system

Abstract

The system according to this embodiment aims to improve the user experience by optimizing content based on the user's interests. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an optimization unit, and a provision unit. The collection unit collects user preferences and activity history. The analysis unit analyzes the data collected by the collection unit. The optimization unit optimizes the content based on the analysis results obtained by the analysis unit. The provision unit provides the content optimized by the optimization unit.

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, and includes 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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, optimization of content based on a user's interests and concerns has not been sufficiently performed in the SNS platform, and there are problems of excessive information and display of content with low relevance.

[0005] The system according to the embodiment aims to optimize content based on a user's interests and concerns and improve the user experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a provision unit. The collection unit collects user preferences and activity history. The analysis unit analyzes the data collected by the collection unit. The optimization unit optimizes the content based on the analysis results obtained by the analysis unit. The provision unit provides the content optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize content based on user interests and improve the user experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music-focused SNS platform according to an embodiment of the present invention is a system that connects musicians and fans. This system uses an AI agent to optimize content based on the user's interests. This promotes the discovery of new music and interaction with artists, thereby revitalizing the music community. This service solves the problems of information overload and irrelevant content display that plague conventional SNS, improving the user experience. For example, the system uses AI to analyze the user's preferences and activity history and displays highly relevant posts and news. This allows users to easily find content that matches their interests. Next, the system provides artist support functions, such as supporting crowdfunding, merchandise sales, and live streaming. This makes it easier for artists to receive support from fans. Furthermore, the system has a community-building function that promotes interaction among users with shared musical tastes. This allows users to connect with like-minded individuals. In addition, the system uses a notification optimization function to notify users only of information they are interested in at the appropriate time. This prevents users from missing important information. Finally, the system has a safe environment provision function in which AI detects and removes inappropriate content and spam. This allows users to use the platform with peace of mind. This allows music-focused social networking platforms to optimize content based on users' interests and improve the user experience.

[0029] The music-focused SNS platform according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a content provision unit. The data collection unit collects user preferences and activity history. User preferences include, but are not limited to, music genres and artist preferences. The data collection unit can, for example, collect the user's past viewing history and search history. The analysis unit analyzes the data collected by the data collection unit. The analysis unit, for example, uses AI to analyze user preferences and activity history and identifies the user's interests. The optimization unit optimizes content based on the analysis results obtained by the analysis unit. The optimization unit, for example, uses AI to select content based on the user's interests and adjusts the display order. The content provision unit provides the content optimized by the optimization unit. The content provision unit, for example, displays content selected based on the user's interests. As a result, the music-focused SNS platform according to this embodiment can improve the user experience by optimizing and providing content based on the user's preferences and activity history.

[0030] The data collection unit collects user preferences and activity history. User preferences include, but are not limited to, music genres and artist preferences. The data collection unit can, for example, collect users' past listening and search history. Specifically, it collects the history of songs played by the user, playlist creation history, rating and comment history, and even information on artists and other users that the user follows. This data is important for understanding user behavior patterns and preferences in detail. The data collection unit collects this data in real time and stores it in a central database. Furthermore, the data collection unit also collects posts and shares made by users on social media, as well as interactions with other users. This allows for an understanding of users' interest in music and communication trends. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and optimization departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analytics department analyzes the data collected by the data collection department. For example, the analytics department uses AI to analyze user preferences and activity history to identify user interests. Specifically, it uses machine learning algorithms to extract patterns from users' viewing and search history to identify music genres and artists that users prefer. For example, it profiles users' musical preferences based on the artists and genres they frequently listen to. It also uses natural language processing technology to analyze emotions and opinions from user comments and reviews to gain a deeper understanding of user interests. Furthermore, the analytics department can analyze user behavior data over time to grasp changes and trends in user preferences. This allows it to identify genres and artists that users have recently become interested in and provide appropriate content. Based on these analysis results, the analytics department builds personalized music recommendation models for each user. This enables the analytics department to accurately identify user preferences and interests and provide a foundation for delivering optimal content.

[0032] The optimization unit optimizes content based on the analysis results obtained by the analysis unit. For example, the optimization unit uses AI to select content based on user interests and adjust the display order. Specifically, it prioritizes displaying songs and artists that match the user's preferences and recommends new songs and artists that the user might be interested in. For example, if a user likes a particular genre, it prioritizes displaying new and popular songs related to that genre. It also recommends songs similar to those the user has highly rated based on the user's past listening history and ratings. Furthermore, the optimization unit can analyze user behavior data in real time and dynamically adjust content in response to changes in the user's interests. For example, if a user has recently been frequently listening to a particular artist, it prioritizes displaying new songs and related songs by that artist. In addition, the optimization unit can collect user feedback and continuously improve the accuracy of its recommendation algorithm. As a result, the optimization unit can provide optimal content based on user interests and improve the user experience.

[0033] The content delivery unit provides content optimized by the optimization unit. For example, the content delivery unit displays content selected based on the user's interests. Specifically, it displays information about songs and artists that match the user's preferences on the user's home screen and feed. For example, it displays new and popular songs in genres the user likes on the home screen, and displays live performance information and new song release information of artists the user might be interested in on the feed. The content delivery unit also recommends related songs and artists based on the songs the user has listened to. For example, if a user listens to a song by a particular artist, it will recommend other songs and artists related to that artist. Furthermore, the content delivery unit can collect user feedback and continuously improve the accuracy and effectiveness of the content it provides. For example, if a user gives a high rating to a recommended song, it will prioritize recommending songs similar to that song. The content delivery unit also supports multiple devices and platforms, ensuring that users can access the optimal content from any device. This allows the content delivery unit to quickly and reliably deliver the optimal content to users, improving the user experience.

[0034] A music-focused social networking platform includes a support section that assists artists with fundraising, merchandise sales, and live streaming. This support section can, for example, support artists' projects through crowdfunding. It can, for example, sell artists' merchandise through online shops. It can, for example, support artists' live streams through streaming services. This makes it easier for artists to receive support from fans. Some or all of the above processes in the support section may be performed using AI, or not.

[0035] A music-focused social networking platform includes a communication section that facilitates interaction among users who share a common musical interest. This communication section can, for example, facilitate interaction among users through online forums. It can also facilitate interaction among users through offline events. Furthermore, it can facilitate interaction among users through chat functions. This allows users to connect with others who share the same interests. Some or all of the above-described processes in the communication section may be performed using AI, or they may not.

[0036] A music-focused social networking platform includes a notification unit that notifies users of only the information they are interested in at the appropriate time. The notification unit can, for example, adjust the timing of notifications based on the user's behavior patterns. The notification unit can, for example, customize the content of notifications based on the user's interests. The notification unit can, for example, adjust the timing of notifications based on the user's time of day. This ensures that users do not miss important information. Some or all of the above processing in the notification unit may be performed using AI or not.

[0037] The music-focused SNS platform includes a detection unit that detects and removes inappropriate content and spam. The detection unit can, for example, use AI to detect inappropriate content. The detection unit can, for example, use AI to detect spam. The detection unit can, for example, use AI to remove inappropriate content and spam. This allows users to use the platform with peace of mind. Some or all of the above-described processes in the detection unit may be performed using AI or not using AI.

