system

Abstract

The system according to the embodiment aims to allow participants to create and evaluate music and video in real time. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a video generation unit, a performance unit, and an evaluation unit. The reception unit receives input of music genre and tempo from participants. The generation unit generates music based on the information received by the reception unit. The video generation unit generates video based on the music generated by the generation unit. The performance unit plays the music and video generated by the generation unit and the video generation unit in real time. The evaluation unit evaluates the music played by the performance unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to hold live events where participants can create and evaluate music and video in real time.

[0005] The system according to the embodiment aims to allow participants to create and evaluate music and video in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a video generation unit, a performance unit, and an evaluation unit. The reception unit receives input of music genre and tempo from participants. The generation unit generates music based on the information received by the reception unit. The video generation unit generates video based on the music generated by the generation unit. The performance unit plays the music and video generated by the generation unit and the video generation unit in real time. The evaluation unit evaluates the music played by the performance unit. [Effects of the Invention]

[0007] The system according to the embodiment allows participants to create and rate music and video in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A live event system according to an embodiment of the present invention uses generative AI to create original music in real time and perform it as a DJ. This system simultaneously generates not only music but also video, stimulating participants' imaginations. Music is ranked, and participants vote via an app based on judging criteria such as enjoyment and emotional impact. The creator owns the copyright, and top-ranked performers can earn revenue through perks (such as collaborations with major vocalists or band performances). Organizers can earn revenue through participation fees, sponsorships, secondary sales, and advertising monetization. For example, participants attend an event and use generative AI to create original music in real time. The generative AI generates music based on participants' input and performs it as a DJ. For example, if a participant specifies a specific genre and tempo, the generative AI generates music accordingly. Next, the generative AI generates video in conjunction with the music. The video changes in sync with the rhythm and melody of the music, stimulating participants' imaginations. For example, the video dynamically changes during exciting parts of the music, providing visual enjoyment. Participants use an app to rate other participants' music. Evaluations are based on criteria such as enjoyment and emotional impact, and a ranking is generated. Top performers are offered perks, such as collaborations with major vocalists or band performances. The copyright belongs to the participant who created the music, and top performers have the opportunity to monetize it. Organizers can earn revenue through participation fees, sponsorships, secondary sales, and advertising monetization. For example, they can resell event footage and receive advertising revenue from sponsors. In this way, live events using generative AI stimulate participants' imaginations and offer new ways to enjoy music and video. Furthermore, participants' evaluations of their work are expected to motivate them and lead to the creation of higher-quality works. This allows the live event system to generate music and video based on participant input, allowing for real-time performance and evaluation.

[0029] A live event system according to an embodiment includes a reception unit, a generation unit, a video generation unit, a performance unit, and an evaluation unit. The reception unit receives input from participants regarding the genre and tempo of music. Participants can specify genres such as classical, jazz, and rock, and tempos such as beats per minute (BPM). The generation unit uses a generation AI to generate music based on the information received by the reception unit. The generation AI generates music according to the genre and tempo specified by the participant. For example, when generating classical music, the generation unit references an existing classical music database and the AI ​​composes new classical music. The generation unit can also generate music for other genres such as jazz and rock. The video generation unit generates video based on the music generated by the generation unit. For example, the video generation unit generates video in accordance with the rhythm and melody of the music. The video generation unit can use the generation AI to generate video based on the rhythm and melody of the music. For example, during exciting parts of the music, the video dynamically changes, providing visual enjoyment. The performance unit performs the music and video generated by the generation unit and the video generation unit in real time. The performance unit distributes the generated music and video in real time, for example, using streaming technology. The performance unit achieves real-time performance using technology to minimize delay. The evaluation unit evaluates the music performed by the performance unit. The evaluation unit evaluates the music based on evaluation criteria such as enjoyment and emotional impact. The evaluation unit can use AI to tally the participants' evaluations and generate rankings. For example, when participants use an app to evaluate other participants' music, the evaluation unit evaluates based on criteria such as enjoyment and emotional impact. This enables the live event system according to the embodiment to generate music and video based on participants' input, and perform and evaluate them in real time.

[0030] The evaluation unit can evaluate music based on evaluation criteria such as enjoyment or emotional impact. For example, when a participant uses the app to evaluate other participants' music, the evaluation unit performs the evaluation based on criteria such as enjoyment or emotional impact. For example, the evaluation unit can evaluate the participants' enjoyment using a questionnaire survey. The evaluation unit can also evaluate the participants' emotional impact using emotion analysis technology. For example, the evaluation unit can analyze changes in the participants' heart rates to evaluate the emotional impact. In this way, the music is evaluated based on evaluation criteria such as enjoyment and emotional impact.

[0031] The evaluation unit can generate rankings based on the evaluation results. For example, the evaluation unit generates rankings based on results of participants using an app to evaluate other participants' music. The evaluation unit can use AI to tally evaluation scores and determine rankings. For example, the evaluation unit generates rankings based on the evaluation scores of participants and determines top rankers. In this way, rankings are generated based on the evaluation results.

[0032] The generation unit can generate music according to the genre and tempo specified by the participant. For example, if a participant specifies classical music, the generation unit generates classical music. The generation unit can use AI to refer to an existing classical music database and compose new classical music. The generation unit can also generate music in a similar manner if a participant specifies other genres, such as jazz or rock. The generation unit can also generate music according to the tempo specified by the participant. For example, if a participant specifies BPM (beats per minute), the generation unit generates music according to that tempo. In this way, music is generated according to the participant's specifications.

[0033] The video generation unit can generate video based on the rhythm or melody of the music. For example, the video generation unit generates video in time with the rhythm of the music. The video generation unit can use AI to generate video based on the rhythm of the music. For example, at parts where the music gets exciting, the video changes dynamically, providing visual enjoyment. The video generation unit can also generate video based on the melody of the music. For example, the video generation unit generates video in time with the melody of the music, providing visual enjoyment. In this way, video is generated that matches the rhythm or melody of the music.

