system

Abstract

Provide a system. 【Solution means】 Means for collecting the user's past entertainment history, Means for analyzing the user's preferences based on the history, Means for generating entertainment information to be proposed to the user, Means for transmitting the generated information to the user's terminal, Means for supporting the reservation and purchase of the entertainment selected by the user, Means for automatically generating a review after the user watches, Means for sharing the review on a social network, Means for gamifying the user's entertainment experience based on a point system, Means for reminding the next entertainment schedule, Means for immediately playing the content on the terminal, Means for enabling the user to view, modify, and share the review, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern entertainment selections, there are various options, and it is not easy to find entertainment that suits individual preferences. Also, if the entire experience process, such as reservation procedures, creation of reviews after experience, and sharing on SNS, can be smoothly advanced, a richer entertainment experience can be enjoyed. However, a system that consistently supports these processes is not currently provided sufficiently. Therefore, it is an issue to provide a mechanism that proposes entertainment based on individual user preferences and consistently supports the entire experience.

Means for Solving the Problems

[0005] This invention provides a means for collecting a user's past entertainment history and analyzing their preferences based on that history. Furthermore, it includes means for generating entertainment information to suggest to the user and transmitting it to the user's terminal. In addition, it provides means to support reservations and purchases for entertainment selected by the user, and means to automatically generate reviews after the user has viewed the content and share them on social networks. Furthermore, it introduces means to gamify the user's entertainment experience based on a point system and includes means to remind the user of their next entertainment plans, providing an integrated entertainment support system with all these functions combined.

[0006] "User's past entertainment history" refers to data about what movies, music, and events a user has watched or attended in the past.

[0007] "Means of analyzing preferences" refers to techniques or methods for analyzing collected historical data in order to understand a user's preferences and tendencies regarding entertainment.

[0008] "Means for generating entertainment information" refers to technologies or systems that create appropriate movie, music, and event information based on user preference analysis results.

[0009] "Means of transmission to the terminal" refers to communication functions and technologies for transferring generated entertainment information to the user's device.

[0010] "Means of supporting reservations and purchases" refer to systems and functions that assist users with the necessary reservation and purchase procedures for the entertainment they have selected.

[0011] "Methods for automatically generating reviews" refer to technologies that automatically create reviews using computers based on users' impressions and ratings after viewing a product or service.

[0012] "Means of sharing on social networks" refers to technologies and methods for posting automatically generated reviews to online social media platforms.

[0013] "A means of gamification based on a point system" refers to a method of awarding points to users for their entertainment activities and using those points to incorporate game elements into the user experience.

[0014] "A way to remind users of upcoming entertainment events" is a feature that notifies users of upcoming entertainment events to help them remember their schedules. [Brief explanation of the drawing]

[0015] [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. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] 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.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for enriching the entertainment experience and providing users with optimal content. This system primarily consists of the interaction of three parties: a server, a terminal, and a user.

[0037] Server Functions

[0038] The server collects the user's past entertainment history. This history includes titles of movies the user has watched, ratings, details of events they have attended, and genres of music they have listened to. The server analyzes this data to learn the user's preferences. Machine learning algorithms are used for this analysis. The algorithms are responsible for understanding the user's preferences and organizing the data for future recommendations.

[0039] Based on the analysis results, the server generates information on movies, music, and events best suited to the user. This generated information reflects the user's individual preferences and is provided at the appropriate time. This information is sent to the terminal and can be viewed by the user.

[0040] Device functions

[0041] The terminal displays entertainment information sent from the server to the user. This allows the user to easily view the information and select entertainment on their device. For selected entertainment, the terminal supports the reservation and purchase process. Specifically, this includes reserving movie tickets and purchasing tickets for music events.

[0042] Furthermore, after the user has viewed the content, the device automatically generates a review in conjunction with the server. This review is created based on the user's feedback and viewing data, and the user is asked whether or not they wish to share it on social networks.

[0043] Furthermore, the device will remind users of their next entertainment plans. This ensures that users won't forget and can enjoy their next scheduled movie or event.

[0044] User actions

[0045] Users experience a process of selecting content that interests them based on the entertainment information displayed on their device. Once their selection is complete, they can smoothly reserve and purchase content through their device. After viewing, they can review automatically generated reviews from their device and revise them as needed. They can also choose to share their reviews on social networks.

[0046] For example, if a user wants to watch a movie on a holiday, the server analyzes the user's past viewing history and preferences and provides movie recommendations to the device based on that information. The user selects a movie and makes a ticket reservation through the device. After watching the movie, the device automatically generates a review of the film, which the user can then share on social media at their discretion. Furthermore, the device receives a reminder for the next weekend, notifying the user of their next movie viewing schedule.

[0047] As described above, the present invention is a system that more personalizes and consistently supports the user's entertainment experience.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server collects the user's past entertainment history. This history includes movie viewing history, music playback records, event attendance information, and more. The server retrieves this data from a database.

[0051] Step 2:

[0052] The server analyzes the collected data and applies machine learning algorithms to identify user preferences. The algorithms identify the user's preferred genres and artist tendencies, and update the user profile accordingly.

[0053] Step 3:

[0054] The server generates movie, music, and event information tailored to the user based on the results of preference analysis. This information includes details such as recommended viewing and participation times, and ratings of related works.

[0055] Step 4:

[0056] The server generates entertainment information and sends it to the user's device. The device receives this information and prepares the necessary UI components for display.

[0057] Step 5:

[0058] The device displays entertainment information to the user. The user reviews the provided information through a visual interface and selects entertainment based on their interests.

[0059] Step 6:

[0060] The user selects their desired entertainment and proceeds with the reservation and purchase process through the terminal. The terminal provides an interface for entering payment information and transmits that data to the server.

[0061] Step 7:

[0062] The user experiences the entertainment of their choice. Once the experience is complete, the user provides brief feedback through their device.

[0063] Step 8:

[0064] The device collects feedback and sends it to the server. The server automatically generates reviews based on this feedback. It also uses this data to reanalyze user preferences.

[0065] Step 9:

[0066] The server sends the generated review to the device. The device then prompts the user to confirm the review and awaits the user's approval before sharing it on social networks.

[0067] Step 10:

[0068] The device uses a feature to remind the user of their next entertainment event. The reminder integrates with the user's calendar and notification system to inform them of the next event at the appropriate time.

[0069] (Example 1)

[0070] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0071] Traditional entertainment delivery systems have been insufficient in addressing individual user preferences, making it difficult to find the optimal content from countless options. Furthermore, it was challenging to reliably remember the user's planned use of selected content, resulting in a lack of consistency in the user experience.

[0072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0073] In this invention, the server includes means for collecting the user's past information history, means for analyzing the user's preferences, and means for generating personalized recommendations using a generative AI model. This enables a consistent entertainment experience by efficiently providing content optimized for the user and notifying them of their next scheduled use.

[0074] "Information history" refers to a record of actions a user has taken or content they have used in the past.

[0075] "Preferences" refer to specific tastes or tendencies that a user exhibits based on their past activities.

[0076] "Content information" refers to entertainment-related information provided to users, such as movies, music, and events.

[0077] "Device" refers to a terminal or device used by a user, and is a device capable of receiving and displaying information.

[0078] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate useful information from data.

[0079] A "notification" is a message or alert used to inform a user about upcoming usage schedules or other important information.

[0080] A "computational learning algorithm" is a mathematical method used to clarify patterns and preferences through data analysis.

[0081] This invention is a system in which a server, a terminal, and a user work together to provide the user with the most suitable content. In this system, the server is located in a cloud environment and uses a database to collect the user's past information history. This information history includes the titles and ratings of movies the user has watched, details of events they have attended, and genres of music they have listened to. The collected data is analyzed using machine learning algorithms to identify the user's preferences. This analysis particularly utilizes clustering and collaborative filtering techniques.

[0082] The server generates personalized recommendations using a generative AI model based on the analysis results. For example, it might generate a prompt like, "You will enjoy this movie: 'Mystery Adventure' - featuring a captivating plot and diverse cast." This generated information is then sent to the terminal via the network.

[0083] The device organizes content information sent from the server and displays it in a user-friendly format. Users can easily select content of interest and reserve or purchase it using the device. For selected content, the device automatically generates reviews and sends them to the server for further analysis.

[0084] The device also has a feature that notifies users of upcoming content schedules. This allows users to enjoy their next entertainment without forgetting.

[0085] By using this system, users can receive content suggestions based on their individual preferences, leading to a richer entertainment experience.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects historical information transmitted from the user's device into a cloud database. Inputs include the titles, ratings, and categories of content the user has viewed. The data is collected in real time and stored for later analysis. The output is a chronologically organized history of the information.

[0089] Step 2:

[0090] The server analyzes user preferences using collected information history. This analysis is performed using machine learning algorithms, employing clustering and collaborative filtering techniques. The input is the collected information history, and the output is the user's preference pattern. Specifically, the server builds a preference model tailored to each individual user, while also referencing data from other users with similar preferences.

[0091] Step 3:

[0092] The server generates personalized content recommendations using a generative AI model based on the analysis results. The input is the user's preference pattern, and the output is the recommendation text. Specifically, the server creates prompt texts such as "You will enjoy this movie: 'Mystery Adventure'" by filling in appropriate content for a template.

[0093] Step 4:

[0094] Content information generated from the server is sent to the device, which then displays it. The input is the content information from the server, and the output is the information visually presented to the user. The device uses a notification function to display new content on the dashboard, allowing the user to easily check it.

[0095] Step 5:

[0096] Users select items of interest from the content information displayed on the terminal and make reservations or purchases. The input is the content information presented on the terminal, and the output is reservation confirmation information for the selected content. The terminal integrates with the payment system, allowing users to complete the purchase process with a single click.

[0097] Step 6:

[0098] After viewing, the device collects user feedback and automatically generates an evaluation. The input is user feedback, and the output is a calculated evaluation statement. The device sends the evaluation to a server and stores it in a database for future analysis.

[0099] Step 7:

[0100] The device reminds the user of their next scheduled content usage. The input is schedule information from the server, and the output is a notification message to the user. This allows users to plan their daily enjoyment without forgetting their next entertainment event.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] When users enjoy entertainment, there are challenges in ensuring they can choose the best content from a diverse range of options, watch it smoothly, and easily share reviews. Furthermore, there is a need to improve entertainment satisfaction by providing personalized experiences based on user preferences.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, means for generating entertainment information to suggest to the user, means for transmitting the generated information to the user's terminal, means for supporting the reservation and purchase of entertainment selected by the user, means for automatically generating reviews after the user has viewed the content and sharing them on social networks, means for gamifying the user's entertainment experience based on a point system, means for reminding the user of their next entertainment appointment, means for instantly playing the content on the terminal, and means for enabling the user to review, modify, and share reviews. This enables personalized suggestions that reflect the user's preferences and allows for an efficient execution of the entire process from content selection to viewing and review sharing.

[0106] "Means of collecting a user's past entertainment history" refers to a function that collects data about entertainment content that a user has previously viewed or participated in.

[0107] "Means for analyzing user preferences" refers to analytical functions that identify user preferences and trends based on collected historical data.

[0108] "Means for generating entertainment information to suggest to users" refers to a function that presents users with suitable entertainment options based on analysis results.

[0109] "Means for transmitting generated information to the user's terminal" refers to a communication function for delivering proposed entertainment information to the user's device.

[0110] "Means to support the reservation and purchase of entertainment selected by the user" refers to support functions that facilitate the reservation and purchase of content chosen by the user.

[0111] "A means of automatically generating reviews after a user has viewed a video" refers to a function that automatically creates a review of the content after the user has finished watching it.