[0038] The data collection unit can analyze the user's past activity history and select the optimal data collection method. For example, the data collection unit may prioritize collecting content that the user has frequently accessed in the past. For example, if the data collection unit tends to be active during certain time periods, it may concentrate data collection during those times. For example, the data collection unit may analyze the user's past behavior patterns and suggest the optimal data collection method. This enables efficient data collection by selecting the optimal data collection method based on the user's past activity history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0039] The data collection unit can filter data based on the user's current musical preferences and areas of interest. For example, the data collection unit can collect relevant content based on the music genres the user has recently listened to. For example, the data collection unit can prioritize collecting new release information from artists the user follows. For example, the data collection unit can collect relevant news and articles based on the user's current areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current musical preferences and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of music event information in the area where the user is currently located. For example, the data collection unit can collect information on local artists based on the user's geographical location. For example, the data collection unit can collect region-specific music news based on the user's location information. By collecting highly relevant data while considering the user's geographical location information, the data collection unit can provide information tailored to the user. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect the latest information on artists that the user follows on social media. For example, the data collection unit can collect relevant content based on music that the user has shared on social media. For example, the data collection unit can collect music news that might be of interest to the user based on their social media activity. In this way, by analyzing the user's social media activity and collecting relevant data, it is possible to provide information that is relevant to the user. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on high-importance data. For example, the analysis unit performs a concise analysis on low-importance data. The analysis unit adjusts the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms for each music genre. For example, the analysis unit can select an appropriate analysis algorithm based on the user's activity history. For example, the analysis unit can apply the optimal analysis method depending on the data category. By applying different analysis algorithms depending on the data category, the optimal analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis of historical data according to its importance. The analysis unit may adjust the order of analysis based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0046] The optimization unit can improve the accuracy of optimization by considering the interrelationships of data during the optimization process. For example, the optimization unit combines relevant data to provide optimal content. For example, the optimization unit analyzes the interrelationships of data and selects the optimal display method. For example, the optimization unit improves the accuracy of optimization based on the interrelationships of data. As a result, by improving the accuracy of optimization by considering the interrelationships of data, content suitable for the user can be provided. Some or all of the above-described processes in the optimization unit may be performed using AI or not.

[0047] The optimization unit can perform optimization while considering the attribute information of the data submitter. For example, if the data submitter is an artist, the optimization unit will perform optimization while considering that attribute information. For example, if the data submitter is a fan, the optimization unit will perform optimization while considering that attribute information. For example, the optimization unit will improve the accuracy of optimization based on the attribute information of the data submitter. As a result, by performing optimization while considering the attribute information of the data submitter, content suitable for the user can be provided. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0048] The optimization unit can perform optimization while considering the geographical distribution of the data. For example, the optimization unit can prioritize displaying local content based on the user's geographical location. For example, the optimization unit can select the optimal display method while considering the geographical distribution of the data. For example, the optimization unit can improve the accuracy of optimization based on the geographical distribution of the data. As a result, by performing optimization while considering the geographical distribution of the data, content suitable for the user can be provided. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0049] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the data during the optimization process. For example, the optimization unit provides optimal content by referring to relevant literature on the data. For example, the optimization unit selects the optimal display method based on the relevant literature. For example, the optimization unit improves the accuracy of optimization based on the relevant literature. In this way, by improving the accuracy of optimization by referring to relevant literature on the data, content suitable for the user can be provided. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0050] The content delivery unit can select the optimal delivery method by referring to the user's past browsing history when providing content. For example, the content delivery unit provides relevant content based on content the user has previously viewed. For example, the content delivery unit analyzes the user's past browsing history and selects the optimal delivery method. For example, the content delivery unit selects the optimal display method based on the user's past browsing history. In this way, by selecting the optimal delivery method by referring to the user's past browsing history, content suitable for the user can be provided. Some or all of the above processing in the content delivery unit may be performed using AI or not.

[0051] The content delivery unit can customize the content it provides based on the user's current areas of interest. For example, it can provide relevant content based on the music genre the user is currently interested in. For example, it can prioritize providing the latest information on artists the user follows. For example, it can provide relevant news and articles based on the user's current areas of interest. By customizing the content provided based on the user's current areas of interest, it is possible to provide content that is suitable for the user. Some or all of the above processing in the content delivery unit may be performed using AI or not.

[0052] The content delivery unit can select the optimal delivery method by considering the user's device information when providing content. For example, if the user is using a smartphone, the delivery unit will provide content that matches the screen size. For example, if the user is using a tablet, the delivery unit will provide content optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit will provide concise and highly visible content. In this way, by selecting the optimal delivery method by considering the user's device information, content suitable for the user can be provided. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0053] The content delivery unit can analyze a user's social media activity and provide relevant content when delivering content. For example, the delivery unit can provide the latest information on artists the user follows on social media. For example, the delivery unit can provide relevant content based on music the user has shared on social media. For example, the delivery unit can provide music news that the user might be interested in based on their social media activity. In this way, by analyzing a user's social media activity and providing relevant content, the delivery unit can provide content that is tailored to the user. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0054] The support department can select the optimal support method by referring to the artist's past activity history when providing support. For example, the support department may propose the optimal support method based on the artist's past successful live performances. For example, the support department may analyze the artist's past funding history and select the optimal support method. For example, the support department may propose the optimal support method based on the artist's past activity history. In this way, by selecting the optimal support method by referring to the artist's past activity history, it is possible to provide support that is appropriate for the artist. Some or all of the above processes in the support department may be performed using AI or not.

[0055] The support department can customize the means of support based on the artist's current activities when providing support. For example, the support department may propose the optimal support method based on the project the artist is currently working on. For example, the support department may analyze the artist's current activities and select the optimal means of support. For example, the support department may customize the means of support based on the artist's current activities. This allows the support department to provide support that is appropriate for the artist by customizing the means of support based on the artist's current activities. Some or all of the above processes in the support department may be performed using AI or not.

[0056] The support department can select the most suitable support method when providing support, taking into account the artist's geographical location. For example, the support department may prioritize providing funding information for the area where the artist is currently located. For example, the support department may propose local support methods based on the artist's geographical location. For example, the support department may provide region-specific support methods based on the artist's location information. In this way, by selecting the most suitable support method considering the artist's geographical location, it is possible to provide support that is appropriate for the artist. Some or all of the above processing in the support department may be performed using AI or not.

[0057] The support department can analyze the artist's social media activity and propose support methods when providing assistance. For example, the support department can provide support information from fans that the artist follows on social media. For example, the support department can propose support methods based on projects that the artist has shared on social media. For example, the support department can propose the most suitable support method based on the artist's social media activity. In this way, by analyzing the artist's social media activity and proposing support methods, it is possible to provide support that is appropriate for the artist. Some or all of the above processing in the support department may be performed using AI or not.

[0058] The interaction unit can select the optimal interaction method by referring to the user's past interaction history during interaction. For example, the interaction unit may suggest the optimal interaction method based on successful past interactions with the user. For example, the interaction unit may analyze the user's past interaction history and select the optimal interaction method. For example, the interaction unit may suggest the optimal interaction method based on the user's past interaction history. In this way, by selecting the optimal interaction method by referring to the user's past interaction history, it is possible to provide interactions that are suitable for the user. Some or all of the above processing in the interaction unit may be performed using AI, or it may be performed without using AI.

