system

Abstract

The system according to this embodiment aims to provide a customized experience based on user responses. [Solution] The system according to this embodiment comprises an analysis unit, a provision unit, a recognition unit, and a generation unit. The analysis unit analyzes the user's reactions. The provision unit provides a customized experience based on the reactions analyzed by the analysis unit. The recognition unit has real-time speech recognition and translation functions. The generation unit generates music and visual effects generated by AI.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a customized experience based on the user's reaction has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a customized experience based on the user's reaction.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a recognition unit, and a generation unit. The analysis unit analyzes the user's reactions. The provision unit provides a customized experience based on the reactions analyzed by the analysis unit. The recognition unit has real-time speech recognition and translation functions. The generation unit generates music and visual effects generated by AI. [Effects of the Invention]

[0007] The system according to this embodiment can provide a customized experience based on user responses. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music collaboration agent system according to an embodiment of the present invention is a platform that combines a music streaming service (e.g., LINE® MUSIC) with VR / AR technology to enable live experiences at home. This music collaboration agent system allows artists to provide real-time performances, and users can experience concerts through VR / AR devices and interact with other viewers. For example, the music collaboration agent system can utilize an AI agent to improve viewer participation and enhance interactive engagement with artists. In the future, it can be expanded to global live events. Specific functions of the AI ​​agent include real-time speech recognition and translation, the ability to analyze user reactions and provide customized experiences, and AI-generated music and visual effects. This allows users to meet the need for live experiences at home and provides innovative experiences to users interested in VR / AR technology and those who desire new forms of interaction with artists. For example, the music collaboration agent system is at the forefront of VR / AR technology, aiming to innovate the user experience by realizing AI-driven real-time interaction. The target audience is young people aged 20-35 who are music lovers, interested in technology, and seeking new experiences. To address the difficulties in attending live events (geographical and economic constraints), VR / AR enables live experiences at home, transcending geographical limitations and providing accessibility. Furthermore, real-time interaction can enhance participant engagement. AI is used to analyze user reactions and customize individual viewing experiences. It is also used for real-time generation of music and visuals. In terms of market size, the convergence of the VR / AR market and the online music distribution market can open up a new market. Based on growth forecasts for the VR / AR market and the increase in the number of users of music streaming services, the TAM is estimated at 1 trillion yen and the SAM at 50 billion yen. With the evolution of VR / AR technology and the changes in the format of live events due to the COVID-19 pandemic providing a tailwind, now is the time to enter this market.The goal is to create a society where more people can participate in cultural events through the fusion of music and technology. This will enable the music-integrated agent system to analyze user reactions and provide customized experiences, thereby improving the user experience.

[0029] The music-linked agent system according to this embodiment comprises an analysis unit, a provision unit, a recognition unit, and a generation unit. The analysis unit analyzes the user's reactions. User reactions include, but are not limited to, facial expressions, voice, and behavior logs. The analysis unit analyzes the user's facial expressions using, for example, facial expression recognition technology. The analysis unit can also analyze the user's voice using voice analysis technology. Furthermore, the analysis unit can identify the user's behavior patterns by analyzing behavior logs. For example, the analysis unit uses a camera to analyze the user's facial expressions in real time and estimate their emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates their emotions. Behavior log analysis collects user behavior data and identifies behavior patterns. The provision unit provides a customized experience based on the reactions analyzed by the analysis unit. A customized experience includes, but is not limited to, individual content and interface changes. The provision unit customizes content according to the user's preferences. The provision unit can also change the interface according to the user's reactions. Furthermore, the delivery unit can adjust the content of the experience based on user feedback. For example, the delivery unit can refer to the user's viewing history and provide optimal content. Interface changes adjust the layout and design in response to user feedback. Experience content adjustments modify the difficulty level and content of the content based on user feedback. The recognition unit has real-time speech recognition and translation capabilities. Real-time speech recognition includes, but is not limited to, the algorithm used and processing speed. For example, the recognition unit recognizes user speech in real time using a speech recognition algorithm. The recognition unit can also translate user speech in real time using a translation engine. Furthermore, the recognition unit can support multiple languages. For example, the recognition unit uses a deep learning-based speech recognition algorithm to achieve high-precision speech recognition. The translation engine translates user speech in real time and supports multiple languages. The generation unit generates AI-generated music and visual effects.The music and visual effects generated by the AI ​​include, but are not limited to, the AI ​​technology used and the types of music and effects generated. For example, the generation unit can generate music in real time using a music generation AI. The generation unit can also generate visual effects in real time using a visual effect generation AI. Furthermore, the generation unit can adjust the content of the music and visual effects based on the user's reactions. For example, the generation unit can generate music that responds to the user's emotions using a music generation AI. The visual effect generation AI can adjust the content of the visual effects based on the user's reactions. As a result, the music-linked agent system according to this embodiment can improve the user experience by analyzing the user's reactions and providing a customized experience.

[0030] The analytics department analyzes user reactions from multiple perspectives. User reactions include, but are not limited to, facial expressions, voice, and behavioral logs. Specifically, the analytics department uses facial recognition technology to analyze user facial expressions in real time and estimate emotions. For example, it uses a camera to capture the user's face and a deep learning-based facial recognition algorithm to identify emotions such as smiles, surprise, and sadness. Voice analysis technology analyzes the tone, speed, and pitch of the user's voice to estimate emotions and stress levels. For example, it collects voice data, extracts acoustic features, and inputs them into an emotion classification model to identify the user's emotional state. Behavioral log analysis collects user behavioral data and identifies behavioral patterns. For example, it collects user click history, viewing history, and movement history, and clusters behavioral patterns using machine learning algorithms. This allows the analytics department to comprehensively analyze diverse user reactions and understand user states and preferences with high accuracy. Furthermore, the analytics department centrally manages and updates this data in real time, enabling analysis based on the latest user information at all times. This makes it possible to respond quickly to changing user needs and situations.

[0031] The service provider delivers a customized experience based on user feedback obtained by the analytics department. This customized experience includes, but is not limited to, individual content and interface modifications. Specifically, the service provider customizes content according to user preferences. For example, it analyzes user viewing history and behavioral patterns to suggest optimal music and visual effects. The service provider can also dynamically change the interface in response to user feedback. For instance, it adjusts the interface layout and design for content the user shows interest in, providing a more intuitive and user-friendly environment. Furthermore, the service provider can adjust the experience based on user feedback. For example, if a user wants to relax, the service provider provides relaxing music and visual effects. Conversely, if a user desires an energetic experience, it provides upbeat music and dynamic visual effects. This allows the service provider to deliver the optimal experience tailored to user needs and circumstances, improving the user experience. Additionally, the service provider collects user feedback to continuously improve the accuracy and effectiveness of the experience it delivers. This ensures that the service provider always provides the best possible experience for users.