[0112] "Means of sharing on social networks" refers to communication and transmission functions that enable the generated reviews to be shared with others online.

[0113] "A means of gamifying the user's entertainment experience based on a point system" refers to a function that awards points as an incentive for entertainment activities and incorporates game elements to improve the user's experience.

[0114] "A way to remind users of upcoming entertainment events" is a feature that notifies users of their next entertainment event and encourages them to check their schedule so they don't forget.

[0115] "Means for instantly playing content on a device" refers to a function that allows selected content to be immediately streamed on the user's device.

[0116] "Means that enable users to review, revise, and share reviews" refers to features that help users review the content of automatically generated reviews, edit them as needed, and then share them with others again.

[0117] This invention is a system for personalizing and efficiently supporting users' entertainment experiences. This system is primarily built on the interaction between a server, a terminal, and the user.

[0118] The server collects the user's past entertainment history, including data such as movies watched, events attended, and music listened to. Based on this data, the server uses machine learning algorithms (e.g., scikit-learn) to analyze the user's preferences. This analysis helps understand the user's tendencies and is used to suggest appropriate content. The suggested entertainment information reflects the user's individual preferences and can be played immediately using the streaming capabilities of external services.

[0119] The device is responsible for presenting entertainment information sent from the server to the user. Once the user selects content, the device supports its reservation and purchase, and immediately plays the selected content. Furthermore, after viewing, a review is automatically generated using a natural language processing library (e.g., spaCy), which the user can review, modify as needed, and share on social networks.

[0120] Users select content based on the information displayed on their device and enjoy streaming playback. After watching, they can review automatically generated reviews and choose whether or not to share them. This allows users to maximize their entertainment experience.

[0121] For example, if a user wants to watch a movie on their day off, the server will display recommended movies based on their past viewing history and analysis results. The user can select a movie and play it instantly on their device. Afterwards, a review is generated, and if they want to share it with friends, they can easily adjust the sharing settings.

[0122] An example of a prompt to a generative AI model would be, "I'm looking for a relaxing movie to watch on my day off. What movies do you think are good these days?" This would allow the model to receive content suggestions in this format.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server collects the user's past entertainment history from a database. This data includes information such as movies the user has watched, events they have attended, and music they have listened to. This data is collected as input to prepare a basic dataset for analysis.

[0126] Step 2:

[0127] The server analyzes the collected data using machine learning algorithms (e.g., scikit-learn) to model user preferences. Here, user viewing trends and preferences are extracted as patterns, and basic information for personalized content recommendations is output. This process involves data analysis and calculations, resulting in the generation of a user preference profile.

[0128] Step 3:

[0129] Based on the generated preference profile, the server selects the most suitable entertainment content for the user and creates a recommendation list. This list contains information about the content to be suggested and is temporarily stored for transmission to the device.

[0130] Step 4:

[0131] The terminal receives a list of recommendations sent from the server and presents it visually to the user. The user can select content of interest from this list. The terminal processes this received data and displays it as options on a GUI.

[0132] Step 5:

[0133] The user selects content they are interested in on their device. This selection serves as input, and the device supports the reservation and purchase process for the specified entertainment content. The data processing involved here includes receiving the selection information and creating a reservation completion notification based on that information.

[0134] Step 6:

[0135] The device immediately streams the selected content. Here, an API from an external streaming service is used to obtain a link for real-time playback of the selected movie or music, and playback begins on the user's device.

[0136] Step 7:

[0137] Once a user finishes viewing content, the device automatically generates a review using a natural language processing library (e.g., spaCy) based on the data collected during viewing. This generation process analyzes the content viewed and the user's reactions, and then outputs a review statement.

[0138] Step 8:

[0139] Users can review the generated review and revise it if necessary. After revision, the review can be shared on social networks. The device will follow the user's instructions to save and submit the review.

[0140] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0141] This invention provides a system that comprehensively supports users' entertainment experiences and enables the delivery of optimal content based on individual preferences and emotions. This system consists of multiple components, including a server, terminals, and an emotion engine.

[0142] Server Functions

[0143] The server collects the user's past entertainment history and analyzes their preferences using machine learning algorithms. The collected data includes movie viewing history, music playback history, and details of events attended. Furthermore, the server integrates emotional data from an emotion engine to understand the user's real-time emotional state.

[0144] This allows the server to generate optimal entertainment information based on the user's preferences and emotional state. This information includes details on recommended movies and events, suggested music playlists, and more, all customized to the user's current mood.

[0145] Device functions

[0146] The device presents entertainment information sent from the server to the user and makes it available through a visual interface. A key role here is that the device works in conjunction with an emotion engine to detect the user's emotions in real time from their facial expressions and tone of voice.

[0147] When a user selects entertainment information they are interested in, the device supports reservation and purchase functions, allowing for easy completion of the necessary procedures. It also provides an interface for receiving payment information, ensuring a smooth user experience.

[0148] Furthermore, after viewing, the device automatically generates a review and supports the process of sharing it on social networks. It also has a function to remind users of their next entertainment plans, allowing for efficient schedule management.

[0149] User actions

[0150] Users select content that interests them from the entertainment information displayed on their device, based on their emotions and preferences. During this process, an emotion engine analyzes the user's emotions in real time and suggests the most appropriate options based on their current state of mind.

[0151] For example, if a user wants to relax, the server suggests calming movies and music and sends them to the device. After the user selects a movie and books tickets, the emotion engine generates a further customized review based on the user's emotions while watching. This review may be posted to social media via the device.

[0152] As described above, the present invention is a system that personalizes entertainment based on the user's emotions and preferences, providing a consistent user experience from booking and review creation to managing future appointments.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The server collects the user's past entertainment history. This history data includes records of movies the user has watched, music they have listened to, and events they have attended. The server retrieves this history information from a database.

[0156] Step 2:

[0157] The server works in conjunction with the emotion engine to detect the user's emotions in real time. Facial expression and voice data from when the user is using the device are analyzed through the emotion engine to recognize their emotional state. This information is then sent to the server.

[0158] Step 3:

[0159] Based on historical data collected by the server and real-time emotional states, a machine learning algorithm is used to analyze user preferences. The algorithm considers the user's current emotions and past preferences to suggest appropriate entertainment content.

[0160] Step 4:

[0161] The server generates entertainment recommendations for the user. This information includes suggestions for movies, music, and events that take into account the user's current mood. The generated information is sent to the terminal.

[0162] Step 5:

[0163] The device displays entertainment information received from the server to the user. The user can review and select the suggested content through the interface.

[0164] Step 6:

[0165] The device supports the reservation and purchase of entertainment content selected by the user. The device provides a form for entering necessary information and manages the reservation and payment process.

[0166] Step 7:

[0167] The user experiences the entertainment they selected. After the experience ends, the device re-analyzes the changes in their emotional state through an emotion engine. This analysis is used to evaluate the user's overall experience.

[0168] Step 8:

[0169] The device sends feedback to the server. The server automatically generates a review based on the feedback. The review is customized based on the user's emotional state and preferences.

[0170] Step 9:

[0171] The server generates a review and sends it to the device. The device then allows the user to review the review, make any necessary corrections, and then suggests sharing it on social networks.

[0172] Step 10:

[0173] The device activates a feature that reminds you of your next entertainment event. The reminder notifies the user of information about their next scheduled entertainment activity.

[0174] (Example 2)

[0175] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0176] Traditional entertainment systems have faced challenges in providing highly satisfying entertainment experiences because they struggle to personalize them to adequately consider user preferences and emotions. Furthermore, they have difficulty consistently managing the user experience, resulting in problems with the smooth operation of everything from selecting, booking, and purchasing entertainment to sharing reviews.

[0177] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0178] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, and means for detecting the user's real-time emotions. This makes it possible to generate optimal entertainment information based on the user's preferences and emotions and transmit it to the user terminal, thereby providing a consistent and personalized entertainment experience and improving user satisfaction.

[0179] A "user" refers to an individual who utilizes this system, and is the subject of analysis of their entertainment history, preferences, and real-time emotions.

[0180] "History" refers to a record of entertainment activities such as movies, music, and events that a user has accessed in the past, and it forms the basis of preference analysis.

[0181] "Preferences" refer to the trends in a user's preferences based on their past history, and are elements that are analyzed using machine learning techniques.

[0182] "Emotions" refer to the user's current emotional state and are collected through real-time detection using cameras and microphones.

[0183] "Generation" refers to the process of creating optimal entertainment information based on user preferences and emotions, utilizing machine learning and generative models.

[0184] A "terminal" refers to a device used to present entertainment information to a user, and is a device that can be operated through a user interface.

[0185] "Ratings" refer to reviews created by users after they have used a particular form of entertainment, and are automatically generated based on sentiment data and history.

[0186] An "information and communication network" refers to the technological infrastructure that enables terminals to exchange data with servers, and utilizes communication technologies, including the internet.

[0187] A "point system" is a mechanism for gamifying users' entertainment experiences, where points awarded for specific activities are accumulated and can be exchanged for rewards.

[0188] "Notifications" refer to a means of informing users of upcoming leisure activities, enabling them to manage their schedules efficiently.

[0189] This system includes multiple components, such as a server, a device, and an emotion engine, to personalize the user's entertainment experience. The server first collects the user's past entertainment history and stores it in a database. This data collection includes movie viewing history, music playback history, and event attendance history, and uses a specific database management system. The server leverages programming languages ​​such as Python and R, along with machine learning libraries such as Scikit-learn and TENSORFLOW®, to analyze the user's preferences. The results of the preference analysis are stored in the user profile and used to gain a detailed understanding of the user's preference trends.

[0190] Next, the terminal has a function for real-time emotion detection. The terminal uses input devices such as a camera and microphone to analyze the user's facial expressions and voice tone using an emotion engine, and acquires real-time emotion data. Face recognition software and voice analysis technology are used for this analysis. The terminal visually presents content sent from the server to the user through a user interface. This information presentation includes an interface that allows for intuitive selection.

[0191] The server generates content using a generative AI model based on the user's preferences and real-time emotions. In this generation process, an example of a prompt is used: "Generate movies and music that best suit the user's emotions and preferences." The generated content is sent to the device as suggestions for movies, music, and events tailored to the user's mood and preferences.

[0192] Furthermore, after users view selected entertainment through their devices, they can share automatically generated reviews on social media. The review generation process takes into account the user's emotional data during viewing. In this way, the entire system works together to provide personalized entertainment experiences for individual users.

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] The server collects users' past entertainment data. As input, it retrieves information such as movie viewing history, music playback history, and events attended from a database. Based on this input data, it performs data cleaning and normalization to generate a consistent dataset. The refined data is then used for user preference analysis.

[0196] Step 2:

[0197] The server analyzes user preferences using machine learning algorithms based on collected historical data. It uses the dataset from the previous step as input and applies algorithms such as random forest and k-nearest neighbors. This analysis generates a user preference profile as output. This preference profile includes tendencies regarding which types of content users are most interested in.

[0198] Step 3:

[0199] The device uses an emotion engine to detect the user's emotions in real time. It utilizes facial expression data and voice tone acquired through the camera and microphone as input. This data is processed using facial recognition technology and voice analysis to generate an output representing the user's current emotional state.

[0200] Step 4:

[0201] The server integrates the preference profile and sentiment data obtained in the previous step. Here, a generative AI model is used to generate optimal entertainment content based on the data provided as input. An example of a prompt is, "Request entertainment recommendations based on current sentiment and preference data." The output of this generation process provides information on movies, music, and events suitable for the user.

[0202] Step 5:

[0203] The device receives entertainment information sent from the server and displays it through the user interface. Specifically, it visually highlights categories and recommendation levels, generating a list that users can intuitively select from. This makes it easy for users to choose content that interests them.

