system

Abstract

The system according to this embodiment aims to suggest optimal entertainment based on the user's preferences and browsing history. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a suggestion unit, a provision unit, and a customization unit. The reception unit inputs the user's preferences and browsing history. The analysis unit analyzes the information input by the reception unit. The suggestion unit suggests entertainment based on the information analyzed by the analysis unit. The provision unit provides detailed information about the entertainment suggested by the suggestion unit. The customization unit provides a user-level customized experience based on the information provided by the provision 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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose optimal entertainment based on the user's preferences and browsing history, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal entertainment based on the user's preferences and browsing history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a provision unit, and a customization unit. The reception unit receives user preferences and browsing history. The analysis unit analyzes the information entered by the reception unit. The suggestion unit suggests entertainment based on the information analyzed by the analysis unit. The provision unit provides detailed information about the entertainment suggested by the suggestion unit. The customization unit provides a user-level customized experience based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal entertainment based on the user's preferences and browsing history. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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) An entertainment provision system according to an embodiment of the present invention is a system in which AI provides optimal entertainment based on the user's preferences and browsing history. This entertainment provision system takes the user's preferences and past browsing history as input, analyzes the input information, and proposes the most suitable entertainment to the user. Furthermore, the generating AI also provides detailed information related to the works. This allows the user to deepen their understanding of the works and gain a more fulfilling enjoyment. In addition, the AI ​​director constantly proposes the most suitable entertainment, providing a customized experience at the user level. This allows the user to enjoy fresh experiences and expand their entertainment options. For example, by being offered works in genres the user doesn't usually watch, they can experience new discoveries and surprises. Thus, the entertainment provision system can provide optimal entertainment and a customized experience based on the user's preferences and browsing history.

[0029] The entertainment provision system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a provision unit, and a customization unit. The reception unit inputs the user's preferences and browsing history. The reception unit can input information such as content the user has previously viewed, the viewing time, and the frequency of viewing. The reception unit can be configured in a way that the user manually inputs the information or automatically acquires browser history data. The analysis unit analyzes the information input by the reception unit. The analysis unit can extract the user's preferences using, for example, data mining technology. The analysis unit can also analyze the user's viewing patterns using statistical analysis or machine learning algorithms. The suggestion unit suggests entertainment based on the information analyzed by the analysis unit. The suggestion unit can suggest the most suitable content for the user using, for example, a recommendation system. The suggestion unit can also provide personalized suggestions. The provision unit provides detailed information about the suggested entertainment using a generative AI. The provision unit provides, for example, a description of the content, related information, and user reviews. The generative AI generates detailed information using a text generation AI (e.g., LLM). The customization unit provides a user-level customized experience based on information provided by the service provider. For example, the customization unit provides interfaces and personalized content tailored to the user's preferences. This allows the entertainment delivery system to provide optimal entertainment and a customized experience based on the user's preferences and browsing history.

[0030] The reception desk inputs user preferences and browsing history. Specifically, it can input information such as content the user has watched in the past, the duration of viewing, and the frequency of viewing. For example, it collects detailed data such as the titles of movies, dramas, and music videos the user has watched, the date and time of viewing, the length of viewing, and the frequency of viewing. This data can be entered manually by the user or automatically retrieved from browser history data. In the case of manual entry, the user can enter their viewing history into a form or select the content they have watched using checkboxes. In the case of automatic retrieval, the system automatically collects the user's viewing history using browser history data or streaming service APIs. Furthermore, the reception desk can also provide surveys and quizzes to understand the user's preferences. For example, by asking the user about their favorite genres, actors, directors, and music artists, it can collect more detailed preference information. This allows the reception desk to comprehensively collect user viewing history and preferences and prepare data to provide to the analytics department.

[0031] The analysis unit analyzes the information entered by the reception unit. Specifically, it extracts user preferences using data mining techniques. Data mining techniques include clustering and association rule mining. For example, it uses clustering to group users with similar viewing patterns, and association rule mining to analyze what other content users who have viewed specific content have viewed. The analysis unit can also analyze user viewing patterns using statistical analysis and machine learning algorithms. For example, it uses regression analysis to model the relationship between user viewing time, frequency, and preferences, and uses machine learning algorithms to predict future viewing trends from user viewing history. Furthermore, the analysis unit can use natural language processing techniques to analyze text data entered by users (e.g., reviews and comments) and extract user sentiment and opinions. This allows the analysis unit to analyze user viewing history and preferences in detail and generate information to provide to the suggestion unit.

[0032] The suggestion department proposes entertainment based on information analyzed by the analysis department. Specifically, it uses a recommendation system to suggest the most suitable content for the user. The recommendation system includes collaborative filtering and content-based filtering. Collaborative filtering is a method of recommending content based on the viewing history of other users with similar preferences, while content-based filtering is a method of recommending new content based on the characteristics of content the user has previously viewed. For example, using collaborative filtering, new content can be recommended to user A based on the viewing history of user B, who has viewed content similar to what user A has viewed. Also, using content-based filtering, new movies with similar characteristics can be recommended based on the genre, actors, directors, etc., of movies the user has previously viewed. Furthermore, the suggestion department can also provide personalized suggestions. For example, it can suggest the most suitable content for a specific time of day or day of the week based on the user's viewing history and preferences. In this way, the suggestion department can propose the most suitable entertainment for the user and improve user satisfaction.

[0033] The service provider uses generative AI to provide detailed information about the suggested entertainment. Specifically, it uses text generation AI (e.g., LLM) to generate detailed information. The generative AI generates descriptions, related information, and user reviews about the suggested content. For example, as a movie description, it provides information such as the synopsis, main cast, and director, and as related information, it provides other movies in the same genre and related news articles. As user reviews, it summarizes reviews posted by other users and provides ratings and comments. The generative AI uses natural language processing technology to generate text that is easy for users to understand and interesting. Furthermore, the service provider has an interface to provide the generated information to users. For example, it allows users to view detailed information about the suggested content through a website or mobile app. This allows the service provider to provide users with detailed information about the suggested entertainment, attract their interest, and encourage viewing.