[0059] The interaction unit can customize the means of interaction based on the user's current areas of interest during interaction. For example, the interaction unit can provide relevant interaction methods based on the music genres the user is currently interested in. For example, the interaction unit can suggest interaction methods based on the latest information from artists the user follows. For example, the interaction unit can provide the optimal means of interaction based on the user's current areas of interest. In this way, by customizing the means of interaction based on the user's current areas of interest, it is possible to provide interactions that are suitable for the user. Some or all of the above processing in the interaction unit may be performed using AI or not.

[0060] The interaction unit can select the optimal interaction method by considering the user's geographical location information during interaction. For example, the interaction unit may prioritize providing music event information in the user's current location. For example, the interaction unit may suggest local interaction methods based on the user's geographical location. For example, the interaction unit may provide region-specific interaction methods based on the user's location information. In this way, by selecting the optimal interaction method by considering the user's geographical location information, it is possible to provide interactions that are suitable for the user. Some or all of the above processing in the interaction unit may be performed using AI or not.

[0061] The interaction unit can analyze a user's social media activity during interaction and suggest ways to interact. For example, the interaction unit can provide the latest information on artists the user follows on social media. For example, the interaction unit can provide relevant interaction methods based on music the user has shared on social media. For example, the interaction unit can suggest the most suitable way to interact based on the user's social media activity. In this way, by analyzing the user's social media activity and suggesting ways to interact, it can provide interactions that are appropriate for the user. Some or all of the above processing in the interaction unit may be performed using AI or not.

[0062] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may suggest the optimal notification method based on successful examples of notifications the user has received in the past. For example, the notification unit may analyze the user's past notification history and select the optimal notification method. For example, the notification unit may suggest the optimal notification method based on the user's past notification history. In this way, by selecting the optimal notification method by referring to the user's past notification history, it is possible to provide notifications that are appropriate for the user. Some or all of the above processing in the notification unit may be performed using AI or not.

[0063] The notification unit can customize the content of notifications based on the user's current areas of interest. For example, the notification unit can provide relevant notifications based on the music genres the user is currently interested in. For example, the notification unit can prioritize notifications about the latest information from artists the user follows. For example, the notification unit can notify the user of relevant news and articles based on the user's current areas of interest. In this way, by customizing the content of notifications based on the user's current areas of interest, it is possible to provide notifications that are appropriate for the user. Some or all of the above processing in the notification unit may be performed using AI or not.

[0064] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit provides a notification method that matches the screen size. For example, if the user is using a tablet, the notification unit provides a notification method optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit provides a concise and highly visible notification method. By selecting the optimal notification method considering the user's device information, the notification unit can provide notifications that are appropriate for the user. Some or all of the above processing in the notification unit may be performed using AI, or it may be performed without using AI.

[0065] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, the notification unit may notify the user of the latest information about artists the user follows on social media. For example, the notification unit may provide relevant notifications based on music the user has shared on social media. For example, the notification unit may notify the user of music news that might be of interest to the user based on their social media activity. In this way, by analyzing the user's social media activity and providing relevant notifications, the notification unit can provide notifications that are tailored to the user. Some or all of the above processing in the notification unit may be performed using AI or not.

[0066] The detection unit can optimize the detection algorithm by referring to past detection history during detection. For example, the detection unit selects the optimal detection algorithm based on past detection history. For example, the detection unit analyzes the detection history to improve the accuracy of the detection algorithm. For example, the detection unit proposes the optimal detection method based on past detection history. In this way, the accuracy of detection is improved by optimizing the detection algorithm by referring to past detection history. Some or all of the above processes in the detection unit may be performed using AI or not.

[0067] The detection unit can apply different detection algorithms depending on the content category during detection. For example, the detection unit can apply different detection algorithms for each music genre. For example, the detection unit can select an appropriate detection algorithm based on the content category. For example, the detection unit can apply the optimal detection method depending on the content category. This improves the accuracy of detection by applying different detection algorithms depending on the content category. Some or all of the above processing in the detection unit may be performed using AI or not.

[0068] The detection unit can perform detection while considering the geographical distribution of content. For example, the detection unit prioritizes the detection of local content based on the user's geographical location. For example, the detection unit selects the optimal detection method while considering the geographical distribution of content. For example, the detection unit improves the accuracy of detection based on the geographical distribution of content. As a result, by performing detection while considering the geographical distribution of content, it is possible to provide content that is appropriate for the user. Some or all of the above processing in the detection unit may be performed using AI or not.

[0069] The detection unit can improve detection accuracy by referring to related literature for the content during detection. For example, the detection unit provides an optimal detection method by referring to related literature for the content. For example, the detection unit selects an optimal detection algorithm based on the related literature. For example, the detection unit improves detection accuracy based on the related literature. In this way, by improving detection accuracy by referring to related literature for the content, content suitable for the user can be provided. Some or all of the above processing in the detection unit may be performed using AI or not using AI.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The data collection unit can collect not only the user's musical preferences but also their lifestyle and daily activity patterns. For example, it can collect data on the music the user listens to while exercising or relaxing. This allows for music suggestions tailored to the user's lifestyle. The data collection unit can also collect the user's device usage and provide content optimized for that device. For example, it can provide short video clips when using a smartphone and longer live videos when using a tablet. Furthermore, the data collection unit can collect the user's social media activity and suggest relevant content based on the music the user shares and the artists they follow.

[0072] The support department can assist artists with crowdfunding, merchandise sales, live streaming, and promotional activities. For example, it can help with creating social media ads and press releases when an artist releases a new song. The support department can also help plan and manage events for artists to interact with their fans. For example, it can help with online meet-and-greets and fan-only live streams. Furthermore, the support department can help increase an artist's media exposure by supporting the publication of interviews and feature articles. In this way, it can provide comprehensive support for an artist's promotional activities.

[0073] The community section can offer music-related games and quizzes to facilitate interaction among users with shared musical tastes. For example, it can provide music trivia quizzes and games that test knowledge about artists. The community section can also provide a function for users to share and exchange their music playlists with other users, allowing them to share their musical tastes. Furthermore, the community section can provide a function for users to post their music reviews and opinions and discuss them with other users, thereby facilitating active exchange of opinions about music.

[0074] The notification unit notifies users only of information they are interested in at the right time, and can also adjust notification timing based on the user's schedule. For example, it can link with the user's calendar app to refrain from sending notifications before and after important meetings or events. Furthermore, the notification unit can analyze the user's notification history, learn patterns of notifications that have received positive responses in the past, and select the optimal notification method. In addition, the notification unit can consider the user's device's battery level and refrain from sending notifications when the battery is low. This enables flexible notifications tailored to the user's situation.

[0075] In addition to detecting and eliminating inappropriate content and spam, the detection unit can improve its detection algorithm based on user feedback. For example, it can provide a function for users to report content they deem inappropriate, and adjust the detection algorithm based on that feedback. Furthermore, the detection unit can perform appropriate content filtering based on the user's age and region. For example, it can prevent adult content from being displayed to underage users. In addition, the detection unit can filter out irrelevant content based on the user's interests. This ensures that only content beneficial to the user is displayed.