[0032] The recognition unit has real-time speech recognition and translation capabilities. Real-time speech recognition includes, but is not limited to, the algorithm used and processing speed. Specifically, the recognition unit recognizes user speech in real time using a speech recognition algorithm. For example, it uses a deep learning-based speech recognition model to achieve high-precision speech recognition. Speech data is collected through a microphone, and acoustic features are extracted. These features are input to the speech recognition model, and the user's speech is output as text data. The recognition unit can also translate user speech in real time using a translation engine. For example, when translating a user's English speech into Japanese, the text data obtained from speech recognition is input to the translation engine, and the translation result is output in real time. The recognition unit can also support multiple languages. For example, it provides speech recognition and translation capabilities for multiple languages ​​such as English, Japanese, Chinese, and Spanish. This allows the recognition unit to recognize user speech in real time and translate it as needed, thereby supporting communication across language barriers. Furthermore, the recognition unit can continuously update training data to improve the accuracy of speech recognition and translation, thereby improving the performance of the model. This allows the recognition unit to consistently provide highly accurate speech recognition and translation, improving the user's communication experience.

[0033] The generation unit generates music and visual effects using AI. The music and visual effects generated by the AI ​​include, but are not limited to, the AI ​​technology used and the types of music and effects generated. Specifically, the generation unit generates music in real time using music generation AI. For example, it uses a music generation model employing deep learning to generate music that responds to the user's emotions and preferences. The music generation AI receives user response data as input and generates melodies and rhythms that match the emotions. The generated music is provided to the user in real time, enriching the user's experience. The generation unit can also generate visual effects in real time using visual effect generation AI. For example, it adjusts color and movement patterns based on user responses to generate visually appealing effects. The visual effect generation AI analyzes user emotion and behavior data to generate optimal effects. This allows the generation unit to integrate music and visual effects, providing a consistent experience for the user. Furthermore, the generation unit can adjust the content of the music and visual effects based on user responses. For example, if the user wants to relax, the generation unit generates relaxing music and calming visual effects. Conversely, if the user is seeking an energetic experience, the system will generate upbeat music and dynamic visual effects. This allows the generation unit to provide the optimal experience tailored to the user's needs and situation, thereby improving the user experience.

[0034] The data collection unit collects data for analyzing user responses. For example, the data collection unit collects user biometric data using sensors. For instance, it collects the user's heart rate in real time using a heart rate sensor. The data collection unit can also acquire log data from the user's device to collect behavioral logs. For example, it collects behavioral logs from the user's smartphone to identify the user's behavioral patterns. Furthermore, the data collection unit can collect user opinions and feedback using surveys. For example, it conducts online surveys to collect user feedback. This allows the data collection unit to collect data for analyzing user responses, thereby improving the accuracy of the analysis. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input biometric data acquired by sensors into a generating AI and have the generating AI perform data analysis.

[0035] The output unit provides generated music and visual effects. The output unit provides music using, for example, speakers. For example, the output unit provides generated music with clear sound quality using high-quality speakers. The output unit can also provide visual effects using a display. For example, the output unit displays generated visual effects clearly using a high-resolution display. Furthermore, the output unit can provide generated music and visual effects in real time using streaming technology. For example, the output unit streams generated music and visual effects to the user via the internet. This allows the output unit to provide a richer experience to the user by providing generated music and visual effects. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can provide music and visual effects generated by a generation AI.

[0036] The recognition unit has real-time speech recognition and translation capabilities. For example, the recognition unit recognizes user speech in real time using a speech recognition algorithm. For instance, the recognition unit uses a deep learning-based speech recognition algorithm to achieve high-precision speech recognition. Furthermore, the recognition unit can also translate user speech in real time using a translation engine. For example, the recognition unit translates user speech in real time and supports multiple languages. This real-time speech recognition and translation capabilities improve user interaction. Some or all of the above-described processes in the recognition unit may be performed using AI, or they may not. For example, the recognition unit can perform speech recognition and translation using generative AI.

[0037] The generation unit generates music and visual effects generated by AI. For example, the generation unit generates music in real time using a music generation AI. For example, the generation unit generates music that responds to the user's emotions using a music generation AI. The generation unit can also generate visual effects in real time using a visual effect generation AI. For example, the generation unit adjusts the content of the visual effects based on the user's reaction using a visual effect generation AI. In this way, the generation unit provides the user with a new experience by generating music and visual effects generated by AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can generate music and visual effects using a generation AI.

[0038] The analysis unit can optimize its analysis algorithm by referring to the user's past response data during analysis. For example, the analysis unit's AI can refer to the user's past responses and reflect them in the current response analysis. For example, the analysis unit can also have the AI ​​select the optimal analysis algorithm based on the user's past response data. Furthermore, the analysis unit can have the AI ​​learn from the user's past response data and continuously improve the analysis algorithm. This optimizes the analysis algorithm and improves the accuracy of the analysis by referring to the user's past response data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past response data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0039] The analysis unit can identify reaction patterns by considering the user's viewing history during analysis. For example, the analysis unit can use AI to refer to content the user has previously viewed to identify reaction patterns. For example, the analysis unit can also use AI to predict reaction patterns based on the user's viewing history. Furthermore, the analysis unit can use AI to analyze the user's viewing history to identify reaction patterns. This allows for more accurate analysis by considering the user's viewing history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user viewing history data into a generating AI and have the generating AI perform the identification of reaction patterns.

[0040] The analysis unit can analyze regional trends in responses by considering the user's geographical location information during analysis. For example, the analysis unit can use AI to refer to the user's geographical location information and analyze regional response trends. For example, the analysis unit can use AI to identify regional response patterns based on the user's geographical location information. The analysis unit can also use AI to analyze the user's geographical location information and predict regional response trends. In this way, by considering the user's geographical location information, regional response trends can be analyzed and an optimal experience tailored to each region can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform an analysis of regional response trends.

[0041] The analysis unit can analyze the relevance of responses by referring to the user's social media activity during the analysis. For example, the analysis unit can use AI to refer to the user's social media activity and analyze the relevance of responses. For example, the analysis unit can use AI to identify the relevance of responses based on the user's social media activity. The analysis unit can also use AI to analyze the user's social media activity and predict the relevance of responses. This allows for a more accurate analysis by referring to the user's social media activity and analyzing the relevance of responses. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the analysis of the relevance of responses.

[0042] The service provider can provide the optimal experience by referring to the user's past viewing history at the time of delivery. For example, the service provider can use AI to refer to content the user has previously viewed and provide the optimal experience. For example, the service provider can use AI to suggest the optimal experience based on the user's past viewing history. The service provider can also use AI to analyze the user's viewing history and provide the optimal experience. In this way, by referring to the user's past viewing history, the service provider can provide the optimal experience and improve the user experience. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's viewing history data into a generating AI and have the generating AI perform the task of providing the optimal experience.

[0043] The service provider can customize the content of the experience based on the user's current viewing environment at the time of delivery. For example, the service provider can use AI to detect the type of device the user is using and provide an experience accordingly. For example, the service provider can use AI to detect the user's viewing environment (brightness, volume, etc.) and provide an experience accordingly. Furthermore, the service provider can use AI to analyze the user's viewing environment and provide the optimal experience. In this way, by customizing the content of the experience based on the user's current viewing environment, the service provider can provide the best possible experience for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's viewing environment data into a generating AI and have the generating AI perform the customization of the experience content.

[0044] The service provider can provide an optimal experience by considering the user's geographical location information at the time of delivery. For example, the service provider can use AI to refer to the user's geographical location information and provide an experience appropriate for that region. For example, the service provider can use AI to suggest region-specific experiences based on the user's geographical location information. The service provider can also use AI to analyze the user's geographical location information and provide an optimal experience. In this way, by considering the user's geographical location information, an optimal experience tailored to the region can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI execute the provision of an optimal experience.