[0034] The performance unit can play the generated music and video in real time. The performance unit distributes the generated music and video in real time, for example, using streaming technology. The performance unit achieves real-time performance using technology to minimize delay. For example, the performance unit applies an algorithm to optimize synchronization between music and video to perform the performance in real time. In this way, the generated music and video are played in real time.

[0035] The evaluation unit can use the app to rate other participants' music. For example, when a participant uses the app to rate other participants' music, the evaluation unit performs the evaluation based on criteria such as enjoyment and level of emotion. The evaluation unit can use AI to aggregate the participants' evaluations and generate rankings. For example, when a participant uses the app to rate other participants' music, the evaluation unit performs the evaluation based on criteria such as enjoyment and level of emotion. In this way, other participants' music is rated using the app.

[0036] The evaluation unit can provide rewards to top rankers. For example, the evaluation unit allows participants to use the app to rate other participants' music and generates rankings based on the results. The evaluation unit can provide rewards to top rankers. For example, the evaluation unit can provide rewards to top rankers, such as collaborations with major vocalists or band performances. In this way, rewards are provided to top rankers.

[0037] Furthermore, the live event system includes a reception unit that analyzes participants' past input history and automatically suggests optimal genres and tempos. For example, the reception unit prioritizes suggesting genres that participants have previously preferred. The reception unit can use AI to analyze participants' past input history and automatically suggest optimal genres and tempos based on that history. For example, the reception unit can suggest optimal tempos based on tempos that participants have previously selected. The reception unit can also suggest genres that participants prefer at specific time periods based on participants' past input history. In this way, optimal genres and tempos are suggested based on participants' past input history.

[0038] Furthermore, the live event system includes a reception unit that performs filtering based on the participant's current mood and environment at the time of reception. For example, if the participant is outdoors, the reception unit suggests a genre that includes natural sounds. The reception unit can use AI to estimate the participant's current mood and environment and perform filtering based on that information. For example, if the participant is participating at night, the reception unit can suggest a relaxing genre. Also, if the participant is exercising, the reception unit can suggest an energetic genre. In this way, filtering is performed based on the participant's current mood and environment.

[0039] Furthermore, the live event system includes a reception unit that, at the time of reception, prioritizes suggesting relevant genres and tempos based on the participant's geographical location information. For example, if the participant is in an urban area, the reception unit suggests pop or electronic genres. The reception unit can use AI to acquire the participant's geographical location information and suggest relevant genres and tempos based on that information. For example, if the participant is in nature, the reception unit can suggest acoustic or ambient genres. Furthermore, if the participant is attending a music festival in a specific region, the reception unit can suggest traditional music from that region. In this way, relevant genres and tempos are suggested based on the participant's geographical location information.

[0040] Furthermore, the live event system includes a reception unit that analyzes participants' social media activity at the time of reception and suggests related genres and tempos. For example, the reception unit suggests related genres based on music that participants have shared on social media. The reception unit can use AI to analyze participants' social media activity and suggest related genres and tempos based on that information. For example, the reception unit can suggest the most appropriate genre based on the genres of artists that participants follow. The reception unit can also suggest related tempos based on music that participants have "liked" on social media. In this way, related genres and tempos are suggested based on participants' social media activity.

[0041] Furthermore, the live event system includes a generation unit that, when generating music, selects an optimal generation algorithm by referring to the participant's past music creation history. The generation unit selects the optimal generation algorithm, for example, based on the genre of music the participant has created in the past. The generation unit can use a generation AI to reference the participant's past music creation history and select the optimal generation algorithm based on that history. For example, the generation unit selects the optimal generation algorithm based on the tempo of music the participant has created in the past. The generation unit can also select a generation algorithm preferred for a specific time period from the participant's past music creation history. In this way, the optimal generation algorithm is selected based on the participant's past music creation history.

[0042] Furthermore, the live event system includes a generation unit that customizes the generation means based on the participant's current living situation when generating music. For example, if the participant is working, the generation unit generates music that helps the participant concentrate. The generation unit can use the generation AI to estimate the participant's current living situation and customize the generation means based on that information. For example, if the participant is relaxing, the generation unit generates relaxing music. Also, if the participant is exercising, the generation unit can generate energetic music. In this way, the generation means is customized based on the participant's current living situation.

[0043] Furthermore, the live event system includes a generation unit that generates optimal music by taking into account the geographical location information of participants. For example, if participants are in an urban area, the generation unit generates pop or electronic music. The generation unit can obtain the participants' geographical location information using a generation AI and generate optimal music based on that information. For example, if participants are in nature, the generation unit generates acoustic or ambient music. Also, if participants are participating in a music festival in a specific region, the generation unit can generate traditional music from that region. In this way, optimal music is generated based on the participants' geographical location information.

[0044] Furthermore, the live event system includes a generation unit that analyzes participants' social media activity and suggests generation methods when generating music. For example, the generation unit generates related music based on music shared by participants on social media. The generation unit can use generative AI to analyze participants' social media activity and suggest generation methods based on that information. For example, the generation unit generates optimal music based on the genre of artists the participants follow. The generation unit can also generate music with a related tempo based on music that the participants have "liked" on social media. This allows generation methods to be suggested based on participants' social media activity.

[0045] Furthermore, the live event system includes a video generation unit that adjusts the level of detail of the video based on the rhythm and melody of the music when generating the video. For example, if the music is slow, the video generation unit generates a simple video. The video generation unit can adjust the level of detail of the video based on the rhythm and melody of the music using a generation AI. For example, if the music is up-tempo, the video generation unit generates a detailed video. The video generation unit can also generate visually stimulating video during exciting parts of the music. In this way, the level of detail of the video is adjusted based on the rhythm and melody of the music.

[0046] Furthermore, the live event system includes a video generation unit that applies different video generation algorithms depending on the music category when generating videos. For example, in the case of classical music, the video generation unit applies an algorithm that generates calming images. The video generation unit can use generation AI to apply different video generation algorithms depending on the music category. For example, in the case of rock music, the video generation unit applies an algorithm that generates dynamic images. In addition, in the case of electronic music, the video generation unit can also apply an algorithm that generates visually stimulating images. In this way, the optimal video generation algorithm is applied depending on the music category.