[0076] The data collection unit can analyze a user's past activity history and select the optimal data collection method. For example, it can prioritize collecting content that a user has frequently accessed in the past. If a user tends to be active during specific time periods, it can concentrate data collection during those times. By analyzing the user's past behavior patterns, it can propose the optimal data collection method. This enables efficient data collection by selecting the most suitable method based on the user's past activity history.

[0077] The data collection unit can filter data based on the user's current musical preferences and areas of interest. For example, it can collect relevant content based on the music genres the user has recently listened to, prioritize collecting new release information from artists the user follows, and collect relevant news and articles based on the user's current areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current musical preferences and areas of interest.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, it can prioritize the collection of music event information in the user's current location. It can also collect information on local artists based on the user's geographical location. Furthermore, it can collect region-specific music news based on the user's location. By collecting highly relevant data while considering the user's geographical location, it can provide information tailored to the user.

[0079] The following briefly describes the processing flow for example form 1.

[0080] Step 1: The data collection unit collects the user's preferences and activity history. User preferences include, but are not limited to, music genres and artist preferences. The data collection unit can also collect the user's past viewing and search history. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department uses AI to analyze user preferences and activity history to identify user interests. Step 3: The optimization unit optimizes the content based on the analysis results obtained by the analysis unit. The optimization unit uses AI to select content based on user interests and adjusts the display order. Step 4: The delivery unit provides content optimized by the optimization unit. The delivery unit displays content selected based on the user's interests.

[0081] (Example of form 2) The music-focused SNS platform according to an embodiment of the present invention is a system that connects musicians and fans. This system uses an AI agent to optimize content based on the user's interests. This promotes the discovery of new music and interaction with artists, thereby revitalizing the music community. This service solves the problems of information overload and irrelevant content display that plague conventional SNS, improving the user experience. For example, the system uses AI to analyze the user's preferences and activity history and displays highly relevant posts and news. This allows users to easily find content that matches their interests. Next, the system provides artist support functions, such as supporting crowdfunding, merchandise sales, and live streaming. This makes it easier for artists to receive support from fans. Furthermore, the system has a community-building function that promotes interaction among users with shared musical tastes. This allows users to connect with like-minded individuals. In addition, the system uses a notification optimization function to notify users only of information they are interested in at the appropriate time. This prevents users from missing important information. Finally, the system has a safe environment provision function in which AI detects and removes inappropriate content and spam. This allows users to use the platform with peace of mind. This allows music-focused social networking platforms to optimize content based on users' interests and improve the user experience.

[0082] The music-focused SNS platform according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a content provision unit. The data collection unit collects user preferences and activity history. User preferences include, but are not limited to, music genres and artist preferences. The data collection unit can, for example, collect the user's past viewing history and search history. The analysis unit analyzes the data collected by the data collection unit. The analysis unit, for example, uses AI to analyze user preferences and activity history and identifies the user's interests. The optimization unit optimizes content based on the analysis results obtained by the analysis unit. The optimization unit, for example, uses AI to select content based on the user's interests and adjusts the display order. The content provision unit provides the content optimized by the optimization unit. The content provision unit, for example, displays content selected based on the user's interests. As a result, the music-focused SNS platform according to this embodiment can improve the user experience by optimizing and providing content based on the user's preferences and activity history.

[0083] The data collection unit collects user preferences and activity history. User preferences include, but are not limited to, music genres and artist preferences. The data collection unit can, for example, collect users' past listening and search history. Specifically, it collects the history of songs played by the user, playlist creation history, rating and comment history, and even information on artists and other users that the user follows. This data is important for understanding user behavior patterns and preferences in detail. The data collection unit collects this data in real time and stores it in a central database. Furthermore, the data collection unit also collects posts and shares made by users on social media, as well as interactions with other users. This allows for an understanding of users' interest in music and communication trends. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and optimization departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0084] The analytics department analyzes the data collected by the data collection department. For example, the analytics department uses AI to analyze user preferences and activity history to identify user interests. Specifically, it uses machine learning algorithms to extract patterns from users' viewing and search history to identify music genres and artists that users prefer. For example, it profiles users' musical preferences based on the artists and genres they frequently listen to. It also uses natural language processing technology to analyze emotions and opinions from user comments and reviews to gain a deeper understanding of user interests. Furthermore, the analytics department can analyze user behavior data over time to grasp changes and trends in user preferences. This allows it to identify genres and artists that users have recently become interested in and provide appropriate content. Based on these analysis results, the analytics department builds personalized music recommendation models for each user. This enables the analytics department to accurately identify user preferences and interests and provide a foundation for delivering optimal content.

[0085] The optimization unit optimizes content based on the analysis results obtained by the analysis unit. For example, the optimization unit uses AI to select content based on user interests and adjust the display order. Specifically, it prioritizes displaying songs and artists that match the user's preferences and recommends new songs and artists that the user might be interested in. For example, if a user likes a particular genre, it prioritizes displaying new and popular songs related to that genre. It also recommends songs similar to those the user has highly rated based on the user's past listening history and ratings. Furthermore, the optimization unit can analyze user behavior data in real time and dynamically adjust content in response to changes in the user's interests. For example, if a user has recently been frequently listening to a particular artist, it prioritizes displaying new songs and related songs by that artist. In addition, the optimization unit can collect user feedback and continuously improve the accuracy of its recommendation algorithm. As a result, the optimization unit can provide optimal content based on user interests and improve the user experience.

[0086] The content delivery unit provides content optimized by the optimization unit. For example, the content delivery unit displays content selected based on the user's interests. Specifically, it displays information about songs and artists that match the user's preferences on the user's home screen and feed. For example, it displays new and popular songs in genres the user likes on the home screen, and displays live performance information and new song release information of artists the user might be interested in on the feed. The content delivery unit also recommends related songs and artists based on the songs the user has listened to. For example, if a user listens to a song by a particular artist, it will recommend other songs and artists related to that artist. Furthermore, the content delivery unit can collect user feedback and continuously improve the accuracy and effectiveness of the content it provides. For example, if a user gives a high rating to a recommended song, it will prioritize recommending songs similar to that song. The content delivery unit also supports multiple devices and platforms, ensuring that users can access the optimal content from any device. This allows the content delivery unit to quickly and reliably deliver the optimal content to users, improving the user experience.

[0087] A music-focused social networking platform includes a support section that assists artists with fundraising, merchandise sales, and live streaming. This support section can, for example, support artists' projects through crowdfunding. It can, for example, sell artists' merchandise through online shops. It can, for example, support artists' live streams through streaming services. This makes it easier for artists to receive support from fans. Some or all of the above processes in the support section may be performed using AI, or not.

[0088] A music-focused social networking platform includes a communication section that facilitates interaction among users who share a common musical interest. This communication section can, for example, facilitate interaction among users through online forums. It can also facilitate interaction among users through offline events. Furthermore, it can facilitate interaction among users through chat functions. This allows users to connect with others who share the same interests. Some or all of the above-described processes in the communication section may be performed using AI, or they may not.