[0045] The service provider can customize the content of the experience by referring to the user's social media activity at the time of delivery. For example, the service provider can use AI to refer to the user's social media activity and provide an experience based on that. For example, the service provider can use AI to suggest a customized experience based on the user's social media activity. Alternatively, the service provider can use AI to analyze the user's social media activity and provide the optimal experience. In this way, by referring to the user's social media activity, the content of the experience is customized to provide the best possible experience for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the experience content.

[0046] The recognition unit can optimize its recognition algorithm by referring to the user's past speech data during recognition. For example, the recognition unit's AI can refer to what the user has said in the past and reflect it in the current recognition. For example, the recognition unit can also have the AI ​​select the optimal recognition algorithm based on the user's past speech data. Furthermore, the recognition unit can have the AI ​​learn from the user's past speech data and continuously improve the recognition algorithm. In this way, by referring to the user's past speech data, the recognition algorithm is optimized and the accuracy of recognition is improved. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's past speech data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0047] The recognition unit can determine translation priorities based on the user's utterances during recognition. For example, if the user utters something important, the AI ​​will prioritize translating that content. For example, the recognition unit can also have the AI ​​analyze the user's utterances and determine translation priorities according to their importance. Alternatively, the recognition unit can have the AI ​​refer to the user's utterances and determine the optimal translation order. This allows for prioritizing the translation of important content by determining translation priorities based on the user's utterances. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input user utterance data into a generating AI and have the generating AI determine the translation priorities.

[0048] The recognition unit can provide the optimal translation by considering the user's geographical location information during recognition. For example, the recognition unit can use AI to refer to the user's geographical location information and provide a translation appropriate for that region. For example, the recognition unit can also use AI to suggest region-specific translations based on the user's geographical location information. Furthermore, the recognition unit can use AI to analyze the user's geographical location information and provide the optimal translation. In this way, by considering the user's geographical location information, it is possible to provide the optimal translation according to the region. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing the optimal translation.

[0049] The recognition unit can improve the accuracy of recognition by referring to the user's social media activity during recognition. For example, the recognition unit can improve the accuracy of recognition by having the AI ​​refer to the user's social media activity. For example, the recognition unit can also improve the accuracy of recognition by having the AI ​​improve the accuracy of recognition based on the user's social media activity. Furthermore, the recognition unit can improve the accuracy of recognition by having the AI ​​analyze the user's social media activity. In this way, the accuracy of recognition can be improved by referring to the user's social media activity. Some or all of the above processing in the recognition unit may be performed using AI or not using AI. For example, the recognition unit can input the user's social media activity data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0050] The generation unit can generate optimal music and visual effects by referencing the user's past viewing history during the generation process. For example, the generation unit can use AI to reference content the user has previously viewed and generate optimal music and visual effects. For example, the generation unit can also use AI to suggest optimal music and visual effects based on the user's past viewing history. Furthermore, the generation unit can use AI to analyze the user's viewing history and generate optimal music and visual effects. This improves the user experience by generating optimal music and visual effects by referencing the user's past viewing history. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's viewing history data into a generation AI and have the generation AI perform the generation of optimal music and visual effects.

[0051] The generation unit can customize the generated content based on the user's current viewing environment during generation. For example, the generation unit can use AI to detect the type of device the user is using and generate music and visual effects accordingly. For example, the generation unit can also use AI to detect the user's viewing environment (brightness, volume, etc.) and generate music and visual effects accordingly. Furthermore, the generation unit can use AI to analyze the user's viewing environment and generate optimal music and visual effects. This allows the generation unit to provide the user with the best possible experience by customizing the generated content based on the user's current viewing environment. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user viewing environment data into the generation AI and have the generation AI perform the customization of the generated content.

[0052] The generation unit can generate optimal music and visual effects while considering the user's geographical location information. For example, the generation unit can use AI to reference the user's geographical location information and generate music and visual effects suitable for that region. For example, the generation unit can use AI to suggest region-specific music and visual effects based on the user's geographical location information. The generation unit can also use AI to analyze the user's geographical location information and generate optimal music and visual effects. In this way, by considering the user's geographical location information, it is possible to generate optimal music and visual effects according to the region. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information data into the generation AI and have the generation AI execute the generation of optimal music and visual effects.

[0053] The generation unit can customize the generated content by referencing the user's social media activity during the generation process. For example, the generation unit can use AI to reference the user's social media activity and generate music and visual effects based on it. For example, the generation unit can also use AI to suggest customized music and visual effects based on the user's social media activity. Alternatively, the generation unit can use AI to analyze the user's social media activity and generate optimal music and visual effects. This allows the generation content to be customized by referencing the user's social media activity, providing the user with the best possible experience. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the customization of the generated content.

[0054] The data collection unit can select the optimal data collection method by referring to the user's past data collection history during data collection. For example, the AI ​​in the data collection unit can refer to what kind of data the user has collected in the past and reflect this in the current data collection. For example, the AI ​​in the data collection unit can select the optimal data collection method based on the user's past data collection history. Furthermore, the AI ​​in the data collection unit can learn from the user's past data collection history and continuously improve the data collection method. This allows the AI ​​to select the optimal data collection method by referring to the user's past data collection history and improve the accuracy of data collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal data collection method.

[0055] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, the data collection unit can use AI to refer to the user's geographical location information and prioritize the collection of data related to that region. For example, the data collection unit can use AI to prioritize the collection of region-specific data based on the user's geographical location information. Alternatively, the data collection unit can use AI to analyze the user's geographical location information and prioritize the collection of highly relevant data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0056] The output unit can output optimal music and visual effects by referring to the user's past viewing history at the time of output. For example, the output unit can use AI to refer to content the user has previously viewed and output optimal music and visual effects. For example, the output unit can also use AI to suggest optimal music and visual effects based on the user's past viewing history. Furthermore, the output unit can use AI to analyze the user's viewing history and output optimal music and visual effects. This improves the user experience by outputting optimal music and visual effects by referring to the user's past viewing history. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input the user's viewing history data into a generating AI and have the generating AI execute the output of optimal music and visual effects.

[0057] The output unit can output optimal music and visual effects while considering the user's geographical location information. For example, the output unit can use AI to refer to the user's geographical location information and output music and visual effects appropriate for that region. For example, the output unit can use AI to suggest region-specific music and visual effects based on the user's geographical location information. Alternatively, the output unit can use AI to analyze the user's geographical location information and output optimal music and visual effects. This allows for the output of optimal music and visual effects tailored to the region by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input the user's geographical location data into a generating AI and have the generating AI execute the output of optimal music and visual effects.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The music integration agent system can refer to a user's past listening history to identify their preferred music genres and artists, and then customize the live experience based on that information. For example, it can prioritize performances by artists the user has listened to frequently in the past. It can also adjust the live setlist to match the user's preferred music genres. Furthermore, it can suggest new artists and songs based on the user's listening history. This allows for the provision of a personalized live experience tailored to the user's preferences.

[0060] The music integration agent system can provide different live experiences for each region, taking into account the user's geographical location. For example, it can prioritize live performances by artists popular in a particular region. It can also provide special performances tailored to local culture and events. Furthermore, it can analyze user reactions in each region and optimize the live experience based on that information. This allows for the provision of customized live experiences for each region.