[0034] The customization department provides a user-level customized experience based on information provided by the service provider. Specifically, it provides interfaces and personalized content tailored to the user's preferences. For example, it customizes the home screen and recommendation lists based on the user's favorite genres, actors, and directors. It can also personalize notifications and reminders based on the user's viewing history and preferences. For example, it sends notifications to the user when a new episode is released. Furthermore, the customization department can collect user feedback and continuously improve the accuracy and effectiveness of customization. For example, by having users leave ratings and comments on the provided content, the system can more accurately understand the user's preferences and reflect them in future suggestions and customizations. In addition, the customization department provides the optimal display method according to the user's device and environment. For example, it provides interfaces that support different devices such as smartphones, tablets, and televisions, so that users can comfortably enjoy entertainment on any device. In this way, the customization department can provide an individually optimized entertainment experience for each user and improve user satisfaction.

[0035] The reception desk can input user preferences and past browsing history. For example, the reception desk can input information such as content the user has previously viewed, the duration of viewing, and the frequency of viewing. The reception desk can accept manual input from the user or automatically retrieve browser history data. This allows for more accurate entertainment recommendations by inputting user preferences and past browsing history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user preferences and past browsing history into an AI, which can then automatically analyze the data.

[0036] The analysis unit can analyze the information entered by the reception unit. For example, the analysis unit can extract user preferences using data mining techniques. The analysis unit can also analyze user viewing patterns using statistical analysis and machine learning algorithms. This allows the system to suggest the most suitable entertainment to the user by analyzing the entered information. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the user's viewing history data into the AI, which then analyzes the data to extract the user's preferences.

[0037] The suggestion unit can suggest entertainment based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest the most suitable content to the user using a recommendation system. The suggestion unit can also provide personalized suggestions. This allows the user to receive the most suitable entertainment by suggesting entertainment based on the analyzed information. Some or all of the above processes in the suggestion unit are performed using AI. For example, the suggestion unit inputs the information analyzed by the analysis unit into the AI, and the AI ​​suggests the most suitable entertainment to the user.

[0038] The service provider can provide detailed information about entertainment using generative AI. For example, the service provider can provide content descriptions, related information, and user reviews. The generative AI generates detailed information using text generation AI (e.g., LLM). This allows the service provider to provide detailed information about entertainment and deepen user understanding. Some or all of the above-described processes in the service provider are performed using the generative AI. For example, the service provider inputs information about entertainment into the generative AI, which then generates and provides detailed information.

[0039] The customization unit can provide a user-level customized experience based on the information provided by the service provider. For example, the customization unit can provide an interface tailored to the user's preferences and personalized content. This allows the user to receive a fresh entertainment experience by providing a customized experience based on the provided information. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit inputs the information provided by the service provider into the AI, and the AI ​​performs the optimal customization for the user.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display the user's frequently used preferences and browsing history as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest preferences and browsing history used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into the AI, and the AI ​​will select the optimal input method.

[0041] The reception desk can filter user preferences and browsing history based on their current interests. For example, it can prioritize displaying relevant preferences and browsing history based on genres the user is currently interested in. It can also suggest relevant preferences and browsing history based on content the user has recently viewed. Furthermore, if the reception desk is interested in a particular event or trend, it can filter based on that information. This allows users to input more relevant information by filtering based on their current interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current interests and data into an AI, which then performs the filtering.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input their preferences and browsing history. For example, the reception desk can prioritize inputting popular content in the user's current location. It can also suggest relevant content based on places the user has visited in the past. Furthermore, if the user is traveling, the reception desk can input relevant content based on information about their travel destination. This allows for the input of more relevant information by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which then prioritizes inputting highly relevant information.

[0043] The reception desk can analyze the user's social media activity and input relevant information when they input their preferences and browsing history. For example, the reception desk can input relevant preferences and browsing history based on the content the user has shared on social media. It can also suggest relevant content based on the activity of accounts the user follows. Furthermore, the reception desk can input relevant information based on trends in online communities the user participates in. This allows for the input of more relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's social media activity data into the AI, and the AI ​​inputs relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. Conversely, it can perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. By adjusting the level of detail of the analysis based on the importance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs information importance data into the AI, and the AI ​​adjusts the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for information about movies, the analysis unit applies a movie-specific analysis algorithm. Similarly, for information about music, it can apply a music-specific analysis algorithm. Furthermore, for information about games, it can apply a game-specific analysis algorithm. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit inputs information category data into the AI, and the AI ​​applies different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit will prioritize the analysis of the most recent information. It can also lower the priority of older information. Furthermore, the analysis unit can adjust the analysis schedule according to the timing of information submission. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the timing of information submission. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs information submission timing data into the AI, and the AI ​​determines the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the relevance data of the information into the AI, and the AI ​​adjusts the order of analysis.

[0048] The proposal department can adjust the level of detail in proposals based on the importance of the entertainment. For example, it can provide detailed proposals for high-importance entertainment, and concise proposals for low-importance entertainment. Furthermore, the proposal department can prioritize proposals according to the importance of the entertainment. This allows for the provision of more appropriate proposals by adjusting the level of detail based on the importance of the entertainment. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs entertainment importance data into the AI, and the AI ​​adjusts the level of detail in the proposals.