[0089] A music-focused social networking platform includes a notification unit that notifies users of only the information they are interested in at the appropriate time. The notification unit can, for example, adjust the timing of notifications based on the user's behavior patterns. The notification unit can, for example, customize the content of notifications based on the user's interests. The notification unit can, for example, adjust the timing of notifications based on the user's time of day. This ensures that users do not miss important information. Some or all of the above processing in the notification unit may be performed using AI or not.

[0090] The music-focused SNS platform includes a detection unit that detects and removes inappropriate content and spam. The detection unit can, for example, use AI to detect inappropriate content. The detection unit can, for example, use AI to detect spam. The detection unit can, for example, use AI to remove inappropriate content and spam. This allows users to use the platform with peace of mind. Some or all of the above-described processes in the detection unit may be performed using AI or not using AI.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect data frequently to reflect their current interests. For example, if the user is stressed, the data collection unit will reduce the frequency of data collection to alleviate the user's burden. For example, if the user is excited, the data collection unit will collect data in real time and reflect it immediately. This reduces the user's burden by adjusting the timing of data collection according to 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.

[0092] The data collection unit can analyze the user's past activity history and select the optimal data collection method. For example, the data collection unit may prioritize collecting content that the user has frequently accessed in the past. For example, if the data collection unit tends to be active during certain time periods, it may concentrate data collection during those times. For example, the data collection unit may analyze the user's past behavior patterns and suggest the optimal data collection method. This enables efficient data collection by selecting the optimal data collection method based on the user's past activity history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0093] The data collection unit can filter data based on the user's current musical preferences and areas of interest. For example, the data collection unit can collect relevant content based on the music genres the user has recently listened to. For example, the data collection unit can prioritize collecting new release information from artists the user follows. For example, the data collection unit can collect relevant news and articles based on the user's current areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current musical preferences and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0094] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting highly entertaining content. For example, if the user is stressed, the data collection unit will prioritize collecting relaxing music and content. For example, if the user is excited, the data collection unit will prioritize collecting active music and news. In this way, by determining the priority of data to collect according to the user's emotions, data appropriate to 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 data collection unit may be performed using AI or not using AI.

[0095] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of music event information in the area where the user is currently located. For example, the data collection unit can collect information on local artists based on the user's geographical location. For example, the data collection unit can collect region-specific music news based on the user's location information. By collecting highly relevant data while considering the user's geographical location information, the data collection unit can provide information tailored to the user. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0096] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect the latest information on artists that the user follows on social media. For example, the data collection unit can collect relevant content based on music that the user has shared on social media. For example, the data collection unit can collect music news that might be of interest to the user based on their social media activity. In this way, by analyzing the user's social media activity and collecting relevant data, it is possible to provide information that is relevant to the user. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0097] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is excited, the analysis unit provides analysis results using visually appealing graphics. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are appropriate 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 analysis unit may be performed using AI or not using AI.

[0098] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on high-importance data. For example, the analysis unit performs a concise analysis on low-importance data. The analysis unit adjusts the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0099] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms for each music genre. For example, the analysis unit can select an appropriate analysis algorithm based on the user's activity history. For example, the analysis unit can apply the optimal analysis method depending on the data category. By applying different analysis algorithms depending on the data category, the optimal analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is stressed, the analysis unit provides a concise analysis. For example, if the user is excited, the analysis unit provides the analysis results using visually appealing graphics. This allows for the provision of analysis results tailored to the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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.

[0101] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis of historical data according to its importance. The analysis unit may adjust the order of analysis based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0102] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0103] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. For example, if the user is relaxed, the optimization unit will prioritize optimizing highly entertaining content. For example, if the user is stressed, the optimization unit will prioritize optimizing relaxing content. For example, if the user is excited, the optimization unit will prioritize optimizing active content. In this way, by adjusting the optimization criteria according to the user's emotions, content suitable 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 optimization unit may be performed using AI or not using AI.

[0104] The optimization unit can improve the accuracy of optimization by considering the interrelationships of data during the optimization process. For example, the optimization unit combines relevant data to provide optimal content. For example, the optimization unit analyzes the interrelationships of data and selects the optimal display method. For example, the optimization unit improves the accuracy of optimization based on the interrelationships of data. As a result, by improving the accuracy of optimization by considering the interrelationships of data, content suitable for the user can be provided. Some or all of the above-described processes in the optimization unit may be performed using AI or not.

[0105] The optimization unit can perform optimization while considering the attribute information of the data submitter. For example, if the data submitter is an artist, the optimization unit will perform optimization while considering that attribute information. For example, if the data submitter is a fan, the optimization unit will perform optimization while considering that attribute information. For example, the optimization unit will improve the accuracy of optimization based on the attribute information of the data submitter. As a result, by performing optimization while considering the attribute information of the data submitter, content suitable for the user can be provided. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0106] The optimization unit can estimate the user's emotions and adjust the order in which the optimization results are displayed based on the estimated emotions. For example, if the user is relaxed, the optimization unit will prioritize displaying highly entertaining content. For example, if the user is stressed, the optimization unit will prioritize displaying relaxing content. For example, if the user is excited, the optimization unit will prioritize displaying active content. In this way, by adjusting the order in which the optimization results are displayed according to the user's emotions, content suitable 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 optimization unit may be performed using AI or not using AI.

[0107] The optimization unit can perform optimization while considering the geographical distribution of the data. For example, the optimization unit can prioritize displaying local content based on the user's geographical location. For example, the optimization unit can select the optimal display method while considering the geographical distribution of the data. For example, the optimization unit can improve the accuracy of optimization based on the geographical distribution of the data. As a result, by performing optimization while considering the geographical distribution of the data, content suitable for the user can be provided. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0108] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the data during the optimization process. For example, the optimization unit provides optimal content by referring to relevant literature on the data. For example, the optimization unit selects the optimal display method based on the relevant literature. For example, the optimization unit improves the accuracy of optimization based on the relevant literature. In this way, by improving the accuracy of optimization by referring to relevant literature on the data, content suitable for the user can be provided. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0109] The content delivery unit can estimate the user's emotions and adjust the content delivery method based on the estimated user emotions. For example, if the user is relaxed, the delivery unit will prioritize providing highly entertaining content. For example, if the user is stressed, the delivery unit will prioritize providing relaxing content. For example, if the user is excited, the delivery unit will prioritize providing active content. In this way, by adjusting the content delivery method according to the user's emotions, content suitable 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 delivery unit may be performed using AI or not using AI.

[0110] The content delivery unit can select the optimal delivery method by referring to the user's past browsing history when providing content. For example, the content delivery unit provides relevant content based on content the user has previously viewed. For example, the content delivery unit analyzes the user's past browsing history and selects the optimal delivery method. For example, the content delivery unit selects the optimal display method based on the user's past browsing history. In this way, by selecting the optimal delivery method by referring to the user's past browsing history, content suitable for the user can be provided. Some or all of the above processing in the content delivery unit may be performed using AI or not.

[0111] The content delivery unit can customize the content it provides based on the user's current areas of interest. For example, it can provide relevant content based on the music genre the user is currently interested in. For example, it can prioritize providing the latest information on artists the user follows. For example, it can provide relevant news and articles based on the user's current areas of interest. By customizing the content provided based on the user's current areas of interest, it is possible to provide content that is suitable for the user. Some or all of the above processing in the content delivery unit may be performed using AI or not.