[0061] The music integration agent system can customize the live experience by referencing the user's social media activity. For example, it can prioritize live performances by artists the user follows on social media. It can also adjust the live setlist based on the music the user has shared on social media. Furthermore, it can analyze the user's social media reactions and optimize the live experience based on that information. This allows for the provision of a personalized live experience based on the user's social media activity.

[0062] The music-integrated agent system can customize the live experience based on the user's viewing environment. For example, it can provide optimal sound and video quality depending on the type of device the user is using. It can also adjust the live performance based on the brightness and volume of the user's viewing environment. Furthermore, it can optimize the interface design and operation methods based on the user's viewing environment. This allows for the provision of an optimal live experience tailored to the user's viewing environment.

[0063] The music-integrated agent system can optimize the live experience by referencing past user reaction data. For example, it can analyze how users have reacted in the past and adjust the live performance based on that information. It can also provide optimal content based on past user reaction data. Furthermore, it can continuously learn from user reaction data and improve the live experience. This allows for the provision of an optimal live experience based on past user reaction data.

[0064] The music integration agent system can provide different live experiences for each region, taking into account the user's geographical location. For example, it can prioritize live performances by artists popular in a particular region. It can also provide special performances tailored to local culture and events. Furthermore, it can analyze user reactions in each region and optimize the live experience based on that information. This allows for the provision of customized live experiences for each region.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis unit analyzes user responses. User responses include facial expressions, voice, and behavioral logs. For example, facial expression recognition technology is used to analyze the user's facial expressions, and voice analysis technology is used to analyze the user's voice. Furthermore, behavioral logs can be analyzed to identify user behavioral patterns. Step 2: The service department provides a customized experience based on the responses analyzed by the analytics department. This customized experience may include individual content and interface modifications. For example, content may be customized according to user preferences, and the interface may be modified based on user responses. Step 3: The recognition unit has real-time speech recognition and translation capabilities. For example, it uses a speech recognition algorithm to recognize the user's speech in real time and a translation engine to translate the user's speech in real time. Furthermore, it can support multiple languages. Step 4: The generation unit generates music and visual effects using AI. For example, it can generate music in real time using a music generation AI and generate visual effects in real time using a visual effect generation AI. Furthermore, it can adjust the content of the music and visual effects based on user reactions.

[0067] (Example of form 2) The music collaboration agent system according to an embodiment of the present invention is a platform that combines a music streaming service (e.g., LINE MUSIC) with VR / AR technology to enable live experiences at home. This music collaboration agent system allows artists to provide real-time performances, and users can experience concerts through VR / AR devices and interact with other viewers. For example, the music collaboration agent system can utilize an AI agent to improve viewer participation and enhance interactive engagement with artists. In the future, it can be expanded to global live events. Specific functions of the AI ​​agent include real-time speech recognition and translation, the ability to analyze user reactions and provide customized experiences, and AI-generated music and visual effects. This allows users to meet the need for live experiences at home and provides innovative experiences for users interested in VR / AR technology and those who desire new forms of interaction with artists. For example, the music collaboration agent system is at the forefront of VR / AR technology, aiming to innovate the user experience by realizing AI-driven real-time interaction. The target audience is young people aged 20-35 who are music lovers, interested in technology, and seeking new experiences. To address the difficulties in attending live events (geographical and economic constraints), VR / AR enables live experiences at home, transcending geographical limitations and providing accessibility. Furthermore, real-time interaction can enhance participant engagement. AI is used to analyze user reactions and customize individual viewing experiences. It is also used for real-time generation of music and visuals. In terms of market size, the convergence of the VR / AR market and the online music distribution market can open up a new market. Based on growth forecasts for the VR / AR market and the increase in the number of users of music streaming services, the TAM is estimated at 1 trillion yen and the SAM at 50 billion yen. With the evolution of VR / AR technology and the changes in the format of live events due to the COVID-19 pandemic providing a tailwind, now is the time to enter this market.The goal is to create a society where more people can participate in cultural events through the fusion of music and technology. This will enable the music-integrated agent system to analyze user reactions and provide customized experiences, thereby improving the user experience.

[0068] The music-linked agent system according to this embodiment comprises an analysis unit, a provision unit, a recognition unit, and a generation unit. The analysis unit analyzes the user's reactions. User reactions include, but are not limited to, facial expressions, voice, and behavior logs. The analysis unit analyzes the user's facial expressions using, for example, facial expression recognition technology. The analysis unit can also analyze the user's voice using voice analysis technology. Furthermore, the analysis unit can identify the user's behavior patterns by analyzing behavior logs. For example, the analysis unit uses a camera to analyze the user's facial expressions in real time and estimate their emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates their emotions. Behavior log analysis collects user behavior data and identifies behavior patterns. The provision unit provides a customized experience based on the reactions analyzed by the analysis unit. A customized experience includes, but is not limited to, individual content and interface changes. The provision unit customizes content according to the user's preferences. The provision unit can also change the interface according to the user's reactions. Furthermore, the delivery unit can adjust the content of the experience based on user feedback. For example, the delivery unit can refer to the user's viewing history and provide optimal content. Interface changes adjust the layout and design in response to user feedback. Experience content adjustments modify the difficulty level and content of the content based on user feedback. The recognition unit has real-time speech recognition and translation capabilities. Real-time speech recognition includes, but is not limited to, the algorithm used and processing speed. For example, the recognition unit recognizes user speech in real time using a speech recognition algorithm. The recognition unit can also translate user speech in real time using a translation engine. Furthermore, the recognition unit can support multiple languages. For example, the recognition unit uses a deep learning-based speech recognition algorithm to achieve high-precision speech recognition. The translation engine translates user speech in real time and supports multiple languages. The generation unit generates AI-generated music and visual effects.The music and visual effects generated by the AI ​​include, but are not limited to, the AI ​​technology used and the types of music and effects generated. For example, the generation unit can generate music in real time using a music generation AI. The generation unit can also generate visual effects in real time using a visual effect generation AI. Furthermore, the generation unit can adjust the content of the music and visual effects based on the user's reactions. For example, the generation unit can generate music that responds to the user's emotions using a music generation AI. The visual effect generation AI can adjust the content of the visual effects based on the user's reactions. As a result, the music-linked agent system according to this embodiment can improve the user experience by analyzing the user's reactions and providing a customized experience.

[0069] The analytics department analyzes user reactions from multiple perspectives. User reactions include, but are not limited to, facial expressions, voice, and behavioral logs. Specifically, the analytics department uses facial recognition technology to analyze user facial expressions in real time and estimate emotions. For example, it uses a camera to capture the user's face and a deep learning-based facial recognition algorithm to identify emotions such as smiles, surprise, and sadness. Voice analysis technology analyzes the tone, speed, and pitch of the user's voice to estimate emotions and stress levels. For example, it collects voice data, extracts acoustic features, and inputs them into an emotion classification model to identify the user's emotional state. Behavioral log analysis collects user behavioral data and identifies behavioral patterns. For example, it collects user click history, viewing history, and movement history, and clusters behavioral patterns using machine learning algorithms. This allows the analytics department to comprehensively analyze diverse user reactions and understand user states and preferences with high accuracy. Furthermore, the analytics department centrally manages and updates this data in real time, enabling analysis based on the latest user information at all times. This makes it possible to respond quickly to changing user needs and situations.