[0049] The suggestion unit can apply different suggestion algorithms depending on the entertainment category when making suggestions. For example, for suggestions related to movies, the suggestion unit applies a suggestion algorithm specifically for movies. Similarly, for suggestions related to music, it can apply a suggestion algorithm specifically for music. Furthermore, for suggestions related to games, it can apply a suggestion algorithm specifically for games. This allows for the provision of more appropriate suggestions by applying different suggestion algorithms depending on the entertainment category. Some or all of the above processing in the suggestion unit is performed using AI. For example, the suggestion unit inputs entertainment category data into the AI, and the AI ​​applies different suggestion algorithms.

[0050] The proposal department can prioritize proposals based on the submission timing of the entertainment. For example, the department will prioritize proposals for the latest entertainment. It can also lower the priority of proposals for older entertainment. Furthermore, the proposal department can adjust the proposal schedule according to the submission timing of the entertainment. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing of the entertainment. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs entertainment submission timing data into the AI, and the AI ​​determines the priority of the proposals.

[0051] The proposal department can adjust the order of proposals based on the relevance of the entertainment. For example, it will prioritize proposals for highly relevant entertainment. It can also postpone proposals for less relevant entertainment. Furthermore, the proposal department can adjust the proposal schedule according to the relevance of the entertainment. This allows for the provision of more appropriate proposals by adjusting the order of proposals based on the relevance of the entertainment. Some or all of the above processing in the proposal department is performed using AI. For example, the proposal department inputs entertainment relevance data into the AI, and the AI ​​adjusts the order of proposals.

[0052] The information provider can adjust the level of detail provided based on the importance of the entertainment when providing detailed information. For example, the provider can provide detailed information for high-importance entertainment, and concise information for low-importance entertainment. Furthermore, the provider can determine the priority of information provision according to the importance of the entertainment. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the entertainment. Some or all of the above processing in the information provider is performed using a generative AI. For example, the provider inputs entertainment importance data into the generative AI, and the generative AI adjusts the level of detail provided.

[0053] The information delivery unit can apply different delivery algorithms depending on the entertainment category when providing detailed information. For example, for information about movies, the unit can apply a delivery algorithm specifically for movies. Similarly, for information about music, it can apply a delivery algorithm specifically for music. Furthermore, for information about games, it can apply a delivery algorithm specifically for games. This allows for the provision of more appropriate information by applying different delivery algorithms depending on the entertainment category. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the information delivery unit inputs entertainment category data into the generative AI, which then applies different delivery algorithms.

[0054] The information provider can prioritize the provision of detailed information based on the submission date of the entertainment. For example, the provider will prioritize providing information on the latest entertainment. The provider can also lower the priority of providing information on older entertainment. Furthermore, the provider can adjust the information provision schedule according to the submission date of the entertainment. This allows for the provision of more appropriate information by prioritizing the provision based on the submission date of the entertainment. Some or all of the above processing in the information provider is performed using a generative AI. For example, the provider inputs entertainment submission date data into the generative AI, and the generative AI determines the priority of provision.

[0055] The information delivery unit can adjust the order of information delivery based on the relevance of the entertainment when providing detailed information. For example, the unit will prioritize providing information on highly relevant entertainment. It can also postpone the delivery of information on less relevant entertainment. Furthermore, the unit can adjust the information delivery schedule according to the relevance of the entertainment. This allows for the provision of more appropriate information by adjusting the order of delivery based on the relevance of the entertainment. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the information delivery unit inputs entertainment relevance data into the generative AI, and the generative AI adjusts the order of delivery.

[0056] The customization unit can analyze the user's past behavior history to select the optimal customization method during the customization process. For example, the customization unit can propose the optimal customization method based on the customization options the user has previously selected. Furthermore, the customization unit can predict and propose customization options to be used during specific time periods based on the user's past behavior history. In addition, the customization unit can analyze the user's past behavior history and propose the most efficient customization method. This allows the customization unit to provide the user with the most suitable customization method by analyzing past behavior history. Some or all of the above processes in the customization unit may be performed using AI, or not. For example, the customization unit can input the user's past behavior history data into the AI, which then selects the optimal customization method.

[0057] The customization unit can customize the means of customization based on the user's current interests and preferences during the customization process. For example, the customization unit can provide relevant customization options based on the genres the user is currently interested in. It can also suggest relevant customization options based on the content the user has recently viewed. Furthermore, if the user is interested in a particular event or trend, the customization unit can provide customization options based on that information. This allows for more appropriate customization by providing customization options based on current interests and preferences. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's current interests and preferences data into the AI, and the AI ​​can select the means of customization.

[0058] The customization unit can select the optimal customization method by considering the user's geographical location during the customization process. For example, the customization unit may prioritize providing popular customization options in the user's current location. It can also suggest relevant customization options based on places the user has visited in the past. Furthermore, if the user is traveling, the customization unit can provide relevant customization options based on information about their travel destination. This allows for more appropriate customization by considering geographical location. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit may input the user's geographical location information into the AI, which then selects the optimal customization method.

[0059] The customization unit can analyze the user's social media activity during the customization process and propose customization options. For example, the customization unit can provide relevant customization options based on the content the user has shared on social media. It can also propose relevant customization options based on the activity of accounts the user follows. Furthermore, the customization unit can provide relevant customization options based on the trends of online communities the user participates in. This allows for more appropriate customization by analyzing social media activity. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit inputs the user's social media activity data into the AI, and the AI ​​selects the customization options.

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

[0061] The entertainment delivery system can also acquire user health data and suggest entertainment based on this data. For example, it can acquire the user's heart rate and sleep data and suggest relaxing content if relaxation is needed. It can also suggest energetic music or videos if the user is exercising. Furthermore, it can analyze the user's health data over the long term and provide entertainment tailored to changes in their health condition. This allows for the provision of optimal entertainment based on the user's health status.

[0062] The entertainment delivery system can further acquire users' social network data and make entertainment suggestions based on it. For example, it can suggest content that the user's friends are watching, providing common topics of conversation. It can also suggest relevant content based on trends in the online communities the user participates in. Furthermore, it can analyze the user's activity on social networks and suggest new content that the user might be interested in. This allows the system to provide optimal entertainment tailored to the user's social network.