[0112] The content delivery unit can estimate the user's emotions and adjust the frequency of content delivery based on the estimated emotions. For example, if the user is relaxed, the delivery unit may increase the frequency of content delivery. For example, if the user is stressed, the delivery unit may decrease the frequency of content delivery. For example, if the user is excited, the delivery unit may provide content in real time. In this way, by adjusting the frequency of content delivery according to the user's emotions, content suitable 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 delivery unit may be performed using AI or not using AI.

[0113] The content delivery unit can select the optimal delivery method by considering the user's device information when providing content. For example, if the user is using a smartphone, the delivery unit will provide content that matches the screen size. For example, if the user is using a tablet, the delivery unit will provide content optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit will provide concise and highly visible content. In this way, by selecting the optimal delivery method by considering the user's device information, content suitable for the user can be provided. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0114] The content delivery unit can analyze a user's social media activity and provide relevant content when delivering content. For example, the delivery unit can provide the latest information on artists the user follows on social media. For example, the delivery unit can provide relevant content based on music the user has shared on social media. For example, the delivery unit can provide music news that the user might be interested in based on their social media activity. In this way, by analyzing a user's social media activity and providing relevant content, the delivery unit can provide content that is tailored to the user. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0115] The support unit can estimate the user's emotions and adjust the support method based on the estimated emotions. For example, if the user is relaxed, the support unit can provide an entertaining support method. For example, if the user is stressed, the support unit can provide a relaxing support method. For example, if the user is excited, the support unit can provide an active support method. In this way, by adjusting the support method according to the user's emotions, it is possible to provide support that is appropriate 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 support unit may be performed using AI or not using AI.

[0116] The support department can select the optimal support method by referring to the artist's past activity history when providing support. For example, the support department may propose the optimal support method based on the artist's past successful live performances. For example, the support department may analyze the artist's past funding history and select the optimal support method. For example, the support department may propose the optimal support method based on the artist's past activity history. In this way, by selecting the optimal support method by referring to the artist's past activity history, it is possible to provide support that is appropriate for the artist. Some or all of the above processes in the support department may be performed using AI or not.

[0117] The support department can customize the means of support based on the artist's current activities when providing support. For example, the support department may propose the optimal support method based on the project the artist is currently working on. For example, the support department may analyze the artist's current activities and select the optimal means of support. For example, the support department may customize the means of support based on the artist's current activities. This allows the support department to provide support that is appropriate for the artist by customizing the means of support based on the artist's current activities. Some or all of the above processes in the support department may be performed using AI or not.

[0118] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is relaxed, the support unit will prioritize highly entertaining support. For example, if the user is stressed, the support unit will prioritize relaxing support. For example, if the user is excited, the support unit will prioritize active support. In this way, by determining the priority of support according to the user's emotions, it is possible to provide support that is appropriate 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 support unit may be performed using AI or not using AI.

[0119] The support department can select the most suitable support method when providing support, taking into account the artist's geographical location. For example, the support department may prioritize providing funding information for the area where the artist is currently located. For example, the support department may propose local support methods based on the artist's geographical location. For example, the support department may provide region-specific support methods based on the artist's location information. In this way, by selecting the most suitable support method considering the artist's geographical location, it is possible to provide support that is appropriate for the artist. Some or all of the above processing in the support department may be performed using AI or not.

[0120] The support department can analyze the artist's social media activity and propose support methods when providing assistance. For example, the support department can provide support information from fans that the artist follows on social media. For example, the support department can propose support methods based on projects that the artist has shared on social media. For example, the support department can propose the most suitable support method based on the artist's social media activity. In this way, by analyzing the artist's social media activity and proposing support methods, it is possible to provide support that is appropriate for the artist. Some or all of the above processing in the support department may be performed using AI or not.

[0121] The interaction unit can estimate the user's emotions and adjust the method of interaction based on the estimated emotions. For example, if the user is relaxed, the interaction unit can provide an entertaining method of interaction. For example, if the user is stressed, the interaction unit can provide a relaxing method of interaction. For example, if the user is excited, the interaction unit can provide an active method of interaction. In this way, by adjusting the method of interaction according to the user's emotions, it is possible to provide an interaction that is appropriate 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 interaction unit may be performed using AI or not using AI.

[0122] The interaction unit can select the optimal interaction method by referring to the user's past interaction history during interaction. For example, the interaction unit may suggest the optimal interaction method based on successful past interactions with the user. For example, the interaction unit may analyze the user's past interaction history and select the optimal interaction method. For example, the interaction unit may suggest the optimal interaction method based on the user's past interaction history. In this way, by selecting the optimal interaction method by referring to the user's past interaction history, it is possible to provide interactions that are suitable for the user. Some or all of the above processing in the interaction unit may be performed using AI, or it may be performed without using AI.

[0123] The interaction unit can customize the means of interaction based on the user's current areas of interest during interaction. For example, the interaction unit can provide relevant interaction methods based on the music genres the user is currently interested in. For example, the interaction unit can suggest interaction methods based on the latest information from artists the user follows. For example, the interaction unit can provide the optimal means of interaction based on the user's current areas of interest. In this way, by customizing the means of interaction based on the user's current areas of interest, it is possible to provide interactions that are suitable for the user. Some or all of the above processing in the interaction unit may be performed using AI or not.

[0124] The interaction unit can estimate the user's emotions and determine the priority of interactions based on the estimated emotions. For example, if the user is relaxed, the interaction unit will prioritize entertaining interactions. If the user is stressed, the interaction unit will prioritize relaxing interactions. If the user is excited, the interaction unit will prioritize active interactions. This allows the system to provide interactions that are appropriate for the user by determining the priority of interactions according to 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 processing described above in the interaction unit may be performed using AI or not.

[0125] The interaction unit can select the optimal interaction method by considering the user's geographical location information during interaction. For example, the interaction unit may prioritize providing music event information in the user's current location. For example, the interaction unit may suggest local interaction methods based on the user's geographical location. For example, the interaction unit may provide region-specific interaction methods based on the user's location information. In this way, by selecting the optimal interaction method by considering the user's geographical location information, it is possible to provide interactions that are suitable for the user. Some or all of the above processing in the interaction unit may be performed using AI or not.

[0126] The interaction unit can analyze a user's social media activity during interaction and suggest ways to interact. For example, the interaction unit can provide the latest information on artists the user follows on social media. For example, the interaction unit can provide relevant interaction methods based on music the user has shared on social media. For example, the interaction unit can suggest the most suitable way to interact based on the user's social media activity. In this way, by analyzing the user's social media activity and suggesting ways to interact, it can provide interactions that are appropriate for the user. Some or all of the above processing in the interaction unit may be performed using AI or not.

[0127] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is relaxed, the notification unit can provide an entertaining notification method. For example, if the user is stressed, the notification unit can provide a relaxing notification method. For example, if the user is excited, the notification unit can provide an active notification method. In this way, by adjusting the notification method according to the user's emotions, it is possible to provide notifications that are appropriate 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 notification unit may be performed using AI or not using AI.