[0070] The service provider delivers a customized experience based on user feedback obtained by the analytics department. This customized experience includes, but is not limited to, individual content and interface modifications. Specifically, the service provider customizes content according to user preferences. For example, it analyzes user viewing history and behavioral patterns to suggest optimal music and visual effects. The service provider can also dynamically change the interface in response to user feedback. For instance, it adjusts the interface layout and design for content the user shows interest in, providing a more intuitive and user-friendly environment. Furthermore, the service provider can adjust the experience based on user feedback. For example, if a user wants to relax, the service provider provides relaxing music and visual effects. Conversely, if a user desires an energetic experience, it provides upbeat music and dynamic visual effects. This allows the service provider to deliver the optimal experience tailored to user needs and circumstances, improving the user experience. Additionally, the service provider collects user feedback to continuously improve the accuracy and effectiveness of the experience it delivers. This ensures that the service provider always provides the best possible experience for users.

[0071] The recognition unit has real-time speech recognition and translation capabilities. Real-time speech recognition includes, but is not limited to, the algorithm used and processing speed. Specifically, the recognition unit recognizes user speech in real time using a speech recognition algorithm. For example, it uses a deep learning-based speech recognition model to achieve high-precision speech recognition. Speech data is collected through a microphone, and acoustic features are extracted. These features are input to the speech recognition model, and the user's speech is output as text data. The recognition unit can also translate user speech in real time using a translation engine. For example, when translating a user's English speech into Japanese, the text data obtained from speech recognition is input to the translation engine, and the translation result is output in real time. The recognition unit can also support multiple languages. For example, it provides speech recognition and translation capabilities for multiple languages ​​such as English, Japanese, Chinese, and Spanish. This allows the recognition unit to recognize user speech in real time and translate it as needed, thereby supporting communication across language barriers. Furthermore, the recognition unit can continuously update training data to improve the accuracy of speech recognition and translation, thereby improving the performance of the model. This allows the recognition unit to consistently provide highly accurate speech recognition and translation, improving the user's communication experience.

[0072] The generation unit generates music and visual effects using AI. The music and visual effects generated by the AI ​​include, but are not limited to, the AI ​​technology used and the types of music and effects generated. Specifically, the generation unit generates music in real time using music generation AI. For example, it uses a music generation model employing deep learning to generate music that responds to the user's emotions and preferences. The music generation AI receives user response data as input and generates melodies and rhythms that match the emotions. The generated music is provided to the user in real time, enriching the user's experience. The generation unit can also generate visual effects in real time using visual effect generation AI. For example, it adjusts color and movement patterns based on user responses to generate visually appealing effects. The visual effect generation AI analyzes user emotion and behavior data to generate optimal effects. This allows the generation unit to integrate music and visual effects, providing a consistent experience for the user. Furthermore, the generation unit can adjust the content of the music and visual effects based on user responses. For example, if the user wants to relax, the generation unit generates relaxing music and calming visual effects. Conversely, if the user is seeking an energetic experience, the system will generate upbeat music and dynamic visual effects. This allows the generation unit to provide the optimal experience tailored to the user's needs and situation, thereby improving the user experience.

[0073] The data collection unit collects data for analyzing user responses. For example, the data collection unit collects user biometric data using sensors. For instance, it collects the user's heart rate in real time using a heart rate sensor. The data collection unit can also acquire log data from the user's device to collect behavioral logs. For example, it collects behavioral logs from the user's smartphone to identify the user's behavioral patterns. Furthermore, the data collection unit can collect user opinions and feedback using surveys. For example, it conducts online surveys to collect user feedback. This allows the data collection unit to collect data for analyzing user responses, thereby improving the accuracy of the analysis. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input biometric data acquired by sensors into a generating AI and have the generating AI perform data analysis.

[0074] The output unit provides generated music and visual effects. The output unit provides music using, for example, speakers. For example, the output unit provides generated music with clear sound quality using high-quality speakers. The output unit can also provide visual effects using a display. For example, the output unit displays generated visual effects clearly using a high-resolution display. Furthermore, the output unit can provide generated music and visual effects in real time using streaming technology. For example, the output unit streams generated music and visual effects to the user via the internet. This allows the output unit to provide a richer experience to the user by providing generated music and visual effects. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can provide music and visual effects generated by a generation AI.

[0075] The recognition unit has real-time speech recognition and translation capabilities. For example, the recognition unit recognizes user speech in real time using a speech recognition algorithm. For instance, the recognition unit uses a deep learning-based speech recognition algorithm to achieve high-precision speech recognition. Furthermore, the recognition unit can also translate user speech in real time using a translation engine. For example, the recognition unit translates user speech in real time and supports multiple languages. This real-time speech recognition and translation capabilities improve user interaction. Some or all of the above-described processes in the recognition unit may be performed using AI, or they may not. For example, the recognition unit can perform speech recognition and translation using generative AI.

[0076] The generation unit generates music and visual effects generated by AI. For example, the generation unit generates music in real time using a music generation AI. For example, the generation unit generates music that responds to the user's emotions using a music generation AI. The generation unit can also generate visual effects in real time using a visual effect generation AI. For example, the generation unit adjusts the content of the visual effects based on the user's reaction using a visual effect generation AI. In this way, the generation unit provides the user with a new experience by generating music and visual effects generated by AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can generate music and visual effects using a generation AI.

[0077] The analysis unit can estimate the user's emotions and adjust the response analysis method based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect that emotion and perform a rapid response analysis. For example, if the user is relaxed, the AI ​​can detect that emotion and perform a detailed response analysis. Also, if the user is stressed, the AI ​​can detect that emotion and simplify the response analysis. This allows for more accurate analysis by adjusting the response analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The analysis unit can optimize its analysis algorithm by referring to the user's past response data during analysis. For example, the analysis unit's AI can refer to the user's past responses and reflect them in the current response analysis. For example, the analysis unit can also have the AI ​​select the optimal analysis algorithm based on the user's past response data. Furthermore, the analysis unit can have the AI ​​learn from the user's past response data and continuously improve the analysis algorithm. This optimizes the analysis algorithm and improves the accuracy of the analysis by referring to the user's past response data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past response data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0079] The analysis unit can identify reaction patterns by considering the user's viewing history during analysis. For example, the analysis unit can use AI to refer to content the user has previously viewed to identify reaction patterns. For example, the analysis unit can also use AI to predict reaction patterns based on the user's viewing history. Furthermore, the analysis unit can use AI to analyze the user's viewing history to identify reaction patterns. This allows for more accurate analysis by considering the user's viewing history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user viewing history data into a generating AI and have the generating AI perform the identification of reaction patterns.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect that emotion and visually highlight it in the display. For example, if the user is relaxed, the AI ​​can detect that emotion and display the analysis results in detail. Also, if the user is stressed, the AI ​​can detect that emotion and display the analysis results in a simplified manner. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can analyze regional trends in responses by considering the user's geographical location information during analysis. For example, the analysis unit can use AI to refer to the user's geographical location information and analyze regional response trends. For example, the analysis unit can use AI to identify regional response patterns based on the user's geographical location information. The analysis unit can also use AI to analyze the user's geographical location information and predict regional response trends. In this way, by considering the user's geographical location information, regional response trends can be analyzed and an optimal experience tailored to each region can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform an analysis of regional response trends.