[0204] Step 6:

[0205] After a user selects and watches entertainment, the device automatically generates a review. The input is a vast amount of viewing log data, including emotional data. This information is analyzed, and a review text reflecting the user's emotional response is generated as output. This review can be shared on social media with the user's permission.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] In users' entertainment activities, it is difficult to suggest content that best suits individual preferences from a diverse range of options. Furthermore, efficiently suggesting content in real time in response to emotional changes, as well as evaluating and sharing experiences, presents challenges. A system is needed to consistently support the improvement and management of the user experience.

[0209] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0210] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences and real-time emotions, and means for generating optimal entertainment information and transmitting it to the user's terminal. This makes it possible to suggest optimal content based on the user's past behavior and current psychological state.

[0211] "Means of collecting a user's past entertainment history" refers to methods of collecting and accumulating data such as movies a user has watched, music they have listened to, and events they have attended in the past.

[0212] "Methods for analyzing user preferences" refer to methods for analyzing the types of content a user likes based on their collected entertainment history.

[0213] "Means for generating entertainment information" refers to methods for constructing information that selects and suggests the most suitable entertainment content to users, based on collected and analyzed data.

[0214] "Means of transmitting information to a user's terminal" refers to the technology of distributing generated entertainment information to the user's electronic device.

[0215] "Methods for analyzing emotions in real time" refer to mechanisms that use the user's facial expressions, tone of voice, etc., to determine the user's current emotional state on the spot.

[0216] "Means to support reservations and purchases" refers to a system that assists users in easily reserving and purchasing their chosen entertainment.

[0217] "Methods for automatically generating reviews" refer to technologies that automatically create reviews based on the user's experience after viewing a product or service.

[0218] "Methods for sharing reviews on social networks" refers to the process of sharing automatically generated reviews on social media via the internet.

[0219] "A means of gamification based on a point system" refers to a mechanism that awards points when users watch or rate specific entertainment, and allows them to exchange those points for rewards or benefits.

[0220] "A way to remind users of their next entertainment plans" is a function that notifies users in advance of their planned entertainment activities, helping them to remember and carry them out.

[0221] The system implementing this invention aims to individually optimize the user's entertainment experience. The system consists of multiple components, including a server, a terminal, and an emotion engine.

[0222] The server comprehensively collects the user's past entertainment history and uses machine learning algorithms to analyze the user's preferences based on this data. The collected data is diverse, including movie viewing history, music playback history, and details of entertainment events attended. Furthermore, it integrates emotional data obtained in real time from the emotion engine to comprehensively understand the user's current emotional state. Based on this, the server generates optimal entertainment content based on the user's preferences and current emotions and sends it to the user's device.

[0223] The device visually presents entertainment information provided by the server to the user. It also features a function that works in conjunction with an emotion engine to analyze the user's facial expressions and tone of voice, recognizing emotions in real time. This allows for more personalized user selections. Once the user selects entertainment of interest from the provided information, the device provides an interface that efficiently assists with the reservation and purchase process.

[0224] After a user's viewing experience, the device automatically generates a review and supports review sharing on social media. It also assists with schedule management by reminding users of their next planned entertainment activity.

[0225] For example, if a user wants to relax that day, the server suggests calming movies and music and sends them to the device. This information is used when the user chooses entertainment and purchases tickets or streaming services. The server and device utilize a generative AI model to provide optimal content in response to requests such as, "If my mood today calls for relaxation, please recommend the best movie content for me."

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The server collects the user's past entertainment history from a database. The input data collected includes details of movies the user has watched, music they have listened to, and events they have attended. This data is output as foundational data for analyzing the user's preferences.

[0229] Step 2:

[0230] The server applies machine learning algorithms to the collected historical data to analyze user preferences. The input for this preference analysis is the entertainment history obtained in the previous step, and the output is a feature vector representing the user's preferences. This feature vector forms the basis for the next content suggestion.

[0231] Step 3:

[0232] The server uses emotion data acquired in real time from the emotion engine as input to determine the user's current emotional state. This emotion data includes the user's facial expressions and voice tone, and calculations are performed based on this data to identify the emotional state, with the result being output.

[0233] Step 4:

[0234] The server integrates the results of preference analysis with real-time sentiment data and utilizes a generative AI model to generate optimal entertainment information. The inputs for this step are feature vectors and sentiment states, and the output is a list of content to be presented to the user.

[0235] Step 5:

[0236] The terminal receives entertainment information sent from the server and presents it to the user through a visual interface. The input is a list of content from the server, and the output is a screen display formatted for easy user understanding.

[0237] Step 6:

[0238] The user selects content of interest from the entertainment information displayed on the device. The selected content is then used as input for the device to assist with the subsequent purchase process.

[0239] Step 7:

[0240] The terminal supports the reservation and purchase process based on the user's selection. It receives the reservation and payment information entered by the user and completes the process by outputting it.

[0241] Step 8:

[0242] The device automatically generates a review after the user's viewing experience is complete and provides a sharing interface for social media. The input for this step is the user's viewing information and sentiment data, and the output is a formatted review text.

[0243] 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.

[0244] Data generation model 58 is a type 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0245] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0250] 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.

[0251] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0252] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0253] 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.

[0254] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0255] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0256] The 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.

[0257] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0258] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] This invention is a system for enriching the entertainment experience and providing users with optimal content. This system primarily consists of the interaction of three parties: a server, a terminal, and a user.

[0260] Server Functions

[0261] The server collects the user's past entertainment history. This history includes titles of movies the user has watched, ratings, details of events they have attended, and genres of music they have listened to. The server analyzes this data to learn the user's preferences. Machine learning algorithms are used for this analysis. The algorithms are responsible for understanding the user's preferences and organizing the data for future recommendations.

[0262] Based on the analysis results, the server generates information on movies, music, and events best suited to the user. This generated information reflects the user's individual preferences and is provided at the appropriate time. This information is sent to the terminal and can be viewed by the user.

[0263] Device functions

[0264] The terminal displays entertainment information sent from the server to the user. This allows the user to easily view the information and select entertainment on their device. For selected entertainment, the terminal supports the reservation and purchase process. Specifically, this includes reserving movie tickets and purchasing tickets for music events.

[0265] Furthermore, after the user has viewed the content, the device automatically generates a review in conjunction with the server. This review is created based on the user's feedback and viewing data, and the user is asked whether or not they wish to share it on social networks.

[0266] Furthermore, the device will remind users of their next entertainment plans. This ensures that users won't forget and can enjoy their next scheduled movie or event.

[0267] User actions

[0268] Users experience a process of selecting content that interests them based on the entertainment information displayed on their device. Once their selection is complete, they can smoothly reserve and purchase content through their device. After viewing, they can review automatically generated reviews from their device and revise them as needed. They can also choose to share their reviews on social networks.

[0269] For example, if a user wants to watch a movie on a holiday, the server analyzes the user's past viewing history and preferences and provides movie recommendations to the device based on that information. The user selects a movie and makes a ticket reservation through the device. After watching the movie, the device automatically generates a review of the film, which the user can then share on social media at their discretion. Furthermore, the device receives a reminder for the next weekend, notifying the user of their next movie viewing schedule.

[0270] As described above, the present invention is a system that more personalizes and consistently supports the user's entertainment experience.

[0271] The following describes the processing flow.

[0272] Step 1:

[0273] The server collects the user's past entertainment history. This history includes movie viewing history, music playback records, event attendance information, and more. The server retrieves this data from a database.

[0274] Step 2:

[0275] The server analyzes the collected data and applies machine learning algorithms to identify user preferences. The algorithms identify the user's preferred genres and artist tendencies, and update the user profile accordingly.

[0276] Step 3:

[0277] Based on the results of the preference analysis, the server generates movie, music, and event information suitable for the user. This information includes details such as recommended viewing or participation dates and times, and evaluations of related works.

[0278] Step 4:

[0279] The server transmits the entertainment information it has generated to the user's terminal. The terminal receives this information and prepares the UI components necessary for display.

[0280] Step 5:

[0281] The terminal displays the entertainment information to the user. The user checks the provided information through the visual interface and selects entertainment based on their interests.

[0282] Step 6:

[0283] The user selects the entertainment they desire and proceeds with the reservation and purchase procedures through the terminal. The terminal provides an interface for entering payment information and transmits that data to the server.

[0284] Step 7:

[0285] The user experiences the entertainment they selected. When the experience ends, the user enters simple feedback through the terminal.

[0286] Step 8:

[0287] The terminal collects the feedback and transmits it to the server. The server automatically generates a review based on this feedback and also uses it as data for re-analyzing the user's preferences.

[0288] Step 9:

[0289] The server sends the generated review to the device. The device then prompts the user to confirm the review and awaits the user's approval before sharing it on social networks.

[0290] Step 10:

[0291] The device uses a feature to remind the user of their next entertainment event. The reminder integrates with the user's calendar and notification system to inform them of the next event at the appropriate time.

[0292] (Example 1)

[0293] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] Traditional entertainment delivery systems have been insufficient in addressing individual user preferences, making it difficult to find the optimal content from countless options. Furthermore, it was challenging to reliably remember the user's planned use of selected content, resulting in a lack of consistency in the user experience.

[0295] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0296] In this invention, the server includes means for collecting the user's past information history, means for analyzing the user's preferences, and means for generating personalized recommendations using a generative AI model. This enables a consistent entertainment experience by efficiently providing content optimized for the user and notifying them of their next scheduled use.

[0297] "Information history" refers to a record of actions a user has taken or content they have used in the past.

[0298] "Preferences" refer to specific tastes or tendencies that a user exhibits based on their past activities.

[0299] "Content information" refers to entertainment-related information provided to users, such as movies, music, and events.

[0300] "Device" refers to a terminal or device used by a user, and is a device capable of receiving and displaying information.

[0301] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate useful information from data.

[0302] A "notification" is a message or alert used to inform a user about upcoming usage schedules or other important information.

[0303] A "computational learning algorithm" is a mathematical method used to clarify patterns and preferences through data analysis.

[0304] This invention is a system in which a server, a terminal, and a user work together to provide the user with the most suitable content. In this system, the server is located in a cloud environment and uses a database to collect the user's past information history. This information history includes the titles and ratings of movies the user has watched, details of events they have attended, and genres of music they have listened to. The collected data is analyzed using machine learning algorithms to identify the user's preferences. This analysis particularly utilizes clustering and collaborative filtering techniques.

[0305] The server generates personalized recommendations using a generative AI model based on the analysis results. For example, it might generate a prompt like, "You will enjoy this movie: 'Mystery Adventure' - featuring a captivating plot and diverse cast." This generated information is then sent to the terminal via the network.

[0306] The terminal organizes the content information sent from the server and displays it in a user-friendly form. The user can use the terminal to easily select the content they are interested in and make reservations or purchases. For the selected content, the terminal can automatically generate a review and send it to the server to be used for subsequent analysis.

[0307] In addition, the terminal also has a function to notify the next content schedule. This allows the user to enjoy the next entertainment schedule without forgetting.

[0308] By using this system, the user can receive content recommendations based on their individual preferences and obtain a richer entertainment experience.

[0309] The flow of the specific process in Example 1 will be described using FIG. 11.

[0310] Step 1:

[0311] The server collects the past information history sent from the user's terminal in the cloud database. The inputs include the title, evaluation, category, etc. of the content viewed by the user. The data is collected in real time and saved for later analysis. The output is an information history organized in time series.

[0312] Step 2:

[0313] The server analyzes the user's preferences using the collected information history. This analysis is performed by machine learning algorithms, and clustering and collaborative filtering techniques are utilized. The input is the collected information history, and the output is the user's preference pattern. As a specific operation, the server constructs a preference model specialized for each user while referring to the data of other users with similar preferences.