[0047] Furthermore, the live event system includes a video generation unit that, when generating videos, determines the priority of videos based on the time of submission of music. For example, if music is submitted early, the video generation unit increases the priority of video generation. The video generation unit can use a generation AI to consider the time of submission of music and determine the priority of videos based on that information. For example, if music is submitted just before the deadline, the video generation unit can decrease the priority of video generation. Furthermore, if music is submitted in connection with a specific event, the video generation unit can also adjust the priority of video generation to match the event. In this way, the priority of videos is determined based on the time of submission of music.

[0048] Furthermore, the live event system includes a video generation unit that adjusts the order of videos based on the relevance of the music when generating videos. For example, the video generation unit adjusts the order of videos to match the excitement of the music. The video generation unit can use a generation AI to analyze the relevance of the music and adjust the order of videos based on that information. For example, the video generation unit adjusts the order of videos to match the tempo of the music. The video generation unit can also adjust the order of videos to match the genre of the music. In this way, the order of videos is adjusted based on the relevance of the music.

[0049] The live event system further includes a performance unit that applies an algorithm to optimize synchronization between music and video during a performance. The performance unit applies, for example, an algorithm to synchronize video with the rhythm of the music. The performance unit can apply the algorithm to optimize synchronization between music and video using AI. For example, the performance unit applies an algorithm to synchronize video with the melody of the music. The performance unit can also apply an algorithm to synchronize video with the tempo of the music. This optimizes synchronization between music and video.

[0050] Furthermore, the live event system includes a performance unit that, during a performance, selects an optimal performance method by referring to the participant's past performance history. The performance unit selects the optimal performance method, for example, based on the participant's past preferred genre of performance. The performance unit can use AI to reference the participant's past performance history and select the optimal performance method based on that history. For example, the performance unit selects the optimal performance method based on the participant's past performance tempo. The performance unit can also select a performance method preferred at a specific time period from the participant's past performance history. In this way, the optimal performance method is selected based on the participant's past performance history.

[0051] Furthermore, the live event system includes a performance unit that selects the optimal performance method during a performance, taking into account the geographic location information of the participants. For example, if the participants are in an urban area, the performance unit selects a pop or electronic performance method. The performance unit can use AI to obtain the participants' geographic location information and select the optimal performance method based on that information. For example, if the participants are in nature, the performance unit selects an acoustic or ambient performance method. Also, if the participants are participating in a music festival in a specific region, the performance unit can select a way to play traditional music of that region. In this way, the optimal performance method is selected based on the participants' geographic location information.

[0052] Furthermore, the live event system includes a performance unit that analyzes participants' social media activity and suggests performance methods during a performance. For example, the performance unit suggests related performance methods based on music shared by participants on social media. The performance unit can use AI to analyze participants' social media activity and suggest performance methods based on that information. For example, the performance unit can suggest the optimal performance method based on the genre of artists the participant follows. The performance unit can also suggest performance methods with related tempos based on music that the participant has "liked" on social media. In this way, performance methods are suggested based on participants' social media activity.

[0053] Furthermore, the live event system includes an evaluation unit that optimizes the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit optimizes the evaluation algorithm based on, for example, past evaluation data. The evaluation unit can use AI to refer to past evaluation data and optimize the evaluation algorithm based on that data. For example, the evaluation unit analyzes evaluation trends for a specific genre from the past evaluation data and adjusts the evaluation algorithm. The evaluation unit can also dynamically change the evaluation criteria based on the past evaluation data. This optimizes the evaluation algorithm based on the past evaluation data.

[0054] Furthermore, the live event system includes an evaluation unit that performs evaluations taking into account the attribute information of the participants. The evaluation unit adjusts the evaluation criteria based on, for example, the age of the participants. The evaluation unit can use AI to acquire the attribute information of the participants and perform evaluations based on that information. For example, the evaluation unit adjusts the evaluation criteria based on the gender of the participants. The evaluation unit can also adjust the evaluation criteria based on the musical preferences of the participants. In this way, evaluations are performed based on the attribute information of the participants.

[0055] Furthermore, the live event system includes an evaluation unit that takes into account the geographic distribution of music when evaluating it. For example, if music is popular in a particular region, the evaluation unit places emphasis on the evaluation of that region. The evaluation unit can use AI to analyze the geographic distribution of music and evaluate it based on that information. For example, if music is rated in multiple regions, the evaluation unit can comprehensively consider the evaluations of each region. Furthermore, if music is rated low in a particular region, the evaluation unit can also correct the evaluation of that region. In this way, evaluations are performed based on the geographic distribution of music.

[0056] Furthermore, the live event system includes an evaluation unit that, during evaluation, refers to music-related literature to improve the accuracy of the evaluation. The evaluation unit adjusts evaluation criteria based on the music-related literature, for example. The evaluation unit can use AI to refer to music-related literature and improve the accuracy of the evaluation based on that information. For example, the evaluation unit analyzes evaluation trends for specific genres from music-related literature and adjusts the evaluation criteria. The evaluation unit can also optimize the evaluation algorithm based on music-related literature. This improves the accuracy of the evaluation based on music-related literature.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The live event system can further include a voice recognition unit that accepts voice commands from participants and adjusts the generation of music and images based on those commands. For example, if a participant says "faster," the tempo of the music can be increased. Or, if a participant says "brighter," the color tone of the image can be brightened. In this way, the generation of music and images can be adjusted in real time based on the participants' voice commands.

[0059] The live event system may further include a feedback collection unit that collects feedback from participants' social networks and adjusts the music and video generation based on the feedback. For example, if a participant's friend comments, "I like this song," the music genre and tempo may be adjusted to reflect that feedback. The video generation unit may also adjust the style and content of the video based on the feedback from the social networks. This provides a more personalized experience based on the feedback from the participants' social networks.

[0060] The live event system can further include a rating history analysis unit that analyzes participants' past rating histories and optimizes the generation of music and videos based on those histories. For example, the system can prioritize genres and tempos that participants have previously rated highly. The video generation unit can also adjust the style and content of videos based on participants' past rating histories. This provides a more personalized music and video experience based on participants' past rating histories.