[0082] The analysis unit can analyze the relevance of responses by referring to the user's social media activity during the analysis. For example, the analysis unit can use AI to refer to the user's social media activity and analyze the relevance of responses. For example, the analysis unit can use AI to identify the relevance of responses based on the user's social media activity. The analysis unit can also use AI to analyze the user's social media activity and predict the relevance of responses. This allows for a more accurate analysis by referring to the user's social media activity and analyzing the relevance of responses. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the analysis of the relevance of responses.

[0083] The service provider can estimate the user's emotions and adjust the delivery method of a customized experience based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect that emotion and provide an energetic experience. For example, if the user is relaxed, the AI ​​can detect that emotion and provide a calm experience. Also, if the user is stressed, the AI ​​can detect that emotion and provide a relaxing experience. In this way, by adjusting the delivery method of a customized experience based on the user's emotions, the service provider can provide the optimal experience for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The service provider can provide the optimal experience by referring to the user's past viewing history at the time of delivery. For example, the service provider can use AI to refer to content the user has previously viewed and provide the optimal experience. For example, the service provider can use AI to suggest the optimal experience based on the user's past viewing history. The service provider can also use AI to analyze the user's viewing history and provide the optimal experience. In this way, by referring to the user's past viewing history, the service provider can provide the optimal experience and improve the user experience. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's viewing history data into a generating AI and have the generating AI perform the task of providing the optimal experience.

[0085] The service provider can customize the content of the experience based on the user's current viewing environment at the time of delivery. For example, the service provider can use AI to detect the type of device the user is using and provide an experience accordingly. For example, the service provider can use AI to detect the user's viewing environment (brightness, volume, etc.) and provide an experience accordingly. Furthermore, the service provider can use AI to analyze the user's viewing environment and provide the optimal experience. In this way, by customizing the content of the experience based on the user's current viewing environment, the service provider can provide the best possible experience for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's viewing environment data into a generating AI and have the generating AI perform the customization of the experience content.

[0086] The service provider can estimate the user's emotions and prioritize the experiences offered based on those emotions. For example, if the user is excited, the AI ​​can detect that emotion and prioritize providing an energetic experience. For example, if the user is relaxed, the AI ​​can detect that emotion and prioritize providing a calming experience. Similarly, if the user is stressed, the AI ​​can detect that emotion and prioritize providing a relaxing experience. By prioritizing experiences based on the user's emotions, the service provider can offer the optimal experience for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The service provider can provide an optimal experience by considering the user's geographical location information at the time of delivery. For example, the service provider can use AI to refer to the user's geographical location information and provide an experience appropriate for that region. For example, the service provider can use AI to suggest region-specific experiences based on the user's geographical location information. The service provider can also use AI to analyze the user's geographical location information and provide an optimal experience. In this way, by considering the user's geographical location information, an optimal experience tailored to the region can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI execute the provision of an optimal experience.

[0088] The service provider can customize the content of the experience by referring to the user's social media activity at the time of delivery. For example, the service provider can use AI to refer to the user's social media activity and provide an experience based on that. For example, the service provider can use AI to suggest a customized experience based on the user's social media activity. Alternatively, the service provider can use AI to analyze the user's social media activity and provide the optimal experience. In this way, by referring to the user's social media activity, the content of the experience is customized to provide the best possible experience for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the experience content.

[0089] The recognition unit can estimate the user's emotions and adjust the accuracy of speech recognition and translation based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and quickly adjust the accuracy of speech recognition and translation. For example, if the user is relaxed, the AI ​​can detect that emotion and adjust the accuracy of speech recognition and translation in detail. Also, if the user is stressed, the AI ​​can detect that emotion and simplify the accuracy of speech recognition and translation. By adjusting the accuracy of speech recognition and translation based on the user's emotions, more accurate recognition and translation become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not using AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The recognition unit can optimize its recognition algorithm by referring to the user's past speech data during recognition. For example, the recognition unit's AI can refer to what the user has said in the past and reflect it in the current recognition. For example, the recognition unit can also have the AI ​​select the optimal recognition algorithm based on the user's past speech data. Furthermore, the recognition unit can have the AI ​​learn from the user's past speech data and continuously improve the recognition algorithm. In this way, by referring to the user's past speech data, the recognition algorithm is optimized and the accuracy of recognition is improved. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's past speech data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0091] The recognition unit can determine translation priorities based on the user's utterances during recognition. For example, if the user utters something important, the AI ​​will prioritize translating that content. For example, the recognition unit can also have the AI ​​analyze the user's utterances and determine translation priorities according to their importance. Alternatively, the recognition unit can have the AI ​​refer to the user's utterances and determine the optimal translation order. This allows for prioritizing the translation of important content by determining translation priorities based on the user's utterances. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input user utterance data into a generating AI and have the generating AI determine the translation priorities.

[0092] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect that emotion and visually highlight and display the recognition results. For example, if the user is relaxed, the AI ​​can detect that emotion and display the recognition results in detail. Also, if the user is stressed, the AI ​​can detect that emotion and display the recognition results in a simplified manner. By adjusting the display method of the recognition results based on the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the recognition unit may be performed using AI or not using AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The recognition unit can provide the optimal translation by considering the user's geographical location information during recognition. For example, the recognition unit can use AI to refer to the user's geographical location information and provide a translation appropriate for that region. For example, the recognition unit can also use AI to suggest region-specific translations based on the user's geographical location information. Furthermore, the recognition unit can use AI to analyze the user's geographical location information and provide the optimal translation. In this way, by considering the user's geographical location information, it is possible to provide the optimal translation according to the region. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing the optimal translation.

[0094] The recognition unit can improve the accuracy of recognition by referring to the user's social media activity during recognition. For example, the recognition unit can improve the accuracy of recognition by having the AI ​​refer to the user's social media activity. For example, the recognition unit can also improve the accuracy of recognition by having the AI ​​improve the accuracy of recognition based on the user's social media activity. Furthermore, the recognition unit can improve the accuracy of recognition by having the AI ​​analyze the user's social media activity. In this way, the accuracy of recognition can be improved by referring to the user's social media activity. Some or all of the above processing in the recognition unit may be performed using AI or not using AI. For example, the recognition unit can input the user's social media activity data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0095] The generation unit can estimate the user's emotions and adjust the content of the music and visual effects it generates based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and generate energetic music and visual effects. For example, if the user is relaxed, the AI ​​can detect that emotion and generate calming music and visual effects. Also, if the user is stressed, the AI ​​can detect that emotion and generate relaxing music and visual effects. In this way, by adjusting the content of the music and visual effects generated based on the user's emotions, the optimal experience for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0096] The generation unit can generate optimal music and visual effects by referencing the user's past viewing history during the generation process. For example, the generation unit can use AI to reference content the user has previously viewed and generate optimal music and visual effects. For example, the generation unit can also use AI to suggest optimal music and visual effects based on the user's past viewing history. Furthermore, the generation unit can use AI to analyze the user's viewing history and generate optimal music and visual effects. This improves the user experience by generating optimal music and visual effects by referencing the user's past viewing history. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's viewing history data into a generation AI and have the generation AI perform the generation of optimal music and visual effects.