[0314] Step 3:

[0315] The server generates personalized content recommendations using a generative AI model based on the analysis results. The input is the user's preference pattern, and the output is the recommendation text. Specifically, the server creates prompt texts such as "You will enjoy this movie: 'Mystery Adventure'" by filling in appropriate content for a template.

[0316] Step 4:

[0317] Content information generated from the server is sent to the device, which then displays it. The input is the content information from the server, and the output is the information visually presented to the user. The device uses a notification function to display new content on the dashboard, allowing the user to easily check it.

[0318] Step 5:

[0319] Users select items of interest from the content information displayed on the terminal and make reservations or purchases. The input is the content information presented on the terminal, and the output is reservation confirmation information for the selected content. The terminal integrates with the payment system, allowing users to complete the purchase process with a single click.

[0320] Step 6:

[0321] After viewing, the device collects user feedback and automatically generates an evaluation. The input is user feedback, and the output is a calculated evaluation statement. The device sends the evaluation to a server and stores it in a database for future analysis.

[0322] Step 7:

[0323] The device reminds the user of their next scheduled content usage. The input is schedule information from the server, and the output is a notification message to the user. This allows users to plan their daily enjoyment without forgetting their next entertainment event.

[0324] (Application Example 1)

[0325] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0326] When users enjoy entertainment, there are challenges in ensuring they can choose the best content from a diverse range of options, watch it smoothly, and easily share reviews. Furthermore, there is a need to improve entertainment satisfaction by providing personalized experiences based on user preferences.

[0327] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0328] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, means for generating entertainment information to suggest to the user, means for transmitting the generated information to the user's terminal, means for supporting the reservation and purchase of entertainment selected by the user, means for automatically generating reviews after the user has viewed the content and sharing them on social networks, means for gamifying the user's entertainment experience based on a point system, means for reminding the user of their next entertainment appointment, means for instantly playing the content on the terminal, and means for enabling the user to review, modify, and share reviews. This enables personalized suggestions that reflect the user's preferences and allows for an efficient execution of the entire process from content selection to viewing and review sharing.

[0329] "Means of collecting a user's past entertainment history" refers to a function that collects data about entertainment content that a user has previously viewed or participated in.

[0330] "Means for analyzing user preferences" refers to analytical functions that identify user preferences and trends based on collected historical data.

[0331] "Means for generating entertainment information to suggest to users" refers to a function that presents users with suitable entertainment options based on analysis results.

[0332] "Means for transmitting generated information to the user's terminal" refers to a communication function for delivering proposed entertainment information to the user's device.

[0333] "Means to support the reservation and purchase of entertainment selected by the user" refers to support functions that facilitate the reservation and purchase of content chosen by the user.

[0334] "A means of automatically generating reviews after a user has viewed a video" refers to a function that automatically creates a review of the content after the user has finished watching it.

[0335] "Means of sharing on social networks" refers to communication and transmission functions that enable the generated reviews to be shared with others online.

[0336] "A means of gamifying the user's entertainment experience based on a point system" refers to a function that awards points as an incentive for entertainment activities and incorporates game elements to improve the user's experience.

[0337] "A way to remind users of upcoming entertainment events" is a feature that notifies users of their next entertainment event and encourages them to check their schedule so they don't forget.

[0338] "Means for instantly playing content on a device" refers to a function that allows selected content to be immediately streamed on the user's device.

[0339] "Means that enable users to review, revise, and share reviews" refers to features that help users review the content of automatically generated reviews, edit them as needed, and then share them with others again.

[0340] This invention is a system for personalizing and efficiently supporting users' entertainment experiences. This system is primarily built on the interaction between a server, a terminal, and the user.

[0341] The server collects the user's past entertainment history, including data such as movies watched, events attended, and music listened to. Based on this data, the server uses machine learning algorithms (e.g., scikit-learn) to analyze the user's preferences. This analysis helps understand the user's tendencies and is used to suggest appropriate content. The suggested entertainment information reflects the user's individual preferences and can be played immediately using the streaming capabilities of external services.

[0342] The device is responsible for presenting entertainment information sent from the server to the user. Once the user selects content, the device supports its reservation and purchase, and immediately plays the selected content. Furthermore, after viewing, a review is automatically generated using a natural language processing library (e.g., spaCy), which the user can review, modify as needed, and share on social networks.

[0343] Users select content based on the information displayed on their device and enjoy streaming playback. After watching, they can review automatically generated reviews and choose whether or not to share them. This allows users to maximize their entertainment experience.

[0344] For example, if a user wants to watch a movie on their day off, the server will display recommended movies based on their past viewing history and analysis results. The user can select a movie and play it instantly on their device. Afterwards, a review is generated, and if they want to share it with friends, they can easily adjust the sharing settings.

[0345] An example of a prompt to a generative AI model would be, "I'm looking for a relaxing movie to watch on my day off. What movies do you think are good these days?" This would allow the model to receive content suggestions in this format.

[0346] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0347] Step 1:

[0348] The server collects the user's past entertainment history from a database. This data includes information such as movies the user has watched, events they have attended, and music they have listened to. This data is collected as input to prepare a basic dataset for analysis.

[0349] Step 2:

[0350] The server analyzes the collected data using machine learning algorithms (e.g., scikit-learn) to model user preferences. Here, user viewing trends and preferences are extracted as patterns, and basic information for personalized content recommendations is output. This process involves data analysis and calculations, resulting in the generation of a user preference profile.

[0351] Step 3:

[0352] Based on the generated preference profile, the server selects the most suitable entertainment content for the user and creates a recommendation list. This list contains information about the content to be suggested and is temporarily stored for transmission to the device.

[0353] Step 4:

[0354] The terminal receives a list of recommendations sent from the server and presents it visually to the user. The user can select content of interest from this list. The terminal processes this received data and displays it as options on a GUI.

[0355] Step 5:

[0356] The user selects content they are interested in on their device. This selection serves as input, and the device supports the reservation and purchase process for the specified entertainment content. The data processing involved here includes receiving the selection information and creating a reservation completion notification based on that information.

[0357] Step 6:

[0358] The device immediately streams the selected content. Here, an API from an external streaming service is used to obtain a link for real-time playback of the selected movie or music, and playback begins on the user's device.

[0359] Step 7:

[0360] Once a user finishes viewing content, the device automatically generates a review using a natural language processing library (e.g., spaCy) based on the data collected during viewing. This generation process analyzes the content viewed and the user's reactions, and then outputs a review statement.

[0361] Step 8:

[0362] Users can review the generated review and revise it if necessary. After revision, the review can be shared on social networks. The device will follow the user's instructions to save and submit the review.

[0363] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0364] This invention provides a system that comprehensively supports users' entertainment experiences and enables the delivery of optimal content based on individual preferences and emotions. This system consists of multiple components, including a server, terminals, and an emotion engine.

[0365] Server Functions

[0366] The server collects the user's past entertainment history and analyzes their preferences using machine learning algorithms. The collected data includes movie viewing history, music playback history, and details of events attended. Furthermore, the server integrates emotional data from an emotion engine to understand the user's real-time emotional state.

[0367] This allows the server to generate optimal entertainment information based on the user's preferences and emotional state. This information includes details on recommended movies and events, suggested music playlists, and more, all customized to the user's current mood.

[0368] Device functions

[0369] The device presents entertainment information sent from the server to the user and makes it available through a visual interface. A key role here is that the device works in conjunction with an emotion engine to detect the user's emotions in real time from their facial expressions and tone of voice.

[0370] When a user selects entertainment information they are interested in, the device supports reservation and purchase functions, allowing for easy completion of the necessary procedures. It also provides an interface for receiving payment information, ensuring a smooth user experience.

[0371] Furthermore, after viewing, the device automatically generates a review and supports the process of sharing it on social networks. It also has a function to remind users of their next entertainment plans, allowing for efficient schedule management.

[0372] User actions

[0373] Users select content that interests them from the entertainment information displayed on their device, based on their emotions and preferences. During this process, an emotion engine analyzes the user's emotions in real time and suggests the most appropriate options based on their current state of mind.

[0374] For example, if a user wants to relax, the server suggests calming movies and music and sends them to the device. After the user selects a movie and books tickets, the emotion engine generates a further customized review based on the user's emotions while watching. This review may be posted to social media via the device.

[0375] As described above, the present invention is a system that personalizes entertainment based on the user's emotions and preferences, providing a consistent user experience from booking and review creation to managing future appointments.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The server collects the user's past entertainment history. This history data includes records of movies the user has watched, music they have listened to, and events they have attended. The server retrieves this history information from a database.

[0379] Step 2:

[0380] The server works in conjunction with the emotion engine to detect the user's emotions in real time. Facial expression and voice data from when the user is using the device are analyzed through the emotion engine to recognize their emotional state. This information is then sent to the server.

[0381] Step 3:

[0382] Based on historical data collected by the server and real-time emotional states, a machine learning algorithm is used to analyze user preferences. The algorithm considers the user's current emotions and past preferences to suggest appropriate entertainment content.

[0383] Step 4:

[0384] The server generates entertainment recommendations for the user. This information includes suggestions for movies, music, and events that take into account the user's current mood. The generated information is sent to the terminal.

[0385] Step 5:

[0386] The device displays entertainment information received from the server to the user. The user can review and select the suggested content through the interface.

[0387] Step 6:

[0388] The device supports the reservation and purchase of entertainment content selected by the user. The device provides a form for entering necessary information and manages the reservation and payment process.

[0389] Step 7:

[0390] The user experiences the entertainment they selected. After the experience ends, the device re-analyzes the changes in their emotional state through an emotion engine. This analysis is used to evaluate the user's overall experience.

[0391] Step 8:

[0392] The device sends feedback to the server. The server automatically generates a review based on the feedback. The review is customized based on the user's emotional state and preferences.

[0393] Step 9:

[0394] The server generates a review and sends it to the device. The device then allows the user to review the review, make any necessary corrections, and then suggests sharing it on social networks.

[0395] Step 10:

[0396] The device activates a feature that reminds you of your next entertainment event. The reminder notifies the user of information about their next scheduled entertainment activity.

[0397] (Example 2)

[0398] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0399] Traditional entertainment systems have faced challenges in providing highly satisfying entertainment experiences because they struggle to personalize them to adequately consider user preferences and emotions. Furthermore, they have difficulty consistently managing the user experience, resulting in problems with the smooth operation of everything from selecting, booking, and purchasing entertainment to sharing reviews.

[0400] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0401] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, and means for detecting the user's real-time emotions. This makes it possible to generate optimal entertainment information based on the user's preferences and emotions and transmit it to the user terminal, thereby providing a consistent and personalized entertainment experience and improving user satisfaction.

[0402] A "user" refers to an individual who utilizes this system, and is the subject of analysis of their entertainment history, preferences, and real-time emotions.

[0403] "History" refers to a record of entertainment activities such as movies, music, and events that a user has accessed in the past, and it forms the basis of preference analysis.

[0404] "Preferences" refer to the trends in a user's preferences based on their past history, and are elements that are analyzed using machine learning techniques.

[0405] "Emotions" refer to the user's current emotional state and are collected through real-time detection using cameras and microphones.

[0406] "Generation" refers to the process of creating optimal entertainment information based on user preferences and emotions, utilizing machine learning and generative models.

[0407] A "terminal" refers to a device used to present entertainment information to a user, and is a device that can be operated through a user interface.

[0408] "Ratings" refer to reviews created by users after they have used a particular form of entertainment, and are automatically generated based on sentiment data and history.