[0061] The live event system may further include a location information acquisition unit that acquires real-time location information of participants and adjusts the generation of music and video based on that information. For example, if a participant is at the beach, music incorporating the sound of waves may be generated. The video generation unit may also adjust the background or theme of the video based on the participant's location information. This provides a more personalized music and video experience based on the participant's real-time location information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit accepts input of music genre and tempo from participants. Participants can specify genres such as classical, jazz, or rock, and tempos such as BPM (beats per minute). Step 2: The generation unit uses the generation AI to generate music based on the information received by the reception unit. The generation AI generates music according to, for example, the genre and tempo specified by the participant. For example, when generating classical music, the generation unit references an existing classical music database and the AI ​​composes new classical music. The generation unit can also generate music for other genres such as jazz and rock in a similar manner. Step 3: The video generation unit generates video based on the music generated by the generation unit. The video generation unit generates video in accordance with the rhythm and melody of the music, for example. The video generation unit can use generation AI to generate video based on the rhythm and melody of the music. For example, when the music gets exciting, the video changes dynamically, providing visual enjoyment. Step 4: The performance unit performs the music and video generated by the generation unit and the video generation unit in real time. The performance unit distributes the generated music and video in real time, for example, using streaming technology. The performance unit achieves real-time performance using technology to minimize delay. Step 5: The evaluation unit evaluates the music performed by the performance unit. The evaluation unit evaluates the music based on evaluation criteria such as enjoyment and emotional impact. The evaluation unit can use AI to tally the participants' evaluations and generate rankings. For example, when participants use the app to evaluate other participants' music, the evaluation unit evaluates it based on criteria such as enjoyment and emotional impact.

[0064] (Example 2) A live event system according to an embodiment of the present invention uses generative AI to create original music in real time and perform it as a DJ. This system simultaneously generates not only music but also video, stimulating participants' imaginations. Music is ranked, and participants vote via an app based on judging criteria such as enjoyment and emotional impact. The creator owns the copyright, and top-ranked performers can earn revenue through perks (such as collaborations with major vocalists or band performances). Organizers can earn revenue through participation fees, sponsorships, secondary sales, and advertising monetization. For example, participants attend an event and use generative AI to create original music in real time. The generative AI generates music based on participants' input and performs it as a DJ. For example, if a participant specifies a specific genre and tempo, the generative AI generates music accordingly. Next, the generative AI generates video in conjunction with the music. The video changes in sync with the rhythm and melody of the music, stimulating participants' imaginations. For example, the video dynamically changes during exciting parts of the music, providing visual enjoyment. Participants use an app to rate other participants' music. Evaluations are based on criteria such as enjoyment and emotional impact, and a ranking is generated. Top performers are offered perks, such as collaborations with major vocalists or band performances. The copyright belongs to the participant who created the music, and top performers have the opportunity to monetize it. Organizers can earn revenue through participation fees, sponsorships, secondary sales, and advertising monetization. For example, they can resell event footage and receive advertising revenue from sponsors. In this way, live events using generative AI stimulate participants' imaginations and offer new ways to enjoy music and video. Furthermore, participants' evaluations of their work are expected to motivate them and lead to the creation of higher-quality works. This allows the live event system to generate music and video based on participant input, allowing for real-time performance and evaluation.

[0065] A live event system according to an embodiment includes a reception unit, a generation unit, a video generation unit, a performance unit, and an evaluation unit. The reception unit receives input from participants regarding the genre and tempo of music. Participants can specify genres such as classical, jazz, and rock, and tempos such as beats per minute (BPM). The generation unit uses a generation AI to generate music based on the information received by the reception unit. The generation AI generates music according to the genre and tempo specified by the participant. For example, when generating classical music, the generation unit references an existing classical music database and the AI ​​composes new classical music. The generation unit can also generate music for other genres such as jazz and rock. The video generation unit generates video based on the music generated by the generation unit. For example, the video generation unit generates video in accordance with the rhythm and melody of the music. The video generation unit can use the generation AI to generate video based on the rhythm and melody of the music. For example, during exciting parts of the music, the video dynamically changes, providing visual enjoyment. The performance unit performs the music and video generated by the generation unit and the video generation unit in real time. The performance unit distributes the generated music and video in real time, for example, using streaming technology. The performance unit achieves real-time performance using technology to minimize delay. The evaluation unit evaluates the music performed by the performance unit. The evaluation unit evaluates the music based on evaluation criteria such as enjoyment and emotional impact. The evaluation unit can use AI to tally the participants' evaluations and generate rankings. For example, when participants use an app to evaluate other participants' music, the evaluation unit evaluates based on criteria such as enjoyment and emotional impact. This enables the live event system according to the embodiment to generate music and video based on participants' input, and perform and evaluate them in real time.

[0066] The evaluation unit can evaluate music based on evaluation criteria such as enjoyment or emotional impact. For example, when a participant uses the app to evaluate other participants' music, the evaluation unit performs the evaluation based on criteria such as enjoyment or emotional impact. For example, the evaluation unit can evaluate the participants' enjoyment using a questionnaire survey. The evaluation unit can also evaluate the participants' emotional impact using emotion analysis technology. For example, the evaluation unit can analyze changes in the participants' heart rates to evaluate the emotional impact. In this way, the music is evaluated based on evaluation criteria such as enjoyment and emotional impact.

[0067] The evaluation unit can generate rankings based on the evaluation results. For example, the evaluation unit generates rankings based on results of participants using an app to evaluate other participants' music. The evaluation unit can use AI to tally evaluation scores and determine rankings. For example, the evaluation unit generates rankings based on the evaluation scores of participants and determines top rankers. In this way, rankings are generated based on the evaluation results.

