content. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the generated lyrics into a rap by setting them to sound. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated rap to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception desk that accepts monologue videos from users, An analysis unit analyzes the content of the monologue movie received by the reception unit, A generation unit that generates lyrics based on the content analyzed by the analysis unit, A conversion unit that converts the lyrics generated by the generation unit into rap by setting them to sound, The system includes a providing unit that provides the wrap generated by the conversion unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We will use natural language processing and speech recognition technologies to analyze the content of the monologue video. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the information obtained by the analysis unit, emotionally rich lyrics are generated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The conversion unit is The lyrics generated by the generation unit are transformed into rap lyrics set to a delicate yet powerful sound. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The conversion unit provides the user with the generated wrap. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The generated raps are automatically translated into languages ​​corresponding to each country. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving the monologue video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past posting history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving monologue videos, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the monologue videos to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting monologue videos, the system prioritizes accepting videos that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a monologue video, the system analyzes the user's social media activity and accepts relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the monologue movie. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the monologue movie. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the monologue videos were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the monologue movies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the lyric generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating lyrics, adjust the level of detail in the lyrics based on the content of the monologue video. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating lyrics, different generation algorithms are applied depending on the category of the monologue video. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the lyrics based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating lyrics, the priority of the lyrics is determined based on the submission timing of the monologue video. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating lyrics, the order of the lyrics is adjusted based on the relevance of the monologue video. The system described in Appendix 1, characterized by the features described herein. (Note 25) The conversion unit is It estimates the user's emotions and adjusts the wrap conversion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The conversion unit is When converting to rap, the level of detail in the sound is adjusted based on the content of the lyrics. The system described in Appendix 1, characterized by the features described herein. (Note 27) The conversion unit is When converting to rap, different conversion algorithms are applied depending on the lyric category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The conversion unit is It estimates the user's emotions and adjusts the length of the wrap based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The conversion unit is When converting to rap, the priority of the raps is determined based on when the lyrics were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The conversion unit is When converting to rap, the order of raps is adjusted based on the relevance of the lyrics. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts how wraps are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing rap content, the system selects the optimal delivery method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing a wrap, customize the content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of providing wraps based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing wraps, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing wraps, the content is customized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts monologue videos from users, An analysis unit analyzes the content of the monologue movie received by the reception unit, A generation unit that generates lyrics based on the content analyzed by the analysis unit, A conversion unit that converts the lyrics generated by the generation unit into rap by setting them to sound, The system includes a providing unit that provides the wrap generated by the conversion unit. A system characterized by the following features.

2. The aforementioned analysis unit, We will use natural language processing and speech recognition technologies to analyze the content of the monologue video. The system according to feature 1.

3. The generating unit is Based on the information obtained by the analysis unit, emotionally rich lyrics are generated. The system according to feature 1.

4. The conversion unit is The lyrics generated by the aforementioned generation unit are then transformed into rap lyrics set to a delicate yet powerful sound. The system according to feature 1.

5. The aforementioned supply unit is, The wrap generated by the conversion unit is provided to the user. The system according to feature 1.

6. The aforementioned supply unit is, The generated raps are automatically translated into languages ​​corresponding to each country. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving monologue videos based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past posting history and select the most suitable submission method. The system according to feature 1.

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