[0409] An "information and communication network" refers to the technological infrastructure that enables terminals to exchange data with servers, and utilizes communication technologies, including the internet.

[0410] A "point system" is a mechanism for gamifying users' entertainment experiences, where points awarded for specific activities are accumulated and can be exchanged for rewards.

[0411] "Notifications" refer to a means of informing users of upcoming leisure activities, enabling them to manage their schedules efficiently.

[0412] This system includes multiple components—a server, a device, and an emotion engine—to personalize the user's entertainment experience. The server first collects the user's past entertainment history and stores it in a database. This data collection includes movie viewing history, music playback history, and event attendance history, and uses a specific database management system. The server leverages programming languages ​​such as Python and R, along with machine learning libraries like Scikit-learn and TensorFlow, to analyze user preferences. The results of this preference analysis are stored in a user profile and used to gain a detailed understanding of the user's preferences.

[0413] Next, the terminal has a function for real-time emotion detection. The terminal uses input devices such as a camera and microphone to analyze the user's facial expressions and voice tone using an emotion engine, and acquires real-time emotion data. Face recognition software and voice analysis technology are used for this analysis. The terminal visually presents content sent from the server to the user through a user interface. This information presentation includes an interface that allows for intuitive selection.

[0414] The server generates content using a generative AI model based on the user's preferences and real-time emotions. In this generation process, an example of a prompt is used: "Generate movies and music that best suit the user's emotions and preferences." The generated content is sent to the device as suggestions for movies, music, and events tailored to the user's mood and preferences.

[0415] Furthermore, after users view selected entertainment through their devices, they can share automatically generated reviews on social media. The review generation process takes into account the user's emotional data during viewing. In this way, the entire system works together to provide personalized entertainment experiences for individual users.

[0416] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0417] Step 1:

[0418] The server collects users' past entertainment data. As input, it retrieves information such as movie viewing history, music playback history, and events attended from a database. Based on this input data, it performs data cleaning and normalization to generate a consistent dataset. The refined data is then used for user preference analysis.

[0419] Step 2:

[0420] The server analyzes user preferences using machine learning algorithms based on collected historical data. It uses the dataset from the previous step as input and applies algorithms such as random forest and k-nearest neighbors. This analysis generates a user preference profile as output. This preference profile includes tendencies regarding which types of content users are most interested in.

[0421] Step 3:

[0422] The device uses an emotion engine to detect the user's emotions in real time. It utilizes facial expression data and voice tone acquired through the camera and microphone as input. This data is processed using facial recognition technology and voice analysis to generate an output representing the user's current emotional state.

[0423] Step 4:

[0424] The server integrates the preference profile and sentiment data obtained in the previous step. Here, a generative AI model is used to generate optimal entertainment content based on the data provided as input. An example of a prompt is, "Request entertainment recommendations based on current sentiment and preference data." The output of this generation process provides information on movies, music, and events suitable for the user.

[0425] Step 5:

[0426] The device receives entertainment information sent from the server and displays it through the user interface. Specifically, it visually highlights categories and recommendation levels, generating a list that users can intuitively select from. This makes it easy for users to choose content that interests them.

[0427] Step 6:

[0428] After a user selects and watches entertainment, the device automatically generates a review. The input is a vast amount of viewing log data, including emotional data. This information is analyzed, and a review text reflecting the user's emotional response is generated as output. This review can be shared on social media with the user's permission.

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0431] In users' entertainment activities, it is difficult to suggest content that best suits individual preferences from a diverse range of options. Furthermore, efficiently suggesting content in real time in response to emotional changes, as well as evaluating and sharing experiences, presents challenges. A system is needed to consistently support the improvement and management of the user experience.

[0432] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0433] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences and real-time emotions, and means for generating optimal entertainment information and transmitting it to the user's terminal. This makes it possible to suggest optimal content based on the user's past behavior and current psychological state.

[0434] "Means of collecting a user's past entertainment history" refers to methods of collecting and accumulating data such as movies a user has watched, music they have listened to, and events they have attended in the past.

[0435] "Methods for analyzing user preferences" refer to methods for analyzing the types of content a user likes based on their collected entertainment history.

[0436] "Means for generating entertainment information" refers to methods for constructing information that selects and suggests the most suitable entertainment content to users, based on collected and analyzed data.

[0437] "Means of transmitting information to a user's terminal" refers to the technology of distributing generated entertainment information to the user's electronic device.

[0438] "Methods for analyzing emotions in real time" refer to mechanisms that use the user's facial expressions, tone of voice, etc., to determine the user's current emotional state on the spot.

[0439] "Means to support reservations and purchases" refers to a system that assists users in easily reserving and purchasing their chosen entertainment.

[0440] "Methods for automatically generating reviews" refer to technologies that automatically create reviews based on the user's experience after viewing a product or service.

[0441] "Methods for sharing reviews on social networks" refers to the process of sharing automatically generated reviews on social media via the internet.

[0442] "A means of gamification based on a point system" refers to a mechanism that awards points when users watch or rate specific entertainment, and allows them to exchange those points for rewards or benefits.

[0443] "A way to remind users of their next entertainment plans" is a function that notifies users in advance of their planned entertainment activities, helping them to remember and carry them out.

[0444] The system implementing this invention aims to individually optimize the user's entertainment experience. The system consists of multiple components, including a server, a terminal, and an emotion engine.

[0445] The server comprehensively collects the user's past entertainment history and uses machine learning algorithms to analyze the user's preferences based on this data. The collected data is diverse, including movie viewing history, music playback history, and details of entertainment events attended. Furthermore, it integrates emotional data obtained in real time from the emotion engine to comprehensively understand the user's current emotional state. Based on this, the server generates optimal entertainment content based on the user's preferences and current emotions and sends it to the user's device.

[0446] The device visually presents entertainment information provided by the server to the user. It also features a function that works in conjunction with an emotion engine to analyze the user's facial expressions and tone of voice, recognizing emotions in real time. This allows for more personalized user selections. Once the user selects entertainment of interest from the provided information, the device provides an interface that efficiently assists with the reservation and purchase process.

[0447] After a user's viewing experience, the device automatically generates a review and supports review sharing on social media. It also assists with schedule management by reminding users of their next planned entertainment activity.

[0448] For example, if a user wants to relax that day, the server suggests calming movies and music and sends them to the device. This information is used when the user chooses entertainment and purchases tickets or streaming services. The server and device utilize a generative AI model to provide optimal content in response to requests such as, "If my mood today calls for relaxation, please recommend the best movie content for me."

[0449] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0450] Step 1:

[0451] The server collects the user's past entertainment history from a database. The input data collected includes details of movies the user has watched, music they have listened to, and events they have attended. This data is output as foundational data for analyzing the user's preferences.

[0452] Step 2:

[0453] The server applies machine learning algorithms to the collected historical data to analyze user preferences. The input for this preference analysis is the entertainment history obtained in the previous step, and the output is a feature vector representing the user's preferences. This feature vector forms the basis for the next content suggestion.

[0454] Step 3:

[0455] The server uses emotion data acquired in real time from the emotion engine as input to determine the user's current emotional state. This emotion data includes the user's facial expressions and voice tone, and calculations are performed based on this data to identify the emotional state, with the result being output.

[0456] Step 4:

[0457] The server integrates the results of preference analysis with real-time sentiment data and utilizes a generative AI model to generate optimal entertainment information. The inputs for this step are feature vectors and sentiment states, and the output is a list of content to be presented to the user.

[0458] Step 5:

[0459] The terminal receives entertainment information sent from the server and presents it to the user through a visual interface. The input is a list of content from the server, and the output is a screen display formatted for easy user understanding.

[0460] Step 6:

[0461] The user selects content of interest from the entertainment information displayed on the device. The selected content is then used as input for the device to assist with the subsequent purchase process.

[0462] Step 7:

[0463] The terminal supports the reservation and purchase process based on the user's selection. It receives the reservation and payment information entered by the user and completes the process by outputting it.

[0464] Step 8:

[0465] The device automatically generates a review after the user's viewing experience is complete and provides a sharing interface for social media. The input for this step is the user's viewing information and sentiment data, and the output is a formatted review text.

[0466] 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.

[0467] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0468] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0469] [Third Embodiment]

[0470] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0471] 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.

[0472] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0473] 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.

[0474] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0475] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0476] 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.

[0477] 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.

[0478] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0479] The 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.

[0480] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0481] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0482] This invention is a system for enriching the entertainment experience and providing users with optimal content. This system primarily consists of the interaction of three parties: a server, a terminal, and a user.

[0483] Server Functions

[0484] The server collects the user's past entertainment history. This history includes titles of movies the user has watched, ratings, details of events they have attended, and genres of music they have listened to. The server analyzes this data to learn the user's preferences. Machine learning algorithms are used for this analysis. The algorithms are responsible for understanding the user's preferences and organizing the data for future recommendations.

[0485] Based on the analysis results, the server generates information on movies, music, and events best suited to the user. This generated information reflects the user's individual preferences and is provided at the appropriate time. This information is sent to the terminal and can be viewed by the user.

[0486] Device functions

[0487] The terminal displays entertainment information sent from the server to the user. This allows the user to easily view the information and select entertainment on their device. For selected entertainment, the terminal supports the reservation and purchase process. Specifically, this includes reserving movie tickets and purchasing tickets for music events.

[0488] Furthermore, after the user has viewed the content, the device automatically generates a review in conjunction with the server. This review is created based on the user's feedback and viewing data, and the user is asked whether or not they wish to share it on social networks.

[0489] Furthermore, the device will remind users of their next entertainment plans. This ensures that users won't forget and can enjoy their next scheduled movie or event.

[0490] User actions

[0491] Users experience a process of selecting content that interests them based on the entertainment information displayed on their device. Once their selection is complete, they can smoothly reserve and purchase content through their device. After viewing, they can review automatically generated reviews from their device and revise them as needed. They can also choose to share their reviews on social networks.

[0492] For example, if a user wants to watch a movie on a holiday, the server analyzes the user's past viewing history and preferences and provides movie recommendations to the device based on that information. The user selects a movie and makes a ticket reservation through the device. After watching the movie, the device automatically generates a review of the film, which the user can then share on social media at their discretion. Furthermore, the device receives a reminder for the next weekend, notifying the user of their next movie viewing schedule.

[0493] As described above, the present invention is a system that more personalizes and consistently supports the user's entertainment experience.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server collects the user's past entertainment history. This history includes movie viewing history, music playback records, event attendance information, and more. The server retrieves this data from a database.

[0497] Step 2:

[0498] The server analyzes the collected data and applies machine learning algorithms to identify user preferences. The algorithms identify the user's preferred genres and artist tendencies, and update the user profile accordingly.

[0499] Step 3:

[0500] The server generates movie, music, and event information tailored to the user based on the results of preference analysis. This information includes details such as recommended viewing and participation times, and ratings of related works.

[0501] Step 4:

[0502] The server generates entertainment information and sends it to the user's device. The device receives this information and prepares the necessary UI components for display.

[0503] Step 5:

[0504] The device displays entertainment information to the user. The user reviews the provided information through a visual interface and selects entertainment based on their interests.

[0505] Step 6:

[0506] The user selects their desired entertainment and proceeds with the reservation and purchase process through the terminal. The terminal provides an interface for entering payment information and transmits that data to the server.

[0507] Step 7:

[0508] The user experiences the entertainment of their choice. Once the experience is complete, the user provides brief feedback through their device.

[0509] Step 8:

[0510] The device collects feedback and sends it to the server. The server automatically generates reviews based on this feedback. It also uses this data to reanalyze user preferences.