[0068] The generation unit can generate music according to the genre and tempo specified by the participant. For example, if a participant specifies classical music, the generation unit generates classical music. The generation unit can use AI to refer to an existing classical music database and compose new classical music. The generation unit can also generate music in a similar manner if a participant specifies other genres, such as jazz or rock. The generation unit can also generate music according to the tempo specified by the participant. For example, if a participant specifies BPM (beats per minute), the generation unit generates music according to that tempo. In this way, music is generated according to the participant's specifications.

[0069] The video generation unit can generate video based on the rhythm or melody of the music. For example, the video generation unit generates video in time with the rhythm of the music. The video generation unit can use AI to generate video based on the rhythm of the music. For example, at parts where the music gets exciting, the video changes dynamically, providing visual enjoyment. The video generation unit can also generate video based on the melody of the music. For example, the video generation unit generates video in time with the melody of the music, providing visual enjoyment. In this way, video is generated that matches the rhythm or melody of the music.

[0070] The performance unit can play the generated music and video in real time. The performance unit distributes the generated music and video in real time, for example, using streaming technology. The performance unit achieves real-time performance using technology to minimize delay. For example, the performance unit applies an algorithm to optimize synchronization between music and video to perform the performance in real time. In this way, the generated music and video are played in real time.

[0071] The evaluation unit can use the app to rate other participants' music. For example, when a participant uses the app to rate other participants' music, the evaluation unit performs the evaluation based on criteria such as enjoyment and level of emotion. The evaluation unit can use AI to aggregate the participants' evaluations and generate rankings. For example, when a participant uses the app to rate other participants' music, the evaluation unit performs the evaluation based on criteria such as enjoyment and level of emotion. In this way, other participants' music is rated using the app.

[0072] The evaluation unit can provide rewards to top rankers. For example, the evaluation unit allows participants to use the app to rate other participants' music and generates rankings based on the results. The evaluation unit can provide rewards to top rankers. For example, the evaluation unit can provide rewards to top rankers, such as collaborations with major vocalists or band performances. In this way, rewards are provided to top rankers.

[0073] The live event system further includes a reception unit that estimates the emotions of participants and suggests genres and tempos based on the estimated emotions. For example, if a participant is relaxed, the reception unit suggests a music genre with a slow tempo. The reception unit can use AI to estimate the emotions of participants and suggest genres and tempos based on the emotions. For example, if a participant is excited, the reception unit suggests an up-tempo music genre. Also, if a participant is sad, the reception unit can suggest a soothing music genre. In this way, genres and tempos are suggested based on the emotions of participants.

[0074] Furthermore, the live event system includes a reception unit that analyzes participants' past input history and automatically suggests optimal genres and tempos. For example, the reception unit prioritizes suggesting genres that participants have previously preferred. The reception unit can use AI to analyze participants' past input history and automatically suggest optimal genres and tempos based on that history. For example, the reception unit can suggest optimal tempos based on tempos that participants have previously selected. The reception unit can also suggest genres that participants prefer at specific time periods based on participants' past input history. In this way, optimal genres and tempos are suggested based on participants' past input history.

[0075] Furthermore, the live event system includes a reception unit that performs filtering based on the participant's current mood and environment at the time of reception. For example, if the participant is outdoors, the reception unit suggests a genre that includes natural sounds. The reception unit can use AI to estimate the participant's current mood and environment and perform filtering based on that information. For example, if the participant is participating at night, the reception unit can suggest a relaxing genre. Also, if the participant is exercising, the reception unit can suggest an energetic genre. In this way, filtering is performed based on the participant's current mood and environment.

[0076] The live event system further includes a reception unit that estimates the emotions of participants and prioritizes inputs based on the estimated emotions. For example, if a participant is in a hurry, the reception unit prioritizes an easy input method. The reception unit can use AI to estimate the emotions of participants and prioritize inputs based on the emotions. For example, if a participant is relaxed, the reception unit can provide detailed input options. Alternatively, if a participant is excited, the reception unit can provide a visually stimulating input interface. In this way, the priority of inputs is determined based on the emotions of participants.

[0077] Furthermore, the live event system includes a reception unit that, at the time of reception, prioritizes suggesting relevant genres and tempos based on the participant's geographical location information. For example, if the participant is in an urban area, the reception unit suggests pop or electronic genres. The reception unit can use AI to acquire the participant's geographical location information and suggest relevant genres and tempos based on that information. For example, if the participant is in nature, the reception unit can suggest acoustic or ambient genres. Furthermore, if the participant is attending a music festival in a specific region, the reception unit can suggest traditional music from that region. In this way, relevant genres and tempos are suggested based on the participant's geographical location information.

[0078] Furthermore, the live event system includes a reception unit that analyzes participants' social media activity at the time of reception and suggests related genres and tempos. For example, the reception unit suggests related genres based on music that participants have shared on social media. The reception unit can use AI to analyze participants' social media activity and suggest related genres and tempos based on that information. For example, the reception unit can suggest the most appropriate genre based on the genres of artists that participants follow. The reception unit can also suggest related tempos based on music that participants have "liked" on social media. In this way, related genres and tempos are suggested based on participants' social media activity.

[0079] The live event system further includes a generation unit that estimates the emotions of participants and adjusts the music generation method based on the estimated emotions. For example, if a participant is relaxed, the generation unit generates music with a slow tempo. The generation unit can use a generation AI to estimate the emotions of participants and adjust the music generation method based on the emotions. For example, if a participant is excited, the generation unit generates up-tempo music. Also, if a participant is sad, the generation unit can generate soothing music. In this way, the music generation method is adjusted based on the emotions of participants.

[0080] Furthermore, the live event system includes a generation unit that, when generating music, selects an optimal generation algorithm by referring to the participant's past music creation history. The generation unit selects the optimal generation algorithm, for example, based on the genre of music the participant has created in the past. The generation unit can use a generation AI to reference the participant's past music creation history and select the optimal generation algorithm based on that history. For example, the generation unit selects the optimal generation algorithm based on the tempo of music the participant has created in the past. The generation unit can also select a generation algorithm preferred for a specific time period from the participant's past music creation history. In this way, the optimal generation algorithm is selected based on the participant's past music creation history.