[0063] The entertainment delivery system can also acquire the user's geographical location information and suggest entertainment based on it. For example, if the user is traveling, it can suggest tourist information and local entertainment in their destination. If the user is attending a specific event, it can also suggest content related to that event. Furthermore, it can provide information on nearby movie theaters and concerts based on the user's current location. This allows for the provision of optimal entertainment tailored to the user's geographical location.

[0064] The entertainment delivery system can also acquire the user's past purchase history and make entertainment suggestions based on it. For example, it can suggest new content related to movies and music the user has purchased in the past. It can also analyze reviews of products the user has purchased and suggest content that the user might be interested in. Furthermore, it can analyze the user's purchase history over the long term and provide entertainment that responds to changes in the user's preferences. This allows for the provision of optimal entertainment based on the user's purchase history.

[0065] The entertainment delivery system can also acquire information about the user's device usage and suggest entertainment based on that information. For example, if the user is using a smartphone, it can suggest content that can be enjoyed in a short amount of time. If the user is using a tablet, it can suggest longer content. Furthermore, it can analyze the user's device usage and provide entertainment that is best suited to the device the user uses most often. This allows for the provision of optimal entertainment based on the user's device usage.

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

[0067] Step 1: The reception desk inputs the user's preferences and browsing history. For example, it can input information such as content the user has watched in the past, the duration of viewing, and the frequency of viewing. The reception desk can accept manual input from the user or automatically retrieve browser history data. Step 2: The analysis unit analyzes the information entered by the reception unit. For example, it may use data mining techniques to extract user preferences. It can also analyze user viewing patterns using statistical analysis and machine learning algorithms. Step 3: The proposal unit proposes entertainment based on the information analyzed by the analysis unit. For example, it uses a recommendation system to suggest the most suitable content for the user. It can also provide personalized suggestions. Step 4: The providing department provides detailed information about the entertainment proposed by the proposing department. For example, it provides content descriptions, related information, and user reviews using a generative AI. The generative AI generates detailed information using a text generation AI (e.g., LLM). Step 5: The customization team provides a user-level customized experience based on the information provided by the service team. For example, they provide an interface and personalized content tailored to the user's preferences.

[0068] (Example of form 2) An entertainment provision system according to an embodiment of the present invention is a system in which AI provides optimal entertainment based on the user's preferences and browsing history. This entertainment provision system takes the user's preferences and past browsing history as input, analyzes the input information, and proposes the most suitable entertainment to the user. Furthermore, the generating AI also provides detailed information related to the works. This allows the user to deepen their understanding of the works and gain a more fulfilling enjoyment. In addition, the AI ​​director constantly proposes the most suitable entertainment, providing a customized experience at the user level. This allows the user to enjoy fresh experiences and expand their entertainment options. For example, by being offered works in genres the user doesn't usually watch, they can experience new discoveries and surprises. Thus, the entertainment provision system can provide optimal entertainment and a customized experience based on the user's preferences and browsing history.

[0069] The entertainment provision system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a provision unit, and a customization unit. The reception unit inputs the user's preferences and browsing history. The reception unit can input information such as content the user has previously viewed, the viewing time, and the frequency of viewing. The reception unit can be configured in a way that the user manually inputs the information or automatically acquires browser history data. The analysis unit analyzes the information input by the reception unit. The analysis unit can extract the user's preferences using, for example, data mining technology. The analysis unit can also analyze the user's viewing patterns using statistical analysis or machine learning algorithms. The suggestion unit suggests entertainment based on the information analyzed by the analysis unit. The suggestion unit can suggest the most suitable content for the user using, for example, a recommendation system. The suggestion unit can also provide personalized suggestions. The provision unit provides detailed information about the suggested entertainment using a generative AI. The provision unit provides, for example, a description of the content, related information, and user reviews. The generative AI generates detailed information using a text generation AI (e.g., LLM). The customization unit provides a user-level customized experience based on information provided by the service provider. For example, the customization unit provides interfaces and personalized content tailored to the user's preferences. This allows the entertainment delivery system to provide optimal entertainment and a customized experience based on the user's preferences and browsing history.

[0070] The reception desk inputs user preferences and browsing history. Specifically, it can input information such as content the user has watched in the past, the duration of viewing, and the frequency of viewing. For example, it collects detailed data such as the titles of movies, dramas, and music videos the user has watched, the date and time of viewing, the length of viewing, and the frequency of viewing. This data can be entered manually by the user or automatically retrieved from browser history data. In the case of manual entry, the user can enter their viewing history into a form or select the content they have watched using checkboxes. In the case of automatic retrieval, the system automatically collects the user's viewing history using browser history data or streaming service APIs. Furthermore, the reception desk can also provide surveys and quizzes to understand the user's preferences. For example, by asking the user about their favorite genres, actors, directors, and music artists, it can collect more detailed preference information. This allows the reception desk to comprehensively collect user viewing history and preferences and prepare data to provide to the analytics department.

[0071] The analysis unit analyzes the information entered by the reception unit. Specifically, it extracts user preferences using data mining techniques. Data mining techniques include clustering and association rule mining. For example, it uses clustering to group users with similar viewing patterns, and association rule mining to analyze what other content users who have viewed specific content have viewed. The analysis unit can also analyze user viewing patterns using statistical analysis and machine learning algorithms. For example, it uses regression analysis to model the relationship between user viewing time, frequency, and preferences, and uses machine learning algorithms to predict future viewing trends from user viewing history. Furthermore, the analysis unit can use natural language processing techniques to analyze text data entered by users (e.g., reviews and comments) and extract user sentiment and opinions. This allows the analysis unit to analyze user viewing history and preferences in detail and generate information to provide to the suggestion unit.