[0511] Step 9:

[0512] The server sends the generated review to the device. The device then prompts the user to confirm the review and awaits the user's approval before sharing it on social networks.

[0513] Step 10:

[0514] The device uses a feature to remind the user of their next entertainment event. The reminder integrates with the user's calendar and notification system to inform them of the next event at the appropriate time.

[0515] (Example 1)

[0516] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0517] Traditional entertainment delivery systems have been insufficient in addressing individual user preferences, making it difficult to find the optimal content from countless options. Furthermore, it was challenging to reliably remember the user's planned use of selected content, resulting in a lack of consistency in the user experience.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0519] In this invention, the server includes means for collecting the user's past information history, means for analyzing the user's preferences, and means for generating personalized recommendations using a generative AI model. This enables a consistent entertainment experience by efficiently providing content optimized for the user and notifying them of their next scheduled use.

[0520] "Information history" refers to a record of actions a user has taken or content they have used in the past.

[0521] "Preferences" refer to specific tastes or tendencies that a user exhibits based on their past activities.

[0522] "Content information" refers to entertainment-related information provided to users, such as movies, music, and events.

[0523] "Device" refers to a terminal or device used by a user, and is a device capable of receiving and displaying information.

[0524] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate useful information from data.

[0525] A "notification" is a message or alert used to inform a user about upcoming usage schedules or other important information.

[0526] A "computational learning algorithm" is a mathematical method used to clarify patterns and preferences through data analysis.

[0527] This invention is a system in which a server, a terminal, and a user work together to provide the user with the most suitable content. In this system, the server is located in a cloud environment and uses a database to collect the user's past information history. This information history includes the titles and ratings of movies the user has watched, details of events they have attended, and genres of music they have listened to. The collected data is analyzed using machine learning algorithms to identify the user's preferences. This analysis particularly utilizes clustering and collaborative filtering techniques.

[0528] The server generates personalized recommendations using a generative AI model based on the analysis results. For example, it might generate a prompt like, "You will enjoy this movie: 'Mystery Adventure' - featuring a captivating plot and diverse cast." This generated information is then sent to the terminal via the network.

[0529] The device organizes content information sent from the server and displays it in a user-friendly format. Users can easily select content of interest and reserve or purchase it using the device. For selected content, the device automatically generates reviews and sends them to the server for further analysis.

[0530] The device also has a feature that notifies users of upcoming content schedules. This allows users to enjoy their next entertainment without forgetting.

[0531] By using this system, users can receive content suggestions based on their individual preferences, leading to a richer entertainment experience.

[0532] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0533] Step 1:

[0534] The server collects historical information transmitted from the user's device into a cloud database. Inputs include the titles, ratings, and categories of content the user has viewed. The data is collected in real time and stored for later analysis. The output is a chronologically organized history of the information.

[0535] Step 2:

[0536] The server analyzes user preferences using collected information history. This analysis is performed using machine learning algorithms, employing clustering and collaborative filtering techniques. The input is the collected information history, and the output is the user's preference pattern. Specifically, the server builds a preference model tailored to each individual user, while also referencing data from other users with similar preferences.

[0537] Step 3:

[0538] The server generates personalized content recommendations using a generative AI model based on the analysis results. The input is the user's preference pattern, and the output is the recommendation text. Specifically, the server creates prompt texts such as "You will enjoy this movie: 'Mystery Adventure'" by filling in appropriate content for a template.

[0539] Step 4:

[0540] Content information generated from the server is sent to the device, which then displays it. The input is the content information from the server, and the output is the information visually presented to the user. The device uses a notification function to display new content on the dashboard, allowing the user to easily check it.

[0541] Step 5:

[0542] Users select items of interest from the content information displayed on the terminal and make reservations or purchases. The input is the content information presented on the terminal, and the output is reservation confirmation information for the selected content. The terminal integrates with the payment system, allowing users to complete the purchase process with a single click.

[0543] Step 6:

[0544] After viewing, the device collects user feedback and automatically generates an evaluation. The input is user feedback, and the output is a calculated evaluation statement. The device sends the evaluation to a server and stores it in a database for future analysis.

[0545] Step 7:

[0546] The device reminds the user of their next scheduled content usage. The input is schedule information from the server, and the output is a notification message to the user. This allows users to plan their daily enjoyment without forgetting their next entertainment event.

[0547] (Application Example 1)

[0548] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] When users enjoy entertainment, there are challenges in ensuring they can choose the best content from a diverse range of options, watch it smoothly, and easily share reviews. Furthermore, there is a need to improve entertainment satisfaction by providing personalized experiences based on user preferences.

[0550] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0551] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, means for generating entertainment information to suggest to the user, means for transmitting the generated information to the user's terminal, means for supporting the reservation and purchase of entertainment selected by the user, means for automatically generating reviews after the user has viewed the content and sharing them on social networks, means for gamifying the user's entertainment experience based on a point system, means for reminding the user of their next entertainment appointment, means for instantly playing the content on the terminal, and means for enabling the user to review, modify, and share reviews. This enables personalized suggestions that reflect the user's preferences and allows for an efficient execution of the entire process from content selection to viewing and review sharing.

[0552] "Means of collecting a user's past entertainment history" refers to a function that collects data about entertainment content that a user has previously viewed or participated in.

[0553] "Means for analyzing user preferences" refers to analytical functions that identify user preferences and trends based on collected historical data.

[0554] "Means for generating entertainment information to suggest to users" refers to a function that presents users with suitable entertainment options based on analysis results.

[0555] "Means for transmitting generated information to the user's terminal" refers to a communication function for delivering proposed entertainment information to the user's device.

[0556] "Means to support the reservation and purchase of entertainment selected by the user" refers to support functions that facilitate the reservation and purchase of content chosen by the user.

[0557] "A means of automatically generating reviews after a user has viewed a video" refers to a function that automatically creates a review of the content after the user has finished watching it.

[0558] "Means of sharing on social networks" refers to communication and transmission functions that enable the generated reviews to be shared with others online.

[0559] "A means of gamifying the user's entertainment experience based on a point system" refers to a function that awards points as an incentive for entertainment activities and incorporates game elements to improve the user's experience.

[0560] "A way to remind users of upcoming entertainment events" is a feature that notifies users of their next entertainment event and encourages them to check their schedule so they don't forget.

[0561] "Means for instantly playing content on a device" refers to a function that allows selected content to be immediately streamed on the user's device.

[0562] "Means that enable users to review, revise, and share reviews" refers to features that help users review the content of automatically generated reviews, edit them as needed, and then share them with others again.

[0563] This invention is a system for personalizing and efficiently supporting users' entertainment experiences. This system is primarily built on the interaction between a server, a terminal, and the user.

[0564] The server collects the user's past entertainment history, including data such as movies watched, events attended, and music listened to. Based on this data, the server uses machine learning algorithms (e.g., scikit-learn) to analyze the user's preferences. This analysis helps understand the user's tendencies and is used to suggest appropriate content. The suggested entertainment information reflects the user's individual preferences and can be played immediately using the streaming capabilities of external services.

[0565] The device is responsible for presenting entertainment information sent from the server to the user. Once the user selects content, the device supports its reservation and purchase, and immediately plays the selected content. Furthermore, after viewing, a review is automatically generated using a natural language processing library (e.g., spaCy), which the user can review, modify as needed, and share on social networks.

[0566] Users select content based on the information displayed on their device and enjoy streaming playback. After watching, they can review automatically generated reviews and choose whether or not to share them. This allows users to maximize their entertainment experience.

[0567] For example, if a user wants to watch a movie on their day off, the server will display recommended movies based on their past viewing history and analysis results. The user can select a movie and play it instantly on their device. Afterwards, a review is generated, and if they want to share it with friends, they can easily adjust the sharing settings.

[0568] An example of a prompt to a generative AI model would be, "I'm looking for a relaxing movie to watch on my day off. What movies do you think are good these days?" This would allow the model to receive content suggestions in this format.

[0569] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0570] Step 1:

[0571] The server collects the user's past entertainment history from a database. This data includes information such as movies the user has watched, events they have attended, and music they have listened to. This data is collected as input to prepare a basic dataset for analysis.

[0572] Step 2:

[0573] The server analyzes the collected data using machine learning algorithms (e.g., scikit-learn) to model user preferences. Here, user viewing trends and preferences are extracted as patterns, and basic information for personalized content recommendations is output. This process involves data analysis and calculations, resulting in the generation of a user preference profile.

[0574] Step 3:

[0575] Based on the generated preference profile, the server selects the most suitable entertainment content for the user and creates a recommendation list. This list contains information about the content to be suggested and is temporarily stored for transmission to the device.

[0576] Step 4:

[0577] The terminal receives a list of recommendations sent from the server and presents it visually to the user. The user can select content of interest from this list. The terminal processes this received data and displays it as options on a GUI.

[0578] Step 5:

[0579] The user selects content they are interested in on their device. This selection serves as input, and the device supports the reservation and purchase process for the specified entertainment content. The data processing involved here includes receiving the selection information and creating a reservation completion notification based on that information.

[0580] Step 6:

[0581] The device immediately streams the selected content. Here, an API from an external streaming service is used to obtain a link for real-time playback of the selected movie or music, and playback begins on the user's device.

[0582] Step 7:

[0583] Once a user finishes viewing content, the device automatically generates a review using a natural language processing library (e.g., spaCy) based on the data collected during viewing. This generation process analyzes the content viewed and the user's reactions, and then outputs a review statement.

[0584] Step 8:

[0585] Users can review the generated review and revise it if necessary. After revision, the review can be shared on social networks. The device will follow the user's instructions to save and submit the review.

[0586] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0587] This invention provides a system that comprehensively supports users' entertainment experiences and enables the delivery of optimal content based on individual preferences and emotions. This system consists of multiple components, including a server, terminals, and an emotion engine.

[0588] Server Functions

[0589] The server collects the user's past entertainment history and analyzes their preferences using machine learning algorithms. The collected data includes movie viewing history, music playback history, and details of events attended. Furthermore, the server integrates emotional data from an emotion engine to understand the user's real-time emotional state.

[0590] This allows the server to generate optimal entertainment information based on the user's preferences and emotional state. This information includes details on recommended movies and events, suggested music playlists, and more, all customized to the user's current mood.

[0591] Device functions

[0592] The device presents entertainment information sent from the server to the user and makes it available through a visual interface. A key role here is that the device works in conjunction with an emotion engine to detect the user's emotions in real time from their facial expressions and tone of voice.

[0593] When a user selects entertainment information they are interested in, the device supports reservation and purchase functions, allowing for easy completion of the necessary procedures. It also provides an interface for receiving payment information, ensuring a smooth user experience.

[0594] Furthermore, after viewing, the device automatically generates a review and supports the process of sharing it on social networks. It also has a function to remind users of their next entertainment plans, allowing for efficient schedule management.

[0595] User actions

[0596] Users select content that interests them from the entertainment information displayed on their device, based on their emotions and preferences. During this process, an emotion engine analyzes the user's emotions in real time and suggests the most appropriate options based on their current state of mind.

[0597] For example, if a user wants to relax, the server suggests calming movies and music and sends them to the device. After the user selects a movie and books tickets, the emotion engine generates a further customized review based on the user's emotions while watching. This review may be posted to social media via the device.

[0598] As described above, the present invention is a system that personalizes entertainment based on the user's emotions and preferences, providing a consistent user experience from booking and review creation to managing future appointments.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The server collects the user's past entertainment history. This history data includes records of movies the user has watched, music they have listened to, and events they have attended. The server retrieves this history information from a database.

[0602] Step 2:

[0603] The server works in conjunction with the emotion engine to detect the user's emotions in real time. Facial expression and voice data from when the user is using the device are analyzed through the emotion engine to recognize their emotional state. This information is then sent to the server.