[0097] The generation unit can customize the generated content based on the user's current viewing environment during generation. For example, the generation unit can use AI to detect the type of device the user is using and generate music and visual effects accordingly. For example, the generation unit can also use AI to detect the user's viewing environment (brightness, volume, etc.) and generate music and visual effects accordingly. Furthermore, the generation unit can use AI to analyze the user's viewing environment and generate optimal music and visual effects. This allows the generation unit to provide the user with the best possible experience by customizing the generated content based on the user's current viewing environment. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user viewing environment data into the generation AI and have the generation AI perform the customization of the generated content.

[0098] The generation unit can estimate the user's emotions and determine the priority of the music and visual effects to generate based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and prioritize generating energetic music and visual effects. For example, if the user is relaxed, the AI ​​can detect that emotion and prioritize generating calming music and visual effects. Also, if the user is stressed, the AI ​​can detect that emotion and prioritize generating relaxing music and visual effects. In this way, by determining the priority of the music and visual effects generated based on the user's emotions, the optimal experience for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0099] The generation unit can generate optimal music and visual effects while considering the user's geographical location information. For example, the generation unit can use AI to reference the user's geographical location information and generate music and visual effects suitable for that region. For example, the generation unit can use AI to suggest region-specific music and visual effects based on the user's geographical location information. The generation unit can also use AI to analyze the user's geographical location information and generate optimal music and visual effects. In this way, by considering the user's geographical location information, it is possible to generate optimal music and visual effects according to the region. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information data into the generation AI and have the generation AI execute the generation of optimal music and visual effects.

[0100] The generation unit can customize the generated content by referencing the user's social media activity during the generation process. For example, the generation unit can use AI to reference the user's social media activity and generate music and visual effects based on it. For example, the generation unit can also use AI to suggest customized music and visual effects based on the user's social media activity. Alternatively, the generation unit can use AI to analyze the user's social media activity and generate optimal music and visual effects. This allows the generation content to be customized by referencing the user's social media activity, providing the user with the best possible experience. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the customization of the generated content.

[0101] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect that emotion and quickly collect the data. For example, if the user is relaxed, the AI ​​can detect that emotion and collect the data in detail. Also, if the user is stressed, the AI ​​can detect that emotion and collect the data in a simplified manner. This allows for more accurate data collection by adjusting the data collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The data collection unit can select the optimal data collection method by referring to the user's past data collection history during data collection. For example, the AI ​​in the data collection unit can refer to what kind of data the user has collected in the past and reflect this in the current data collection. For example, the AI ​​in the data collection unit can select the optimal data collection method based on the user's past data collection history. Furthermore, the AI ​​in the data collection unit can learn from the user's past data collection history and continuously improve the data collection method. This allows the AI ​​to select the optimal data collection method by referring to the user's past data collection history and improve the accuracy of data collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal data collection method.

[0103] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and prioritize the collection of important data. For example, if the user is relaxed, the AI ​​can detect that emotion and prioritize the collection of detailed data. Also, if the user is stressed, the AI ​​can detect that emotion and prioritize the collection of simplified data. This allows for the priority collection of important data by prioritizing the data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, the data collection unit can use AI to refer to the user's geographical location information and prioritize the collection of data related to that region. For example, the data collection unit can use AI to prioritize the collection of region-specific data based on the user's geographical location information. Alternatively, the data collection unit can use AI to analyze the user's geographical location information and prioritize the collection of highly relevant data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0105] The output unit can estimate the user's emotions and adjust the content of the music and visual effects output based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and output energetic music and visual effects. For example, if the user is relaxed, the AI ​​can detect that emotion and output calming music and visual effects. Also, if the user is stressed, the AI ​​can detect that emotion and output relaxing music and visual effects. In this way, by adjusting the content of the music and visual effects output based on the user's emotions, the optimal experience for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The output unit can output optimal music and visual effects by referring to the user's past viewing history at the time of output. For example, the output unit can use AI to refer to content the user has previously viewed and output optimal music and visual effects. For example, the output unit can also use AI to suggest optimal music and visual effects based on the user's past viewing history. Furthermore, the output unit can use AI to analyze the user's viewing history and output optimal music and visual effects. This improves the user experience by outputting optimal music and visual effects by referring to the user's past viewing history. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input the user's viewing history data into a generating AI and have the generating AI execute the output of optimal music and visual effects.

[0107] The output unit can estimate the user's emotions and determine the priority of the music and visual effects to output based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and prioritize outputting energetic music and visual effects. For example, if the user is relaxed, the AI ​​can detect that emotion and prioritize outputting calming music and visual effects. Also, if the user is stressed, the AI ​​can detect that emotion and prioritize outputting relaxing music and visual effects. In this way, by determining the priority of the music and visual effects to output based on the user's emotions, the optimal experience for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The output unit can output optimal music and visual effects while considering the user's geographical location information. For example, the output unit can use AI to refer to the user's geographical location information and output music and visual effects appropriate for that region. For example, the output unit can use AI to suggest region-specific music and visual effects based on the user's geographical location information. Alternatively, the output unit can use AI to analyze the user's geographical location information and output optimal music and visual effects. This allows for the output of optimal music and visual effects tailored to the region by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input the user's geographical location data into a generating AI and have the generating AI execute the output of optimal music and visual effects.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The music-linked agent system can estimate the user's emotions and adjust the content of the live experience based on those emotions. For example, if the user is excited, the system can provide an energetic performance. If the user is relaxed, the system can provide a calming performance. Furthermore, if the user is stressed, the system can provide relaxing content. This allows for the provision of an optimal live experience tailored to the user's emotions.

[0111] The music integration agent system can refer to a user's past listening history to identify their preferred music genres and artists, and then customize the live experience based on that information. For example, it can prioritize performances by artists the user has listened to frequently in the past. It can also adjust the live setlist to match the user's preferred music genres. Furthermore, it can suggest new artists and songs based on the user's listening history. This allows for the provision of a personalized live experience tailored to the user's preferences.

[0112] The music integration agent system can provide different live experiences for each region, taking into account the user's geographical location. For example, it can prioritize live performances by artists popular in a particular region. It can also provide special performances tailored to local culture and events. Furthermore, it can analyze user reactions in each region and optimize the live experience based on that information. This allows for the provision of customized live experiences for each region.

[0113] The music integration agent system can customize the live experience by referencing the user's social media activity. For example, it can prioritize live performances by artists the user follows on social media. It can also adjust the live setlist based on the music the user has shared on social media. Furthermore, it can analyze the user's social media reactions and optimize the live experience based on that information. This allows for the provision of a personalized live experience based on the user's social media activity.

[0114] The music-integrated agent system can customize the live experience based on the user's viewing environment. For example, it can provide optimal sound and video quality depending on the type of device the user is using. It can also adjust the live performance based on the brightness and volume of the user's viewing environment. Furthermore, it can optimize the interface design and operation methods based on the user's viewing environment. This allows for the provision of an optimal live experience tailored to the user's viewing environment.

[0115] The music-integrated agent system can estimate the user's emotions and adjust the interaction during the live experience based on those emotions. For example, if the user is excited, the system can increase interaction with the artist. If the user is relaxed, the system can provide gentler interactions. Furthermore, if the user is stressed, the system can provide relaxing interactions. This allows the system to provide optimal interaction tailored to the user's emotions.

[0116] The music-integrated agent system can optimize the live experience by referencing past user reaction data. For example, it can analyze how users have reacted in the past and adjust the live performance based on that information. It can also provide optimal content based on past user reaction data. Furthermore, it can continuously learn from user reaction data and improve the live experience. This allows for the provision of an optimal live experience based on past user reaction data.