[0072] The suggestion department proposes entertainment based on information analyzed by the analysis department. Specifically, it uses a recommendation system to suggest the most suitable content for the user. The recommendation system includes collaborative filtering and content-based filtering. Collaborative filtering is a method of recommending content based on the viewing history of other users with similar preferences, while content-based filtering is a method of recommending new content based on the characteristics of content the user has previously viewed. For example, using collaborative filtering, new content can be recommended to user A based on the viewing history of user B, who has viewed content similar to what user A has viewed. Also, using content-based filtering, new movies with similar characteristics can be recommended based on the genre, actors, directors, etc., of movies the user has previously viewed. Furthermore, the suggestion department can also provide personalized suggestions. For example, it can suggest the most suitable content for a specific time of day or day of the week based on the user's viewing history and preferences. In this way, the suggestion department can propose the most suitable entertainment for the user and improve user satisfaction.

[0073] The service provider uses generative AI to provide detailed information about the suggested entertainment. Specifically, it uses text generation AI (e.g., LLM) to generate detailed information. The generative AI generates descriptions, related information, and user reviews about the suggested content. For example, as a movie description, it provides information such as the synopsis, main cast, and director, and as related information, it provides other movies in the same genre and related news articles. As user reviews, it summarizes reviews posted by other users and provides ratings and comments. The generative AI uses natural language processing technology to generate text that is easy for users to understand and interesting. Furthermore, the service provider has an interface to provide the generated information to users. For example, it allows users to view detailed information about the suggested content through a website or mobile app. This allows the service provider to provide users with detailed information about the suggested entertainment, attract their interest, and encourage viewing.

[0074] The customization department provides a user-level customized experience based on information provided by the service provider. Specifically, it provides interfaces and personalized content tailored to the user's preferences. For example, it customizes the home screen and recommendation lists based on the user's favorite genres, actors, and directors. It can also personalize notifications and reminders based on the user's viewing history and preferences. For example, it sends notifications to the user when a new episode is released. Furthermore, the customization department can collect user feedback and continuously improve the accuracy and effectiveness of customization. For example, by having users leave ratings and comments on the provided content, the system can more accurately understand the user's preferences and reflect them in future suggestions and customizations. In addition, the customization department provides the optimal display method according to the user's device and environment. For example, it provides interfaces that support different devices such as smartphones, tablets, and televisions, so that users can comfortably enjoy entertainment on any device. In this way, the customization department can provide an individually optimized entertainment experience for each user and improve user satisfaction.

[0075] The reception desk can input user preferences and past browsing history. For example, the reception desk can input information such as content the user has previously viewed, the duration of viewing, and the frequency of viewing. The reception desk can accept manual input from the user or automatically retrieve browser history data. This allows for more accurate entertainment recommendations by inputting user preferences and past browsing history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user preferences and past browsing history into an AI, which can then automatically analyze the data.

[0076] The analysis unit can analyze the information entered by the reception unit. For example, the analysis unit can extract user preferences using data mining techniques. The analysis unit can also analyze user viewing patterns using statistical analysis and machine learning algorithms. This allows the system to suggest the most suitable entertainment to the user by analyzing the entered information. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the user's viewing history data into the AI, which then analyzes the data to extract the user's preferences.

[0077] The suggestion unit can suggest entertainment based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest the most suitable content to the user using a recommendation system. The suggestion unit can also provide personalized suggestions. This allows the user to receive the most suitable entertainment by suggesting entertainment based on the analyzed information. Some or all of the above processes in the suggestion unit are performed using AI. For example, the suggestion unit inputs the information analyzed by the analysis unit into the AI, and the AI ​​suggests the most suitable entertainment to the user.

[0078] The service provider can provide detailed information about entertainment using generative AI. For example, the service provider can provide content descriptions, related information, and user reviews. The generative AI generates detailed information using text generation AI (e.g., LLM). This allows the service provider to provide detailed information about entertainment and deepen user understanding. Some or all of the above-described processes in the service provider are performed using the generative AI. For example, the service provider inputs information about entertainment into the generative AI, which then generates and provides detailed information.

[0079] The customization unit can provide a user-level customized experience based on the information provided by the service provider. For example, the customization unit can provide an interface tailored to the user's preferences and personalized content. This allows the user to receive a fresh entertainment experience by providing a customized experience based on the provided information. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit inputs the information provided by the service provider into the AI, and the AI ​​performs the optimal customization for the user.

[0080] The reception desk can estimate the user's emotions and adjust the timing of inputting preferences and browsing history based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of preferences and browsing history. This allows for more appropriate input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 reception desk may be performed using AI or not. For example, the reception desk inputs user emotion data into the AI, which estimates the emotions and adjusts the input timing.

[0081] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display the user's frequently used preferences and browsing history as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest preferences and browsing history used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into the AI, and the AI ​​will select the optimal input method.

[0082] The reception desk can filter user preferences and browsing history based on their current interests. For example, it can prioritize displaying relevant preferences and browsing history based on genres the user is currently interested in. It can also suggest relevant preferences and browsing history based on content the user has recently viewed. Furthermore, if the reception desk is interested in a particular event or trend, it can filter based on that information. This allows users to input more relevant information by filtering based on their current interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current interests and data into an AI, which then performs the filtering.

[0083] The reception unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of input preferences and browsing history. For example, if the user is excited, the reception unit will prioritize inputting highly entertaining content. Similarly, if the user is relaxed, the reception unit can prioritize inputting relaxing content. Furthermore, if the user is stressed, the reception unit can prioritize inputting content that helps relieve stress. This allows for the input of more appropriate information by prioritizing input 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 reception unit may be performed using AI or not. For example, the reception unit inputs user emotion data into an AI, which estimates the emotions and determines the input priority.