[0604] Step 3:

[0605] Based on historical data collected by the server and real-time emotional states, a machine learning algorithm is used to analyze user preferences. The algorithm considers the user's current emotions and past preferences to suggest appropriate entertainment content.

[0606] Step 4:

[0607] The server generates entertainment recommendations for the user. This information includes suggestions for movies, music, and events that take into account the user's current mood. The generated information is sent to the terminal.

[0608] Step 5:

[0609] The device displays entertainment information received from the server to the user. The user can review and select the suggested content through the interface.

[0610] Step 6:

[0611] The device supports the reservation and purchase of entertainment content selected by the user. The device provides a form for entering necessary information and manages the reservation and payment process.

[0612] Step 7:

[0613] The user experiences the entertainment they selected. After the experience ends, the device re-analyzes the changes in their emotional state through an emotion engine. This analysis is used to evaluate the user's overall experience.

[0614] Step 8:

[0615] The device sends feedback to the server. The server automatically generates a review based on the feedback. The review is customized based on the user's emotional state and preferences.

[0616] Step 9:

[0617] The server generates a review and sends it to the device. The device then allows the user to review the review, make any necessary corrections, and then suggests sharing it on social networks.

[0618] Step 10:

[0619] The device activates a feature that reminds you of your next entertainment event. The reminder notifies the user of information about their next scheduled entertainment activity.

[0620] (Example 2)

[0621] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0622] Traditional entertainment systems have faced challenges in providing highly satisfying entertainment experiences because they struggle to personalize them to adequately consider user preferences and emotions. Furthermore, they have difficulty consistently managing the user experience, resulting in problems with the smooth operation of everything from selecting, booking, and purchasing entertainment to sharing reviews.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0624] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, and means for detecting the user's real-time emotions. This makes it possible to generate optimal entertainment information based on the user's preferences and emotions and transmit it to the user terminal, thereby providing a consistent and personalized entertainment experience and improving user satisfaction.

[0625] A "user" refers to an individual who utilizes this system, and is the subject of analysis of their entertainment history, preferences, and real-time emotions.

[0626] "History" refers to a record of entertainment activities such as movies, music, and events that a user has accessed in the past, and it forms the basis of preference analysis.

[0627] "Preferences" refer to the trends in a user's preferences based on their past history, and are elements that are analyzed using machine learning techniques.

[0628] "Emotions" refer to the user's current emotional state and are collected through real-time detection using cameras and microphones.

[0629] "Generation" refers to the process of creating optimal entertainment information based on user preferences and emotions, utilizing machine learning and generative models.

[0630] A "terminal" refers to a device used to present entertainment information to a user, and is a device that can be operated through a user interface.

[0631] "Ratings" refer to reviews created by users after they have used a particular form of entertainment, and are automatically generated based on sentiment data and history.

[0632] An "information and communication network" refers to the technological infrastructure that enables terminals to exchange data with servers, and utilizes communication technologies, including the internet.

[0633] A "point system" is a mechanism for gamifying users' entertainment experiences, where points awarded for specific activities are accumulated and can be exchanged for rewards.

[0634] "Notifications" refer to a means of informing users of upcoming leisure activities, enabling them to manage their schedules efficiently.

[0635] This system includes multiple components—a server, a device, and an emotion engine—to personalize the user's entertainment experience. The server first collects the user's past entertainment history and stores it in a database. This data collection includes movie viewing history, music playback history, and event attendance history, and uses a specific database management system. The server leverages programming languages ​​such as Python and R, along with machine learning libraries like Scikit-learn and TensorFlow, to analyze user preferences. The results of this preference analysis are stored in a user profile and used to gain a detailed understanding of the user's preferences.

[0636] Next, the terminal has a function for real-time emotion detection. The terminal uses input devices such as a camera and microphone to analyze the user's facial expressions and voice tone using an emotion engine, and acquires real-time emotion data. Face recognition software and voice analysis technology are used for this analysis. The terminal visually presents content sent from the server to the user through a user interface. This information presentation includes an interface that allows for intuitive selection.

[0637] The server generates content using a generative AI model based on the user's preferences and real-time emotions. In this generation process, an example of a prompt is used: "Generate movies and music that best suit the user's emotions and preferences." The generated content is sent to the device as suggestions for movies, music, and events tailored to the user's mood and preferences.

[0638] Furthermore, after users view selected entertainment through their devices, they can share automatically generated reviews on social media. The review generation process takes into account the user's emotional data during viewing. In this way, the entire system works together to provide personalized entertainment experiences for individual users.

[0639] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0640] Step 1:

[0641] The server collects users' past entertainment data. As input, it retrieves information such as movie viewing history, music playback history, and events attended from a database. Based on this input data, it performs data cleaning and normalization to generate a consistent dataset. The refined data is then used for user preference analysis.

[0642] Step 2:

[0643] The server analyzes user preferences using machine learning algorithms based on collected historical data. It uses the dataset from the previous step as input and applies algorithms such as random forest and k-nearest neighbors. This analysis generates a user preference profile as output. This preference profile includes tendencies regarding which types of content users are most interested in.

[0644] Step 3:

[0645] The device uses an emotion engine to detect the user's emotions in real time. It utilizes facial expression data and voice tone acquired through the camera and microphone as input. This data is processed using facial recognition technology and voice analysis to generate an output representing the user's current emotional state.

[0646] Step 4:

[0647] The server integrates the preference profile and sentiment data obtained in the previous step. Here, a generative AI model is used to generate optimal entertainment content based on the data provided as input. An example of a prompt is, "Request entertainment recommendations based on current sentiment and preference data." The output of this generation process provides information on movies, music, and events suitable for the user.

[0648] Step 5:

[0649] The device receives entertainment information sent from the server and displays it through the user interface. Specifically, it visually highlights categories and recommendation levels, generating a list that users can intuitively select from. This makes it easy for users to choose content that interests them.

[0650] Step 6:

[0651] After a user selects and watches entertainment, the device automatically generates a review. The input is a vast amount of viewing log data, including emotional data. This information is analyzed, and a review text reflecting the user's emotional response is generated as output. This review can be shared on social media with the user's permission.

[0652] (Application Example 2)

[0653] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0654] In users' entertainment activities, it is difficult to suggest content that best suits individual preferences from a diverse range of options. Furthermore, efficiently suggesting content in real time in response to emotional changes, as well as evaluating and sharing experiences, presents challenges. A system is needed to consistently support the improvement and management of the user experience.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0656] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences and real-time emotions, and means for generating optimal entertainment information and transmitting it to the user's terminal. This makes it possible to suggest optimal content based on the user's past behavior and current psychological state.

[0657] "Means of collecting a user's past entertainment history" refers to methods of collecting and accumulating data such as movies a user has watched, music they have listened to, and events they have attended in the past.

[0658] "Methods for analyzing user preferences" refer to methods for analyzing the types of content a user likes based on their collected entertainment history.

[0659] "Means for generating entertainment information" refers to methods for constructing information that selects and suggests the most suitable entertainment content to users, based on collected and analyzed data.

[0660] "Means of transmitting information to a user's terminal" refers to the technology of distributing generated entertainment information to the user's electronic device.

[0661] "Methods for analyzing emotions in real time" refer to mechanisms that use the user's facial expressions, tone of voice, etc., to determine the user's current emotional state on the spot.

[0662] "Means to support reservations and purchases" refers to a system that assists users in easily reserving and purchasing their chosen entertainment.

[0663] "Methods for automatically generating reviews" refer to technologies that automatically create reviews based on the user's experience after viewing a product or service.

[0664] "Methods for sharing reviews on social networks" refers to the process of sharing automatically generated reviews on social media via the internet.

[0665] "A means of gamification based on a point system" refers to a mechanism that awards points when users watch or rate specific entertainment, and allows them to exchange those points for rewards or benefits.

[0666] "A way to remind users of their next entertainment plans" is a function that notifies users in advance of their planned entertainment activities, helping them to remember and carry them out.

[0667] The system implementing this invention aims to individually optimize the user's entertainment experience. The system consists of multiple components, including a server, a terminal, and an emotion engine.

[0668] The server comprehensively collects the user's past entertainment history and uses machine learning algorithms to analyze the user's preferences based on this data. The collected data is diverse, including movie viewing history, music playback history, and details of entertainment events attended. Furthermore, it integrates emotional data obtained in real time from the emotion engine to comprehensively understand the user's current emotional state. Based on this, the server generates optimal entertainment content based on the user's preferences and current emotions and sends it to the user's device.

[0669] The device visually presents entertainment information provided by the server to the user. It also features a function that works in conjunction with an emotion engine to analyze the user's facial expressions and tone of voice, recognizing emotions in real time. This allows for more personalized user selections. Once the user selects entertainment of interest from the provided information, the device provides an interface that efficiently assists with the reservation and purchase process.

[0670] After a user's viewing experience, the device automatically generates a review and supports review sharing on social media. It also assists with schedule management by reminding users of their next planned entertainment activity.

[0671] For example, if a user wants to relax that day, the server suggests calming movies and music and sends them to the device. This information is used when the user chooses entertainment and purchases tickets or streaming services. The server and device utilize a generative AI model to provide optimal content in response to requests such as, "If my mood today calls for relaxation, please recommend the best movie content for me."

[0672] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0673] Step 1:

[0674] The server collects the user's past entertainment history from a database. The input data collected includes details of movies the user has watched, music they have listened to, and events they have attended. This data is output as foundational data for analyzing the user's preferences.

[0675] Step 2:

[0676] The server applies machine learning algorithms to the collected historical data to analyze user preferences. The input for this preference analysis is the entertainment history obtained in the previous step, and the output is a feature vector representing the user's preferences. This feature vector forms the basis for the next content suggestion.

[0677] Step 3:

[0678] The server uses emotion data acquired in real time from the emotion engine as input to determine the user's current emotional state. This emotion data includes the user's facial expressions and voice tone, and calculations are performed based on this data to identify the emotional state, with the result being output.

[0679] Step 4:

[0680] The server integrates the results of preference analysis with real-time sentiment data and utilizes a generative AI model to generate optimal entertainment information. The inputs for this step are feature vectors and sentiment states, and the output is a list of content to be presented to the user.

[0681] Step 5:

[0682] The terminal receives entertainment information sent from the server and presents it to the user through a visual interface. The input is a list of content from the server, and the output is a screen display formatted for easy user understanding.

[0683] Step 6:

[0684] The user selects content of interest from the entertainment information displayed on the device. The selected content is then used as input for the device to assist with the subsequent purchase process.

[0685] Step 7:

[0686] The terminal supports the reservation and purchase process based on the user's selection. It receives the reservation and payment information entered by the user and completes the process by outputting it.

[0687] Step 8:

[0688] The device automatically generates a review after the user's viewing experience is complete and provides a sharing interface for social media. The input for this step is the user's viewing information and sentiment data, and the output is a formatted review text.

[0689] 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.

[0690] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0691] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0692] [Fourth Embodiment]

[0693] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0694] 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.

[0695] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0696] 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.

[0697] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0698] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0699] 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.

[0700] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0701] 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.

[0702] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0703] The 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.

[0704] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0705] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] This invention is a system for enriching the entertainment experience and providing users with optimal content. This system primarily consists of the interaction of three parties: a server, a terminal, and a user.

[0707] Server Functions

[0708] The server collects the user's past entertainment history. This history includes titles of movies the user has watched, ratings, details of events they have attended, and genres of music they have listened to. The server analyzes this data to learn the user's preferences. Machine learning algorithms are used for this analysis. The algorithms are responsible for understanding the user's preferences and organizing the data for future recommendations.