[0081] Furthermore, the live event system includes a generation unit that customizes the generation means based on the participant's current living situation when generating music. For example, if the participant is working, the generation unit generates music that helps the participant concentrate. The generation unit can use the generation AI to estimate the participant's current living situation and customize the generation means based on that information. For example, if the participant is relaxing, the generation unit generates relaxing music. Also, if the participant is exercising, the generation unit can generate energetic music. In this way, the generation means is customized based on the participant's current living situation.

[0082] The live event system further includes a generation unit that estimates the emotions of participants and prioritizes the music to be generated based on the estimated emotions. For example, if a participant is in a hurry, the generation unit prioritizes music that can be generated in a short amount of time. The generation unit can use a generative AI to estimate the emotions of participants and prioritize the music to be generated based on the emotions. For example, if a participant is relaxed, the generation unit prioritizes music that has undergone a detailed generation process. Also, if a participant is excited, the generation unit can prioritize music that has visually stimulating effects. In this way, the priority of the music to be generated is determined based on the emotions of participants.

[0083] Furthermore, the live event system includes a generation unit that generates optimal music by taking into account the geographical location information of participants. For example, if participants are in an urban area, the generation unit generates pop or electronic music. The generation unit can obtain the participants' geographical location information using a generation AI and generate optimal music based on that information. For example, if participants are in nature, the generation unit generates acoustic or ambient music. Also, if participants are participating in a music festival in a specific region, the generation unit can generate traditional music from that region. In this way, optimal music is generated based on the participants' geographical location information.

[0084] Furthermore, the live event system includes a generation unit that analyzes participants' social media activity and suggests generation methods when generating music. For example, the generation unit generates related music based on music shared by participants on social media. The generation unit can use generative AI to analyze participants' social media activity and suggest generation methods based on that information. For example, the generation unit generates optimal music based on the genre of artists the participants follow. The generation unit can also generate music with a related tempo based on music that the participants have "liked" on social media. This allows generation methods to be suggested based on participants' social media activity.

[0085] Furthermore, the live event system includes a video generation unit that estimates the emotions of participants and adjusts the video generation method based on the estimated emotions. For example, if a participant is relaxed, the video generation unit generates a relaxed video. The video generation unit can estimate the emotions of participants using a generation AI and adjust the video generation method based on the emotions. For example, if a participant is excited, the video generation unit generates a dynamic video. Also, if a participant is sad, the video generation unit can generate a soothing video. In this way, the video generation method is adjusted based on the emotions of participants.

[0086] Furthermore, the live event system includes a video generation unit that adjusts the level of detail of the video based on the rhythm and melody of the music when generating the video. For example, if the music is slow, the video generation unit generates a simple video. The video generation unit can adjust the level of detail of the video based on the rhythm and melody of the music using a generation AI. For example, if the music is up-tempo, the video generation unit generates a detailed video. The video generation unit can also generate visually stimulating video during exciting parts of the music. In this way, the level of detail of the video is adjusted based on the rhythm and melody of the music.

[0087] Furthermore, the live event system includes a video generation unit that applies different video generation algorithms depending on the music category when generating videos. For example, in the case of classical music, the video generation unit applies an algorithm that generates calming images. The video generation unit can use generation AI to apply different video generation algorithms depending on the music category. For example, in the case of rock music, the video generation unit applies an algorithm that generates dynamic images. In addition, in the case of electronic music, the video generation unit can also apply an algorithm that generates visually stimulating images. In this way, the optimal video generation algorithm is applied depending on the music category.

[0088] Furthermore, the live event system includes a video generation unit that estimates the emotions of participants and adjusts the length of the video based on the estimated emotions. For example, if a participant is relaxed, the video generation unit generates longer videos. The video generation unit can estimate the emotions of participants using a generation AI and adjust the length of the video based on the emotions. For example, if a participant is in a hurry, the video generation unit generates shorter videos. Also, if a participant is excited, the video generation unit can generate visually stimulating videos. In this way, the length of the video is adjusted based on the emotions of participants.

[0089] Furthermore, the live event system includes a video generation unit that, when generating videos, determines the priority of videos based on the time of submission of music. For example, if music is submitted early, the video generation unit increases the priority of video generation. The video generation unit can use a generation AI to consider the time of submission of music and determine the priority of videos based on that information. For example, if music is submitted just before the deadline, the video generation unit can decrease the priority of video generation. Furthermore, if music is submitted in connection with a specific event, the video generation unit can also adjust the priority of video generation to match the event. In this way, the priority of videos is determined based on the time of submission of music.

[0090] Furthermore, the live event system includes a video generation unit that adjusts the order of videos based on the relevance of the music when generating videos. For example, the video generation unit adjusts the order of videos to match the excitement of the music. The video generation unit can use a generation AI to analyze the relevance of the music and adjust the order of videos based on that information. For example, the video generation unit adjusts the order of videos to match the tempo of the music. The video generation unit can also adjust the order of videos to match the genre of the music. In this way, the order of videos is adjusted based on the relevance of the music.

[0091] The live event system further includes a performance unit that estimates the emotions of participants and adjusts the performance style based on the estimated emotions. For example, if a participant is relaxed, the performance unit adopts a relaxed performance style. The performance unit can use AI to estimate the emotions of participants and adjust the performance style based on the emotions. For example, if a participant is excited, the performance unit adopts a dynamic performance style. Also, if a participant is sad, the performance unit can adopt a soothing performance style. In this way, the performance style is adjusted based on the emotions of participants.

[0092] The live event system further includes a performance unit that applies an algorithm to optimize synchronization between music and video during a performance. The performance unit applies, for example, an algorithm to synchronize video with the rhythm of the music. The performance unit can apply the algorithm to optimize synchronization between music and video using AI. For example, the performance unit applies an algorithm to synchronize video with the melody of the music. The performance unit can also apply an algorithm to synchronize video with the tempo of the music. This optimizes synchronization between music and video.