[0084] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input their preferences and browsing history. For example, the reception desk can prioritize inputting popular content in the user's current location. It can also suggest relevant content based on places the user has visited in the past. Furthermore, if the user is traveling, the reception desk can input relevant content based on information about their travel destination. This allows for the input of more relevant information by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which then prioritizes inputting highly relevant information.

[0085] The reception desk can analyze the user's social media activity and input relevant information when they input their preferences and browsing history. For example, the reception desk can input relevant preferences and browsing history based on the content the user has shared on social media. It can also suggest relevant content based on the activity of accounts the user follows. Furthermore, the reception desk can input relevant information based on trends in online communities the user participates in. This allows for the input of more relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's social media activity data into the AI, and the AI ​​inputs relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs user emotion data into the AI, and the AI ​​estimates the emotions and adjusts the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. Conversely, it can perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. By adjusting the level of detail of the analysis based on the importance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs information importance data into the AI, and the AI ​​adjusts the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for information about movies, the analysis unit applies a movie-specific analysis algorithm. Similarly, for information about music, it can apply a music-specific analysis algorithm. Furthermore, for information about games, it can apply a game-specific analysis algorithm. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit inputs information category data into the AI, and the AI ​​applies different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results 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 analysis unit may be performed using AI or not. For example, the analysis unit inputs user emotion data into the AI, and the AI ​​estimates the emotions and adjusts the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit will prioritize the analysis of the most recent information. It can also lower the priority of older information. Furthermore, the analysis unit can adjust the analysis schedule according to the timing of information submission. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the timing of information submission. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs information submission timing data into the AI, and the AI ​​determines the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the relevance data of the information into the AI, and the AI ​​adjusts the order of analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user emotion data into an AI, which estimates the emotion and adjusts the way suggestions are presented.

[0093] The proposal department can adjust the level of detail in proposals based on the importance of the entertainment. For example, it can provide detailed proposals for high-importance entertainment, and concise proposals for low-importance entertainment. Furthermore, the proposal department can prioritize proposals according to the importance of the entertainment. This allows for the provision of more appropriate proposals by adjusting the level of detail based on the importance of the entertainment. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs entertainment importance data into the AI, and the AI ​​adjusts the level of detail in the proposals.

[0094] The suggestion unit can apply different suggestion algorithms depending on the entertainment category when making suggestions. For example, for suggestions related to movies, the suggestion unit applies a suggestion algorithm specifically for movies. Similarly, for suggestions related to music, it can apply a suggestion algorithm specifically for music. Furthermore, for suggestions related to games, it can apply a suggestion algorithm specifically for games. This allows for the provision of more appropriate suggestions by applying different suggestion algorithms depending on the entertainment category. Some or all of the above processing in the suggestion unit is performed using AI. For example, the suggestion unit inputs entertainment category data into the AI, and the AI ​​applies different suggestion algorithms.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide visually appealing suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user emotion data into an AI, which estimates the emotion and adjusts the length of the suggestions.

[0096] The proposal department can prioritize proposals based on the submission timing of the entertainment. For example, the department will prioritize proposals for the latest entertainment. It can also lower the priority of proposals for older entertainment. Furthermore, the proposal department can adjust the proposal schedule according to the submission timing of the entertainment. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing of the entertainment. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs entertainment submission timing data into the AI, and the AI ​​determines the priority of the proposals.

[0097] The proposal department can adjust the order of proposals based on the relevance of the entertainment. For example, it will prioritize proposals for highly relevant entertainment. It can also postpone proposals for less relevant entertainment. Furthermore, the proposal department can adjust the proposal schedule according to the relevance of the entertainment. This allows for the provision of more appropriate proposals by adjusting the order of proposals based on the relevance of the entertainment. Some or all of the above processing in the proposal department is performed using AI. For example, the proposal department inputs entertainment relevance data into the AI, and the AI ​​adjusts the order of proposals.

[0098] The service provider can estimate the user's emotions and adjust how detailed information is provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can also provide concise information that gets straight to the point. Furthermore, if the user is excited, the service provider can provide visually appealing information. This allows for the provision of more appropriate information by adjusting how detailed information is provided 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 service provider may be performed using or without a generative AI. For example, the service provider inputs user emotion data into a generative AI, which estimates the emotions and adjusts how detailed information is provided.

[0099] The information provider can adjust the level of detail provided based on the importance of the entertainment when providing detailed information. For example, the provider can provide detailed information for high-importance entertainment, and concise information for low-importance entertainment. Furthermore, the provider can determine the priority of information provision according to the importance of the entertainment. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the entertainment. Some or all of the above processing in the information provider is performed using a generative AI. For example, the provider inputs entertainment importance data into the generative AI, and the generative AI adjusts the level of detail provided.

[0100] The information delivery unit can apply different delivery algorithms depending on the entertainment category when providing detailed information. For example, for information about movies, the unit can apply a delivery algorithm specifically for movies. Similarly, for information about music, it can apply a delivery algorithm specifically for music. Furthermore, for information about games, it can apply a delivery algorithm specifically for games. This allows for the provision of more appropriate information by applying different delivery algorithms depending on the entertainment category. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the information delivery unit inputs entertainment category data into the generative AI, which then applies different delivery algorithms.

[0101] The service provider can estimate the user's emotions and adjust the length of the detailed information based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise information. If the user is relaxed, the service provider can also provide detailed information. Furthermore, if the user is excited, the service provider can provide visually appealing information. By adjusting the length of the detailed information according to the user's emotions, more appropriate information 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 service provider may be performed using or without a generative AI. For example, the service provider inputs user emotion data into a generative AI, which estimates the emotion and adjusts the length of the detailed information.