[0128] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may suggest the optimal notification method based on successful examples of notifications the user has received in the past. For example, the notification unit may analyze the user's past notification history and select the optimal notification method. For example, the notification unit may suggest the optimal notification method based on the user's past notification history. In this way, by selecting the optimal notification method by referring to the user's past notification history, it is possible to provide notifications that are appropriate for the user. Some or all of the above processing in the notification unit may be performed using AI or not.

[0129] The notification unit can customize the content of notifications based on the user's current areas of interest. For example, the notification unit can provide relevant notifications based on the music genres the user is currently interested in. For example, the notification unit can prioritize notifications about the latest information from artists the user follows. For example, the notification unit can notify the user of relevant news and articles based on the user's current areas of interest. In this way, by customizing the content of notifications based on the user's current areas of interest, it is possible to provide notifications that are appropriate for the user. Some or all of the above processing in the notification unit may be performed using AI or not.

[0130] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit will prioritize entertaining notifications. For example, if the user is stressed, the notification unit will prioritize relaxing notifications. For example, if the user is excited, the notification unit will prioritize active notifications. In this way, by determining the priority of notifications according to the user's emotions, it is possible to provide notifications that are appropriate 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 notification unit may be performed using AI or not using AI.

[0131] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit provides a notification method that matches the screen size. For example, if the user is using a tablet, the notification unit provides a notification method optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit provides a concise and highly visible notification method. By selecting the optimal notification method considering the user's device information, the notification unit can provide notifications that are appropriate for the user. Some or all of the above processing in the notification unit may be performed using AI, or it may be performed without using AI.

[0132] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, the notification unit may notify the user of the latest information about artists the user follows on social media. For example, the notification unit may provide relevant notifications based on music the user has shared on social media. For example, the notification unit may notify the user of music news that might be of interest to the user based on their social media activity. In this way, by analyzing the user's social media activity and providing relevant notifications, the notification unit can provide notifications that are tailored to the user. Some or all of the above processing in the notification unit may be performed using AI or not.

[0133] The detection unit can estimate the user's emotions and adjust the detection criteria for inappropriate content based on the estimated user emotions. For example, if the user is relaxed, the detection unit will prioritize detecting highly entertaining content. For example, if the user is stressed, the detection unit will prioritize detecting relaxing content. For example, if the user is excited, the detection unit will prioritize detecting active content. In this way, by adjusting the detection criteria for inappropriate content according to the user's emotions, content suitable 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 detection unit may be performed using AI or not using AI.

[0134] The detection unit can optimize the detection algorithm by referring to past detection history during detection. For example, the detection unit selects the optimal detection algorithm based on past detection history. For example, the detection unit analyzes the detection history to improve the accuracy of the detection algorithm. For example, the detection unit proposes the optimal detection method based on past detection history. In this way, the accuracy of detection is improved by optimizing the detection algorithm by referring to past detection history. Some or all of the above processes in the detection unit may be performed using AI or not.

[0135] The detection unit can apply different detection algorithms depending on the content category during detection. For example, the detection unit can apply different detection algorithms for each music genre. For example, the detection unit can select an appropriate detection algorithm based on the content category. For example, the detection unit can apply the optimal detection method depending on the content category. This improves the accuracy of detection by applying different detection algorithms depending on the content category. Some or all of the above processing in the detection unit may be performed using AI or not.

[0136] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is relaxed, the detection unit provides an entertaining display method. For example, if the user is stressed, the detection unit provides a relaxing display method. For example, if the user is excited, the detection unit provides an active display method. In this way, by adjusting the display method of the detection results according to the user's emotions, a display method suitable for the user can be provided. 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 such examples. Some or all of the above processing in the detection unit may be performed using AI or not using AI.

[0137] The detection unit can perform detection while considering the geographical distribution of content. For example, the detection unit prioritizes the detection of local content based on the user's geographical location. For example, the detection unit selects the optimal detection method while considering the geographical distribution of content. For example, the detection unit improves the accuracy of detection based on the geographical distribution of content. As a result, by performing detection while considering the geographical distribution of content, it is possible to provide content that is appropriate for the user. Some or all of the above processing in the detection unit may be performed using AI or not.

[0138] The detection unit can improve detection accuracy by referring to related literature for the content during detection. For example, the detection unit provides an optimal detection method by referring to related literature for the content. For example, the detection unit selects an optimal detection algorithm based on the related literature. For example, the detection unit improves detection accuracy based on the related literature. In this way, by improving detection accuracy by referring to related literature for the content, content suitable for the user can be provided. Some or all of the above processing in the detection unit may be performed using AI or not using AI.

[0139] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0140] The data collection unit can collect not only the user's musical preferences but also their lifestyle and daily activity patterns. For example, it can collect data on the music the user listens to while exercising or relaxing. This allows for music suggestions tailored to the user's lifestyle. The data collection unit can also collect the user's device usage and provide content optimized for that device. For example, it can provide short video clips when using a smartphone and longer live videos when using a tablet. Furthermore, the data collection unit can collect the user's social media activity and suggest relevant content based on the music the user shares and the artists they follow.

[0141] The support department can assist artists with crowdfunding, merchandise sales, live streaming, and promotional activities. For example, it can help with creating social media ads and press releases when an artist releases a new song. The support department can also help plan and manage events for artists to interact with their fans. For example, it can help with online meet-and-greets and fan-only live streams. Furthermore, the support department can help increase an artist's media exposure by supporting the publication of interviews and feature articles. In this way, it can provide comprehensive support for an artist's promotional activities.

[0142] The community section can offer music-related games and quizzes to facilitate interaction among users with shared musical tastes. For example, it can provide music trivia quizzes and games that test knowledge about artists. The community section can also provide a function for users to share and exchange their music playlists with other users, allowing them to share their musical tastes. Furthermore, the community section can provide a function for users to post their music reviews and opinions and discuss them with other users, thereby facilitating active exchange of opinions about music.

[0143] The notification unit notifies users only of information they are interested in at the right time, and can also adjust notification timing based on the user's schedule. For example, it can link with the user's calendar app to refrain from sending notifications before and after important meetings or events. Furthermore, the notification unit can analyze the user's notification history, learn patterns of notifications that have received positive responses in the past, and select the optimal notification method. In addition, the notification unit can consider the user's device's battery level and refrain from sending notifications when the battery is low. This enables flexible notifications tailored to the user's situation.

[0144] In addition to detecting and eliminating inappropriate content and spam, the detection unit can improve its detection algorithm based on user feedback. For example, it can provide a function for users to report content they deem inappropriate, and adjust the detection algorithm based on that feedback. Furthermore, the detection unit can perform appropriate content filtering based on the user's age and region. For example, it can prevent adult content from being displayed to underage users. In addition, the detection unit can filter out irrelevant content based on the user's interests. This ensures that only content beneficial to the user is displayed.

[0145] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is relaxed, data collection is performed frequently to reflect their current interests. If the user is stressed, the frequency of data collection is reduced to lessen the user's burden. If the user is excited, data is collected in real time and reflected immediately. In this way, the user's burden can be reduced by adjusting the timing of data collection according to their emotions.