[0093] Furthermore, the live event system includes a performance unit that, during a performance, selects an optimal performance method by referring to the participant's past performance history. The performance unit selects the optimal performance method, for example, based on the participant's past preferred genre of performance. The performance unit can use AI to reference the participant's past performance history and select the optimal performance method based on that history. For example, the performance unit selects the optimal performance method based on the participant's past performance tempo. The performance unit can also select a performance method preferred at a specific time period from the participant's past performance history. In this way, the optimal performance method is selected based on the participant's past performance history.

[0094] The live event system further includes a performance unit that estimates the emotions of participants and prioritizes performances based on the estimated emotions. For example, if a participant is in a hurry, the performance unit prioritizes a method that can be performed in a short amount of time. The performance unit can use AI to estimate the emotions of participants and prioritize performances based on the emotions. For example, if a participant is relaxed, the performance unit prioritizes a method that has gone through a detailed performance process. Also, if a participant is excited, the performance unit can prioritize a visually stimulating performance method. In this way, performance priorities are determined based on the emotions of participants.

[0095] Furthermore, the live event system includes a performance unit that selects the optimal performance method during a performance, taking into account the geographic location information of the participants. For example, if the participants are in an urban area, the performance unit selects a pop or electronic performance method. The performance unit can use AI to obtain the participants' geographic location information and select the optimal performance method based on that information. For example, if the participants are in nature, the performance unit selects an acoustic or ambient performance method. Also, if the participants are participating in a music festival in a specific region, the performance unit can select a way to play traditional music of that region. In this way, the optimal performance method is selected based on the participants' geographic location information.

[0096] Furthermore, the live event system includes a performance unit that analyzes participants' social media activity and suggests performance methods during a performance. For example, the performance unit suggests related performance methods based on music shared by participants on social media. The performance unit can use AI to analyze participants' social media activity and suggest performance methods based on that information. For example, the performance unit can suggest the optimal performance method based on the genre of artists the participant follows. The performance unit can also suggest performance methods with related tempos based on music that the participant has "liked" on social media. In this way, performance methods are suggested based on participants' social media activity.

[0097] The live event system further includes an evaluation unit that estimates the emotions of participants and adjusts the evaluation criteria based on the estimated emotions. For example, if a participant is relaxed, the evaluation unit sets evaluation criteria that emphasize enjoyment. The evaluation unit can use AI to estimate the emotions of participants and adjust the evaluation criteria based on the emotions. For example, if a participant is excited, the evaluation unit sets evaluation criteria that emphasize emotionality. Also, if a participant is sad, the evaluation unit can set evaluation criteria that emphasize healing. In this way, the evaluation criteria are adjusted based on the emotions of participants.

[0098] Furthermore, the live event system includes an evaluation unit that optimizes the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit optimizes the evaluation algorithm based on, for example, past evaluation data. The evaluation unit can use AI to refer to past evaluation data and optimize the evaluation algorithm based on that data. For example, the evaluation unit analyzes evaluation trends for a specific genre from the past evaluation data and adjusts the evaluation algorithm. The evaluation unit can also dynamically change the evaluation criteria based on the past evaluation data. This optimizes the evaluation algorithm based on the past evaluation data.

[0099] Furthermore, the live event system includes an evaluation unit that performs evaluations taking into account the attribute information of the participants. The evaluation unit adjusts the evaluation criteria based on, for example, the age of the participants. The evaluation unit can use AI to acquire the attribute information of the participants and perform evaluations based on that information. For example, the evaluation unit adjusts the evaluation criteria based on the gender of the participants. The evaluation unit can also adjust the evaluation criteria based on the musical preferences of the participants. In this way, evaluations are performed based on the attribute information of the participants.

[0100] The live event system further includes an evaluation unit that estimates the emotions of participants and adjusts the order in which the evaluation results are displayed based on the estimated emotions. For example, if a participant is relaxed, the evaluation unit prioritizes displaying evaluation results that emphasize enjoyment. The evaluation unit can use AI to estimate the emotions of participants and adjust the order in which the evaluation results are displayed based on the emotions. For example, if a participant is excited, the evaluation unit prioritizes displaying evaluation results that emphasize emotional impact. Also, if a participant is sad, the evaluation unit can prioritize displaying evaluation results that emphasize healing impact. In this way, the order in which the evaluation results are displayed is adjusted based on the emotions of participants.

[0101] Furthermore, the live event system includes an evaluation unit that takes into account the geographic distribution of music when evaluating it. For example, if music is popular in a particular region, the evaluation unit places emphasis on the evaluation of that region. The evaluation unit can use AI to analyze the geographic distribution of music and evaluate it based on that information. For example, if music is rated in multiple regions, the evaluation unit can comprehensively consider the evaluations of each region. Furthermore, if music is rated low in a particular region, the evaluation unit can also correct the evaluation of that region. In this way, evaluations are performed based on the geographic distribution of music.

[0102] Furthermore, the live event system includes an evaluation unit that, during evaluation, refers to music-related literature to improve the accuracy of the evaluation. The evaluation unit adjusts evaluation criteria based on the music-related literature, for example. The evaluation unit can use AI to refer to music-related literature and improve the accuracy of the evaluation based on that information. For example, the evaluation unit analyzes evaluation trends for specific genres from music-related literature and adjusts the evaluation criteria. The evaluation unit can also optimize the evaluation algorithm based on music-related literature. This improves the accuracy of the evaluation based on music-related literature. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, video generation unit, performance unit, and evaluation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of the music genre and tempo from participants. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI. The video generation unit is realized, for example, by the control unit 46A of the smart device 14 and generates video based on the generated music. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and plays the generated music and video in real time. The evaluation unit is realized, for example, by the control unit 46A of the smart device 14 and evaluates the performed music. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, video generation unit, performance unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of the music genre and tempo from participants. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI. The video generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates video based on the generated music. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and plays the generated music and video in real time. The evaluation unit is realized, for example, by the control unit 46A of the smart glasses 214 and evaluates the played music. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, video generation unit, performance unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of the music genre and tempo from participants. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI. The video generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and generates video based on the generated music. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and plays the generated music and video in real time. The evaluation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and evaluates the played music. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, video generation unit, performance unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of the music genre and tempo from the participants. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI. The video generation unit is realized, for example, by the control unit 46A of the robot 414 and generates video based on the generated music. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and plays the generated music and video in real time. The evaluation unit is realized, for example, by the control unit 46A of the robot 414 and evaluates the performed music.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The live event system may further include a biometric information acquisition unit that acquires biometric information from participants and adjusts the generation of music and images based on that information. For example, the system may monitor a participant's heart rate and electrodermal response, and generate slower music if the participant is relaxed and faster music if the participant is excited. The image generation unit may also adjust the color tone and movement of the image based on the participant's biometric information. This allows for a more personalized music and image experience based on the participant's biometric information.