[0102] The information provider can prioritize the provision of detailed information based on the submission date of the entertainment. For example, the provider will prioritize providing information on the latest entertainment. The provider can also lower the priority of providing information on older entertainment. Furthermore, the provider can adjust the information provision schedule according to the submission date of the entertainment. This allows for the provision of more appropriate information by prioritizing the provision based on the submission date of the entertainment. Some or all of the above processing in the information provider is performed using a generative AI. For example, the provider inputs entertainment submission date data into the generative AI, and the generative AI determines the priority of provision.

[0103] The information delivery unit can adjust the order of information delivery based on the relevance of the entertainment when providing detailed information. For example, the unit will prioritize providing information on highly relevant entertainment. It can also postpone the delivery of information on less relevant entertainment. Furthermore, the unit can adjust the information delivery schedule according to the relevance of the entertainment. This allows for the provision of more appropriate information by adjusting the order of delivery based on the relevance of the entertainment. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the information delivery unit inputs entertainment relevance data into the generative AI, and the generative AI adjusts the order of delivery.

[0104] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, if the user is relaxed, the customization unit can provide detailed customization options. If the user is in a hurry, it can also provide concise customization options. Furthermore, if the user is excited, it can provide visually appealing customization options. This allows for more appropriate customization by adjusting the customization method 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 customization unit may be performed using AI or not. For example, the customization unit inputs user emotion data into the AI, which estimates the emotions and adjusts the customization method.

[0105] The customization unit can analyze the user's past behavior history to select the optimal customization method during the customization process. For example, the customization unit can propose the optimal customization method based on the customization options the user has previously selected. Furthermore, the customization unit can predict and propose customization options to be used during specific time periods based on the user's past behavior history. In addition, the customization unit can analyze the user's past behavior history and propose the most efficient customization method. This allows the customization unit to provide the user with the most suitable customization method by analyzing past behavior history. Some or all of the above processes in the customization unit may be performed using AI, or not. For example, the customization unit can input the user's past behavior history data into the AI, which then selects the optimal customization method.

[0106] The customization unit can customize the means of customization based on the user's current interests and preferences during the customization process. For example, the customization unit can provide relevant customization options based on the genres the user is currently interested in. It can also suggest relevant customization options based on the content the user has recently viewed. Furthermore, if the user is interested in a particular event or trend, the customization unit can provide customization options based on that information. This allows for more appropriate customization by providing customization options based on current interests and preferences. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's current interests and preferences data into the AI, and the AI ​​can select the means of customization.

[0107] The customization unit can estimate the user's emotions and determine the priority of customizations based on those emotions. For example, if the user is excited, the customization unit will prioritize providing highly entertaining customizations. It can also prioritize providing relaxing customizations if the user is relaxed. Furthermore, if the user is stressed, the customization unit can prioritize providing customizations that help relieve stress. This allows for more appropriate customizations to be provided by prioritizing them 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit inputs user emotion data into an AI, which then estimates the emotion and determines the priority of customizations.

[0108] The customization unit can select the optimal customization method by considering the user's geographical location during the customization process. For example, the customization unit may prioritize providing popular customization options in the user's current location. It can also suggest relevant customization options based on places the user has visited in the past. Furthermore, if the user is traveling, the customization unit can provide relevant customization options based on information about their travel destination. This allows for more appropriate customization by considering geographical location. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit may input the user's geographical location information into the AI, which then selects the optimal customization method.

[0109] The customization unit can analyze the user's social media activity during the customization process and propose customization options. For example, the customization unit can provide relevant customization options based on the content the user has shared on social media. It can also propose relevant customization options based on the activity of accounts the user follows. Furthermore, the customization unit can provide relevant customization options based on the trends of online communities the user participates in. This allows for more appropriate customization by analyzing social media activity. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit inputs the user's social media activity data into the AI, and the AI ​​selects the customization options.

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

[0111] The entertainment delivery system can also acquire user health data and suggest entertainment based on this data. For example, it can acquire the user's heart rate and sleep data and suggest relaxing content if relaxation is needed. It can also suggest energetic music or videos if the user is exercising. Furthermore, it can analyze the user's health data over the long term and provide entertainment tailored to changes in their health condition. This allows for the provision of optimal entertainment based on the user's health status.

[0112] The entertainment delivery system can further acquire users' social network data and make entertainment suggestions based on it. For example, it can suggest content that the user's friends are watching, providing common topics of conversation. It can also suggest relevant content based on trends in the online communities the user participates in. Furthermore, it can analyze the user's activity on social networks and suggest new content that the user might be interested in. This allows the system to provide optimal entertainment tailored to the user's social network.

[0113] The entertainment delivery system can also acquire the user's geographical location information and suggest entertainment based on it. For example, if the user is traveling, it can suggest tourist information and local entertainment in their destination. If the user is attending a specific event, it can also suggest content related to that event. Furthermore, it can provide information on nearby movie theaters and concerts based on the user's current location. This allows for the provision of optimal entertainment tailored to the user's geographical location.

[0114] The entertainment delivery system can also acquire the user's past purchase history and make entertainment suggestions based on it. For example, it can suggest new content related to movies and music the user has purchased in the past. It can also analyze reviews of products the user has purchased and suggest content that the user might be interested in. Furthermore, it can analyze the user's purchase history over the long term and provide entertainment that responds to changes in the user's preferences. This allows for the provision of optimal entertainment based on the user's purchase history.

[0115] The entertainment delivery system can also acquire information about the user's device usage and suggest entertainment based on that information. For example, if the user is using a smartphone, it can suggest content that can be enjoyed in a short amount of time. If the user is using a tablet, it can suggest longer content. Furthermore, it can analyze the user's device usage and provide entertainment that is best suited to the device the user uses most often. This allows for the provision of optimal entertainment based on the user's device usage.