[0117] The music-integrated agent system can estimate the user's emotions and adjust how the live experience is delivered based on those emotions. For example, if the user is excited, the system can provide an energetic experience. If the user is relaxed, the system can provide a calming experience. Furthermore, if the user is stressed, the system can provide a relaxing experience. This allows the system to provide an optimal live experience tailored to the user's emotions.

[0118] The music integration agent system can provide different live experiences for each region, taking into account the user's geographical location. For example, it can prioritize live performances by artists popular in a particular region. It can also provide special performances tailored to local culture and events. Furthermore, it can analyze user reactions in each region and optimize the live experience based on that information. This allows for the provision of customized live experiences for each region.

[0119] The music-linked agent system can estimate the user's emotions and adjust the content of the live experience based on those emotions. For example, if the user is excited, the system can provide an energetic performance. If the user is relaxed, the system can provide a calming performance. Furthermore, if the user is stressed, the system can provide relaxing content. This allows for the provision of an optimal live experience tailored to the user's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The analysis unit analyzes user responses. User responses include facial expressions, voice, and behavioral logs. For example, facial expression recognition technology is used to analyze the user's facial expressions, and voice analysis technology is used to analyze the user's voice. Furthermore, behavioral logs can be analyzed to identify user behavioral patterns. Step 2: The service department provides a customized experience based on the responses analyzed by the analytics department. This customized experience may include individual content and interface modifications. For example, content may be customized according to user preferences, and the interface may be modified based on user responses. Step 3: The recognition unit has real-time speech recognition and translation capabilities. For example, it uses a speech recognition algorithm to recognize the user's speech in real time and a translation engine to translate the user's speech in real time. Furthermore, it can support multiple languages. Step 4: The generation unit generates music and visual effects using AI. For example, it can generate music in real time using a music generation AI and generate visual effects in real time using a visual effect generation AI. Furthermore, it can adjust the content of the music and visual effects based on user reactions.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the analysis unit, provision unit, recognition unit, generation unit, collection unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14 and analyzes them with the control unit 46A. The provision unit provides a customized experience with the specific processing unit 290 of the data processing unit 12. The recognition unit implements real-time speech recognition and translation functions with the control unit 46A of the smart device 14. The generation unit generates AI-generated music and visual effects with the specific processing unit 290 of the data processing unit 12. The collection unit collects the user's biometric data using the sensors of the smart device 14 and analyzes it with the specific processing unit 290 of the data processing unit 12. The output unit provides the generated music and visual effects using the speaker 40B and display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the analysis unit, provision unit, recognition unit, generation unit, collection unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214 and analyzes them with the control unit 46A. The provision unit provides a customized experience with the specific processing unit 290 of the data processing unit 12. The recognition unit implements real-time speech recognition and translation functions with the control unit 46A of the smart glasses 214. The generation unit generates AI-generated music and visual effects with the specific processing unit 290 of the data processing unit 12. The collection unit collects the user's biometric data using the sensors of the smart glasses 214 and analyzes it with the specific processing unit 290 of the data processing unit 12. The output unit provides the generated music and visual effects using the speaker 240 and display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the analysis unit, provision unit, recognition unit, generation unit, collection unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314 and analyzes them with the control unit 46A. The provision unit provides a customized experience with the specific processing unit 290 of the data processing unit 12. The recognition unit implements real-time speech recognition and translation functions with the control unit 46A of the headset terminal 314. The generation unit generates AI-generated music and visual effects with the specific processing unit 290 of the data processing unit 12. The collection unit collects the user's biometric data using the sensors of the headset terminal 314 and analyzes it with the specific processing unit 290 of the data processing unit 12. The output unit provides the generated music and visual effects using the speaker 240 and display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the analysis unit, provision unit, recognition unit, generation unit, collection unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and analyzes them with the control unit 46A. The provision unit provides a customized experience with the specific processing unit 290 of the data processing unit 12. The recognition unit implements real-time speech recognition and translation functions with the control unit 46A of the robot 414. The generation unit generates AI-generated music and visual effects with the specific processing unit 290 of the data processing unit 12. The collection unit collects the user's biometric data using the sensors of the robot 414 and analyzes it with the specific processing unit 290 of the data processing unit 12. The output unit provides the generated music and visual effects using the speaker 240 and display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) The analysis department analyzes user reactions, A provisioning unit that provides a customized experience based on the reactions analyzed by the aforementioned analysis unit, A recognition unit with real-time speech recognition and translation capabilities, It includes a generation unit that generates music and visual effects generated by AI. A system characterized by the following features. (Note 2) It includes a data collection unit that collects data for analyzing user reactions. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an output section for providing generated music and visual effects. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recognition unit, Features real-time speech recognition and translation capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is AI generates music and visual effects. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates the user's emotions and adjusts the response analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to the user's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During analysis, we identify reaction patterns by considering the user's viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, we consider the user's geographical location to analyze regional trends in responses. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During the analysis, we refer to users' social media activity to analyze the relevance of responses. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the experience is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing content, we refer to the user's past viewing history to deliver the best possible experience. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing the service, the content of the experience will be customized based on the user's current viewing environment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the experience to be provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing the service, we take the user's geographical location into consideration to deliver the optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the service, the user's social media activity is referenced to customize the experience. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recognition unit, It estimates the user's emotions and adjusts the accuracy of speech recognition and translation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recognition unit, During recognition, the recognition algorithm is optimized by referring to the user's past speech data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recognition unit, During recognition, the system determines the translation priority based on the user's utterance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recognition unit, When recognition occurs, the system provides the optimal translation while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recognition unit, During recognition, the system improves recognition accuracy by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts the content of the music and visual effects generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the system references the user's past viewing history to generate the most suitable music and visual effects. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the generated content is customized based on the user's current viewing environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and determines the priority of music and visual effects to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the system takes the user's geographical location into consideration to generate the optimal music and visual effects. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the generated content is customized by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned collection unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned collection unit is During data collection, the system selects the optimal collection method by referring to the user's past data collection history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The output unit is, It estimates the user's emotions and adjusts the content of the music and visual effects output based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The output unit is, During output, the system references the user's past viewing history to output the most suitable music and visual effects. The system described in Appendix 3, characterized by the features described herein. (Note 36) The output unit is, It estimates the user's emotions and determines the priority of music and visual effects to be displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The output unit is, When outputting, the system considers the user's geographical location to output the most suitable music and visual effects. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes user reactions, A provisioning unit that provides a customized experience based on the reactions analyzed by the aforementioned analysis unit, A recognition unit with real-time speech recognition and translation capabilities, It comprises a generation unit that generates music and visual effects generated by AI. A system characterized by the following features.

2. It includes a data collection unit that collects data for analyzing user reactions. The system according to feature 1.

3. It features an output section for providing generated music and visual effects. The system according to feature 1.

4. The aforementioned recognition unit, Features real-time speech recognition and translation capabilities. The system according to feature 1.

5. The generating unit is Generate music and visual effects using AI. The system according to feature 1.

6. The aforementioned analysis unit is It estimates the user's emotions and adjusts the response analysis method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to the user's past response data. The system according to feature 1.

8. The aforementioned analysis unit is During analysis, we identify reaction patterns by considering the user's viewing history. The system according to feature 1.

9. The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

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