[0105] The live event system can further include a voice recognition unit that accepts voice commands from participants and adjusts the generation of music and images based on those commands. For example, if a participant says "faster," the tempo of the music can be increased. Or, if a participant says "brighter," the color tone of the image can be brightened. In this way, the generation of music and images can be adjusted in real time based on the participants' voice commands.

[0106] The live event system may further include a feedback collection unit that collects feedback from participants' social networks and adjusts the music and video generation based on the feedback. For example, if a participant's friend comments, "I like this song," the music genre and tempo may be adjusted to reflect that feedback. The video generation unit may also adjust the style and content of the video based on the feedback from the social networks. This provides a more personalized experience based on the feedback from the participants' social networks.

[0107] The live event system can further include a rating history analysis unit that analyzes participants' past rating histories and optimizes the generation of music and videos based on those histories. For example, the system can prioritize genres and tempos that participants have previously rated highly. The video generation unit can also adjust the style and content of videos based on participants' past rating histories. This provides a more personalized music and video experience based on participants' past rating histories.

[0108] The live event system may further include a location information acquisition unit that acquires real-time location information of participants and adjusts the generation of music and video based on that information. For example, if a participant is at the beach, music incorporating the sound of waves may be generated. The video generation unit may also adjust the background or theme of the video based on the participant's location information. This provides a more personalized music and video experience based on the participant's real-time location information.

[0109] The live event system may further include an emotion estimation unit that estimates the emotions of participants and suggests music genres and tempos based on the estimated emotions. For example, if a participant is relaxed, a laid-back genre such as classical or ambient music may be suggested. On the other hand, if a participant is excited, an up-tempo genre such as rock or electronic music may be suggested. In this way, the optimal music genre and tempo are suggested based on the participant's emotions.

[0110] The live event system may further include a video style adjustment unit that estimates the emotions of participants and adjusts the style and content of the video based on the estimated emotions. For example, if a participant is relaxed, a video with calm scenery and soft colors may be generated. On the other hand, if a participant is excited, a video with dynamic movements and vivid colors may be generated. In this way, the optimal video style and content are provided based on the emotions of the participants.

[0111] The live event system may further include an evaluation criterion adjustment unit that estimates the emotions of participants and adjusts the evaluation criteria based on the estimated emotions. For example, if participants are relaxed, evaluation criteria that emphasize enjoyment can be set. Alternatively, if participants are excited, evaluation criteria that emphasize emotional impact can be set. In this way, optimal evaluation criteria can be set based on the emotions of participants.

[0112] The live event system may further include a performance style adjustment unit that estimates the emotions of the participants and adjusts the performance style based on the estimated emotions. For example, if the participants are relaxed, a relaxed performance style may be adopted. If the participants are excited, a dynamic performance style may be adopted. In this way, the optimal performance style is provided based on the emotions of the participants.

[0113] The live event system may further include an evaluation result display unit that estimates the emotions of participants and adjusts the order in which the evaluation results are displayed based on the estimated emotions. For example, if a participant is relaxed, evaluation results that emphasize enjoyment may be displayed preferentially. Alternatively, if a participant is excited, evaluation results that emphasize emotional impact may be displayed preferentially. This provides an optimal display order for the evaluation results based on the emotions of the participants.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit accepts input of music genre and tempo from participants. Participants can specify genres such as classical, jazz, or rock, and tempos such as BPM (beats per minute). Step 2: The generation unit uses the generation AI to generate music based on the information received by the reception unit. The generation AI generates music according to, for example, the genre and tempo specified by the participant. For example, when generating classical music, the generation unit references an existing classical music database and the AI ​​composes new classical music. The generation unit can also generate music for other genres such as jazz and rock in a similar manner. Step 3: The video generation unit generates video based on the music generated by the generation unit. The video generation unit generates video in accordance with the rhythm and melody of the music, for example. The video generation unit can use generation AI to generate video based on the rhythm and melody of the music. For example, when the music gets exciting, the video changes dynamically, providing visual enjoyment. Step 4: The performance unit performs the music and video generated by the generation unit and the video generation unit in real time. The performance unit distributes the generated music and video in real time, for example, using streaming technology. The performance unit achieves real-time performance using technology to minimize delay. Step 5: The evaluation unit evaluates the music performed by the performance unit. The evaluation unit evaluates the music based on evaluation criteria such as enjoyment and emotional impact. The evaluation unit can use AI to tally the participants' evaluations and generate rankings. For example, when participants use the app to evaluate other participants' music, the evaluation unit evaluates it based on criteria such as enjoyment and emotional impact.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of music genres and tempos from participants; a generating unit that generates music based on the information received by the receiving unit; an image generation unit that generates an image based on the music generated by the generation unit; a performance unit that performs the music and video generated by the generation unit and the video generation unit in real time; an evaluation unit that evaluates the music played by the performance unit; A system characterized by:

2. The evaluation unit Evaluate music based on enjoyment or emotion 2. The system of claim 1.

3. The evaluation unit Generate rankings based on the evaluation results 2. The system of claim 1.

4. The generation unit Generate music according to the genre and tempo specified by the participant 2. The system of claim 1.

5. The image generation unit Generate images based on musical rhythm or melody 2. The system of claim 1.

6. The performance unit includes: Playing generated music and video in real time 2. The system of claim 1.

7. The evaluation unit Use the app to rate other participants' music 2. The system of claim 1.

8. The evaluation unit Offer rewards to top rankers 2. The system of claim 1.

Images