[0116] An entertainment delivery system can estimate a user's emotions and suggest entertainment based on those emotions. For example, if a user is sad, it can suggest content that will lift their spirits. If a user is excited, it can suggest energetic content. Furthermore, if a user is relaxed, it can suggest relaxing content. This allows the system to provide optimal entertainment based on the user's emotions.

[0117] An entertainment delivery system can estimate a user's emotions and adjust the way entertainment is delivered based on those emotions. For example, if a user is stressed, content can be provided with a simple interface. If the user is relaxed, content can be provided with an interface containing detailed information. Furthermore, if the user is in a hurry, content can be provided in a way that allows for quick access. This enables the delivery of optimal entertainment based on the user's emotions.

[0118] The entertainment delivery system can estimate the user's emotions and customize the entertainment based on those emotions. For example, if the user is excited, it can provide highly entertaining customizations. If the user is relaxed, it can provide relaxing customizations. Furthermore, if the user is stressed, it can provide customizations that help relieve stress. This enables the system to achieve optimal entertainment customization based on the user's emotions.

[0119] The entertainment delivery system can estimate the user's emotions and provide entertainment feedback based on those emotions. For example, if the user is satisfied, it will provide positive feedback. If the user is dissatisfied, it can also provide feedback suggesting areas for improvement. Furthermore, if the user is excited, it can even suggest the next content to watch. This enables the delivery of optimal entertainment feedback based on the user's emotions.

[0120] The entertainment delivery system can estimate the user's emotions and evaluate the entertainment based on those emotions. For example, it can give a high rating if the user is enjoying themselves, and a low rating if the user is bored. Furthermore, it can give a special rating if the user is moved. This enables the system to provide optimal entertainment evaluation based on the user's emotions.

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

[0122] Step 1: The reception desk inputs the user's preferences and browsing history. For example, it can input information such as the content the user has watched in the past, the duration of viewing, and the frequency of viewing. The reception desk can accept manual input from the user or automatically retrieve browser history data. Step 2: The analysis unit analyzes the information entered by the reception unit. For example, it may use data mining techniques to extract user preferences. It can also analyze user viewing patterns using statistical analysis and machine learning algorithms. Step 3: The proposal unit proposes entertainment based on the information analyzed by the analysis unit. For example, it uses a recommendation system to suggest the most suitable content for the user. It can also provide personalized suggestions. Step 4: The providing department provides detailed information about the entertainment proposed by the proposing department. For example, it provides content descriptions, related information, and user reviews using a generative AI. The generative AI generates detailed information using a text generation AI (e.g., LLM). Step 5: The customization team provides a user-level customized experience based on the information provided by the service team. For example, they provide an interface and personalized content tailored to the user's preferences.

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

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

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

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, provision unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the user's preferences and browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes entertainment based on the analyzed information. The provision unit is implemented by the output device 40 of the smart device 14, which provides detailed information using generating AI. The customization unit is implemented by the control unit 46A of the smart device 14, which provides a user-level customized experience. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, provision unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and inputs the user's preferences and browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes entertainment based on the analyzed information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides detailed information using generating AI. The customization unit is implemented by the control unit 46A of the smart glasses 214 and provides a user-level customized experience. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, provision unit, and customization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and inputs the user's preferences and browsing history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes entertainment based on the analyzed information. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides detailed information using generating AI. The customization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a user-level customized experience. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, provision unit, and customization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and inputs the user's preferences and browsing history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes entertainment based on the analyzed information. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides detailed information using generating AI. The customization unit is implemented by, for example, the control unit 46A of the robot 414 and provides a user-level customized experience. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0194] (Note 1) A reception area where users input their preferences and browsing history, An analysis unit analyzes the information input by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes entertainment, A provisioning unit that provides detailed information about the entertainment proposed by the aforementioned proposal unit, A customization unit provides a user-level customized experience based on the information provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter your preferences and past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The information entered by the reception unit is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the information analyzed by the aforementioned analysis unit, the system proposes entertainment options. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Generative AI provides detailed information about entertainment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned customization unit is Based on the information provided by the aforementioned service provider, a user-level customized experience is provided. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting preferences and browsing history based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their preferences and browsing history, filtering is performed based on their current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input preferences and browsing history based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their preferences and browsing history, the system prioritizes inputting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their preferences and browsing history, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of entertainment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the entertainment category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of entertainment submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to entertainment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how detailed information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing detailed information, we adjust the level of detail based on the importance of the entertainment value. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing detailed information, different delivery algorithms are applied depending on the entertainment category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the detailed information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing detailed information, we will prioritize the provision based on the timing of the entertainment submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing detailed information, we adjust the order of presentation based on the relevance of the entertainment content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customization unit is During customization, the system analyzes the user's past behavior history to select the optimal customization method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned customization unit is During customization, the customization methods are tailored based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned customization unit is During customization, we analyze the user's social media activity and suggest customization options. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A reception area where users input their preferences and browsing history, An analysis unit analyzes the information input by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes entertainment, A provisioning unit that provides detailed information about the entertainment proposed by the aforementioned proposal unit, A customization unit provides a user-level customized experience based on the information provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter your preferences and past browsing history. The system according to feature 1.

3. The aforementioned analysis unit, The information entered by the reception unit is analyzed. The system according to feature 1.

4. The aforementioned proposal section is, Based on the information analyzed by the aforementioned analysis unit, the system proposes entertainment options. The system according to feature 1.

5. The aforementioned supply unit is, Generative AI provides detailed information about entertainment. The system according to feature 1.

6. The aforementioned customization unit is Based on the information provided by the aforementioned service provider, a user-level customized experience is provided. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting preferences and browsing history based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter their preferences and browsing history, filtering is performed based on their current interests and concerns. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input preferences and browsing history based on the estimated user emotions. The system according to feature 1.

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