[0146] The data collection unit can analyze a user's past activity history and select the optimal data collection method. For example, it can prioritize collecting content that a user has frequently accessed in the past. If a user tends to be active during specific time periods, it can concentrate data collection during those times. By analyzing the user's past behavior patterns, it can propose the optimal data collection method. This enables efficient data collection by selecting the most suitable method based on the user's past activity history.

[0147] The data collection unit can filter data based on the user's current musical preferences and areas of interest. For example, it can collect relevant content based on the music genres the user has recently listened to, prioritize collecting new release information from artists the user follows, and collect relevant news and articles based on the user's current areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current musical preferences and areas of interest.

[0148] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is relaxed, it will prioritize collecting highly entertaining content. If the user is stressed, it will prioritize collecting relaxing music and content. If the user is excited, it will prioritize collecting active music and news. By prioritizing the data to collect according to the user's emotions, it can provide data that is relevant to the user.

[0149] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, it can prioritize the collection of music event information in the user's current location. It can also collect information on local artists based on the user's geographical location. Furthermore, it can collect region-specific music news based on the user's location. By collecting highly relevant data while considering the user's geographical location, it can provide information tailored to the user.

[0150] The following briefly describes the processing flow for example form 2.

[0151] Step 1: The data collection unit collects the user's preferences and activity history. User preferences include, but are not limited to, music genres and artist preferences. The data collection unit can also collect the user's past viewing and search history. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department uses AI to analyze user preferences and activity history to identify user interests. Step 3: The optimization unit optimizes the content based on the analysis results obtained by the analysis unit. The optimization unit uses AI to select content based on user interests and adjusts the display order. Step 4: The delivery unit provides content optimized by the optimization unit. The delivery unit displays content selected based on the user's interests.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, support unit, interaction unit, notification unit, and detection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects the user's preferences and activity history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the content based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the optimized content. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports artist funding, merchandise sales, and live streaming. The interaction unit is implemented by the control unit 46A of the smart device 14 and promotes interaction between users. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of information of interest at an appropriate time. The detection unit is implemented by the specific processing unit 290 of the data processing device 12, which detects and removes inappropriate content and spam. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0156] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, support unit, interaction unit, notification unit, and detection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects the user's preferences and activity history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The optimization unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes the content based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the optimized content. The support unit is implemented by the identification processing unit 290 of the data processing unit 12 and supports artist funding, merchandise sales, and live streaming. The interaction unit is implemented by the control unit 46A of the smart glasses 214 and promotes interaction between users. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of information of interest at the appropriate time. The detection unit is implemented by the specific processing unit 290 of the data processing device 12, which detects and removes inappropriate content and spam. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0172] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, support unit, interaction unit, notification unit, and detection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects the user's preferences and activity history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the content based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the optimized content. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports artist funding, merchandise sales, and live streaming. The interaction unit is implemented by the control unit 46A of the headset terminal 314 and facilitates interaction between users. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of information of interest at an appropriate time. The detection unit is implemented by the specific processing unit 290 of the data processing device 12, which detects and removes inappropriate content and spam. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0188] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, support unit, interaction unit, notification unit, and detection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects user preferences and activity history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes content based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides optimized content. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports artist funding, merchandise sales, and live streaming. The interaction unit is implemented by the control unit 46A of the robot 414 and facilitates interaction between users. The notification unit is implemented by the control unit 46A of the robot 414 and notifies users of information of interest at the appropriate time. The detection unit is implemented by the specific processing unit 290 of the data processing device 12, which detects and removes inappropriate content and spam. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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."

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] (Note 1) A data collection unit that collects user preferences and activity history, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit that optimizes content based on the analysis results obtained by the aforementioned analysis unit, The system includes a providing unit that provides content optimized by the optimization unit. A system characterized by the following features. (Note 2) It has a support department that assists artists with crowdfunding, merchandise sales, and live streaming. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a community section to facilitate interaction among users who share a common interest in music. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a notification unit that notifies users of only the information they are interested in, at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a detection unit that detects and removes inappropriate content and spam. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past activity history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current musical preferences and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, It estimates user sentiment and adjusts optimization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, During optimization, consider the interrelationships between data to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates the user's emotions and adjusts the order in which the optimization results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature on the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate user sentiment and adjust how content is delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, the system selects the optimal delivery method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing content, customize the content offered based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates user sentiment and adjusts the frequency of content delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing content, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, we analyze users' social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned support unit, When providing support, we will refer to the artist's past activity history to select the most suitable support method. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned support unit, When providing support, customize the support methods based on the artist's current activity status. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned support unit, When providing support, we select the most suitable support method by taking into account the artist's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned support unit, When providing support, we analyze the artist's social media activity and propose ways to support them. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned AC unit is It estimates the user's emotions and adjusts the method of interaction based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned AC unit is During interaction, the system selects the most suitable interaction method by referring to the user's past interaction history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned AC unit is During interactions, the means of communication are customized based on the user's current areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned AC unit is It estimates the user's emotions and determines the priority of interactions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned AC unit is During interaction, the system selects the optimal interaction method by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned AC unit is During interaction, we analyze the user's social media activity and suggest ways to interact. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system according to appendix 4, characterized in that (Appendix 43) The notification unit selects an optimal notification method by referring to the user's past notification history at the time of notification The system according to appendix 4, characterized in that (Appendix 44) The notification unit customizes the content of the notification based on the user's current area of interest at the time of notification The system according to appendix 4, characterized in that (Appendix 45) The notification unit estimates the user's emotion and determines the priority of the notification based on the estimated user's emotion The system according to appendix 4, characterized in that (Appendix 46) The notification unit selects an optimal notification method by considering the user's device information at the time of notification The system according to appendix 4, characterized in that (Appendix 47) The notification unit analyzes the user's social media activities at the time of notification and provides relevant notifications The system according to appendix 4, characterized in that (Appendix 48) The detection unit estimates the user's emotion and adjusts the detection criteria for inappropriate content based on the estimated user's emotion <第5の付記に記載のシステム。The system according to appendix 5, characterized in that (Appendix 49) The detection unit optimizes the detection algorithm by referring to the past detection history at the time of detection The system according to appendix 5, characterized in that (Appendix 50) The detection unit applies different detection algorithms according to the category of the content at the time of detection The system according to appendix 5, characterized in that (Note 51) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 52) The detection unit is During detection, the geographical distribution of the content is taken into consideration. The system described in Appendix 5, characterized by the features described herein. (Note 53) The detection unit is During detection, we refer to related literature for the content to improve detection accuracy. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0224] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user preferences and activity history, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit that optimizes content based on the analysis results obtained by the aforementioned analysis unit, The system includes a providing unit that provides content optimized by the optimization unit. A system characterized by the following features.

2. It has a support department that assists artists with crowdfunding, merchandise sales, and live streaming. The system according to feature 1.

3. It features a community section to facilitate interaction among users who share a common interest in music. The system according to feature 1.

4. It features a notification unit that notifies users of only the information they are interested in, at the appropriate time. The system according to feature 1.

5. It is equipped with a detection unit that detects and removes inappropriate content and spam. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past activity history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current musical preferences and areas of interest. The system according to feature 1.

Images