[0709] Based on the analysis results, the server generates information on movies, music, and events best suited to the user. This generated information reflects the user's individual preferences and is provided at the appropriate time. This information is sent to the terminal and can be viewed by the user.

[0710] Device functions

[0711] The terminal displays entertainment information sent from the server to the user. This allows the user to easily view the information and select entertainment on their device. For selected entertainment, the terminal supports the reservation and purchase process. Specifically, this includes reserving movie tickets and purchasing tickets for music events.

[0712] Furthermore, after the user has viewed the content, the device automatically generates a review in conjunction with the server. This review is created based on the user's feedback and viewing data, and the user is asked whether or not they wish to share it on social networks.

[0713] Furthermore, the device will remind users of their next entertainment plans. This ensures that users won't forget and can enjoy their next scheduled movie or event.

[0714] User actions

[0715] Users experience a process of selecting content that interests them based on the entertainment information displayed on their device. Once their selection is complete, they can smoothly reserve and purchase content through their device. After viewing, they can review automatically generated reviews from their device and revise them as needed. They can also choose to share their reviews on social networks.

[0716] For example, if a user wants to watch a movie on a holiday, the server analyzes the user's past viewing history and preferences and provides movie recommendations to the device based on that information. The user selects a movie and makes a ticket reservation through the device. After watching the movie, the device automatically generates a review of the film, which the user can then share on social media at their discretion. Furthermore, the device receives a reminder for the next weekend, notifying the user of their next movie viewing schedule.

[0717] As described above, the present invention is a system that more personalizes and consistently supports the user's entertainment experience.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The server collects the user's past entertainment history. This history includes movie viewing history, music playback records, event attendance information, and more. The server retrieves this data from a database.

[0721] Step 2:

[0722] The server analyzes the collected data and applies machine learning algorithms to identify user preferences. The algorithms identify the user's preferred genres and artist tendencies, and update the user profile accordingly.

[0723] Step 3:

[0724] The server generates movie, music, and event information tailored to the user based on the results of preference analysis. This information includes details such as recommended viewing and participation times, and ratings of related works.

[0725] Step 4:

[0726] The server generates entertainment information and sends it to the user's device. The device receives this information and prepares the necessary UI components for display.

[0727] Step 5:

[0728] The device displays entertainment information to the user. The user reviews the provided information through a visual interface and selects entertainment based on their interests.

[0729] Step 6:

[0730] The user selects their desired entertainment and proceeds with the reservation and purchase process through the terminal. The terminal provides an interface for entering payment information and transmits that data to the server.

[0731] Step 7:

[0732] The user experiences the entertainment of their choice. Once the experience is complete, the user provides brief feedback through their device.

[0733] Step 8:

[0734] The device collects feedback and sends it to the server. The server automatically generates reviews based on this feedback. It also uses this data to reanalyze user preferences.

[0735] Step 9:

[0736] The server sends the generated review to the device. The device then prompts the user to confirm the review and awaits the user's approval before sharing it on social networks.

[0737] Step 10:

[0738] The device uses a feature to remind the user of their next entertainment event. The reminder integrates with the user's calendar and notification system to inform them of the next event at the appropriate time.

[0739] (Example 1)

[0740] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0741] Traditional entertainment delivery systems have been insufficient in addressing individual user preferences, making it difficult to find the optimal content from countless options. Furthermore, it was challenging to reliably remember the user's planned use of selected content, resulting in a lack of consistency in the user experience.

[0742] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0743] In this invention, the server includes means for collecting the user's past information history, means for analyzing the user's preferences, and means for generating personalized recommendations using a generative AI model. This enables a consistent entertainment experience by efficiently providing content optimized for the user and notifying them of their next scheduled use.

[0744] "Information history" refers to a record of actions a user has taken or content they have used in the past.

[0745] "Preferences" refer to specific tastes or tendencies that a user exhibits based on their past activities.

[0746] "Content information" refers to entertainment-related information provided to users, such as movies, music, and events.

[0747] "Device" refers to a terminal or device used by a user, and is a device capable of receiving and displaying information.

[0748] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate useful information from data.

[0749] A "notification" is a message or alert used to inform a user about upcoming usage schedules or other important information.

[0750] A "computational learning algorithm" is a mathematical method used to clarify patterns and preferences through data analysis.

[0751] This invention is a system in which a server, a terminal, and a user work together to provide the user with the most suitable content. In this system, the server is located in a cloud environment and uses a database to collect the user's past information history. This information history includes the titles and ratings of movies the user has watched, details of events they have attended, and genres of music they have listened to. The collected data is analyzed using machine learning algorithms to identify the user's preferences. This analysis particularly utilizes clustering and collaborative filtering techniques.

[0752] The server generates personalized recommendations using a generative AI model based on the analysis results. For example, it might generate a prompt like, "You will enjoy this movie: 'Mystery Adventure' - featuring a captivating plot and diverse cast." This generated information is then sent to the terminal via the network.

[0753] The device organizes content information sent from the server and displays it in a user-friendly format. Users can easily select content of interest and reserve or purchase it using the device. For selected content, the device automatically generates reviews and sends them to the server for further analysis.

[0754] The device also has a feature that notifies users of upcoming content schedules. This allows users to enjoy their next entertainment without forgetting.

[0755] By using this system, users can receive content suggestions based on their individual preferences, leading to a richer entertainment experience.

[0756] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0757] Step 1:

[0758] The server collects historical information transmitted from the user's device into a cloud database. Inputs include the titles, ratings, and categories of content the user has viewed. The data is collected in real time and stored for later analysis. The output is a chronologically organized history of the information.

[0759] Step 2:

[0760] The server analyzes user preferences using collected information history. This analysis is performed using machine learning algorithms, employing clustering and collaborative filtering techniques. The input is the collected information history, and the output is the user's preference pattern. Specifically, the server builds a preference model tailored to each individual user, while also referencing data from other users with similar preferences.

[0761] Step 3:

[0762] The server generates personalized content recommendations using a generative AI model based on the analysis results. The input is the user's preference pattern, and the output is the recommendation text. Specifically, the server creates prompt texts such as "You will enjoy this movie: 'Mystery Adventure'" by filling in appropriate content for a template.

[0763] Step 4:

[0764] Content information generated from the server is sent to the device, which then displays it. The input is the content information from the server, and the output is the information visually presented to the user. The device uses a notification function to display new content on the dashboard, allowing the user to easily check it.

[0765] Step 5:

[0766] Users select items of interest from the content information displayed on the terminal and make reservations or purchases. The input is the content information presented on the terminal, and the output is reservation confirmation information for the selected content. The terminal integrates with the payment system, allowing users to complete the purchase process with a single click.

[0767] Step 6:

[0768] After viewing, the device collects user feedback and automatically generates an evaluation. The input is user feedback, and the output is a calculated evaluation statement. The device sends the evaluation to a server and stores it in a database for future analysis.

[0769] Step 7:

[0770] The device reminds the user of their next scheduled content usage. The input is schedule information from the server, and the output is a notification message to the user. This allows users to plan their daily enjoyment without forgetting their next entertainment event.

[0771] (Application Example 1)

[0772] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0773] When users enjoy entertainment, there are challenges in ensuring they can choose the best content from a diverse range of options, watch it smoothly, and easily share reviews. Furthermore, there is a need to improve entertainment satisfaction by providing personalized experiences based on user preferences.

[0774] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0775] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, means for generating entertainment information to suggest to the user, means for transmitting the generated information to the user's terminal, means for supporting the reservation and purchase of entertainment selected by the user, means for automatically generating reviews after the user has viewed the content and sharing them on social networks, means for gamifying the user's entertainment experience based on a point system, means for reminding the user of their next entertainment appointment, means for instantly playing the content on the terminal, and means for enabling the user to review, modify, and share reviews. This enables personalized suggestions that reflect the user's preferences and allows for an efficient execution of the entire process from content selection to viewing and review sharing.

[0776] "Means of collecting a user's past entertainment history" refers to a function that collects data about entertainment content that a user has previously viewed or participated in.

[0777] "Means for analyzing user preferences" refers to analytical functions that identify user preferences and trends based on collected historical data.

[0778] "Means for generating entertainment information to suggest to users" refers to a function that presents users with suitable entertainment options based on analysis results.

[0779] "Means for transmitting generated information to the user's terminal" refers to a communication function for delivering proposed entertainment information to the user's device.

[0780] "Means to support the reservation and purchase of entertainment selected by the user" refers to support functions that facilitate the reservation and purchase of content chosen by the user.

[0781] "A means of automatically generating reviews after a user has viewed a video" refers to a function that automatically creates a review of the content after the user has finished watching it.

[0782] "Means of sharing on social networks" refers to communication and transmission functions that enable the generated reviews to be shared with others online.

[0783] "A means of gamifying the user's entertainment experience based on a point system" refers to a function that awards points as an incentive for entertainment activities and incorporates game elements to improve the user's experience.

[0784] "A way to remind users of upcoming entertainment events" is a feature that notifies users of their next entertainment event and encourages them to check their schedule so they don't forget.

[0785] "Means for instantly playing content on a device" refers to a function that allows selected content to be immediately streamed on the user's device.

[0786] "Means that enable users to review, revise, and share reviews" refers to features that help users review the content of automatically generated reviews, edit them as needed, and then share them with others again.

[0787] This invention is a system for personalizing and efficiently supporting users' entertainment experiences. This system is primarily built on the interaction between a server, a terminal, and the user.

[0788] The server collects the user's past entertainment history, including data such as movies watched, events attended, and music listened to. Based on this data, the server uses machine learning algorithms (e.g., scikit-learn) to analyze the user's preferences. This analysis helps understand the user's tendencies and is used to suggest appropriate content. The suggested entertainment information reflects the user's individual preferences and can be played immediately using the streaming capabilities of external services.

[0789] The device is responsible for presenting entertainment information sent from the server to the user. Once the user selects content, the device supports its reservation and purchase, and immediately plays the selected content. Furthermore, after viewing, a review is automatically generated using a natural language processing library (e.g., spaCy), which the user can review, modify as needed, and share on social networks.

[0790] Users select content based on the information displayed on their device and enjoy streaming playback. After watching, they can review automatically generated reviews and choose whether or not to share them. This allows users to maximize their entertainment experience.

[0791] For example, if a user wants to watch a movie on their day off, the server will display recommended movies based on their past viewing history and analysis results. The user can select a movie and play it instantly on their device. Afterwards, a review is generated, and if they want to share it with friends, they can easily adjust the sharing settings.

[0792] An example of a prompt to a generative AI model would be, "I'm looking for a relaxing movie to watch on my day off. What movies do you think are good these days?" This would allow the model to receive content suggestions in this format.

[0793] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0794] Step 1:

[0795] The server collects the user's past entertainment history from a database. This data includes information such as movies the user has watched, events they have attended, and music they have listened to. This data is collected as input to prepare a basic dataset for analysis.

[0796] Step 2:

[0797] The server analyzes the collected data using machine learning algorithms (e.g., scikit-learn) to model user preferences. Here, user viewing trends and preferences are extracted as patterns, and basic information for personalized content recommendations is output. This process involves data analysis and calculations, resulting in the generation of a user preference profile.

[0798] Step 3:

[0799] Based on the generated preference profile, the server selects the most suitable entertainment content for the user and creates a recommendation list. This list contains information about the content to be suggested and is temporarily stored for transmission to the device.

[0800] Step 4:

[0801] The terminal receives a list of recommendations sent from the server and presents it visually to the user. The user can select content of interest from this list. The terminal processes this received data and displays it as options on a GUI.

[0802] Step 5:

[0803] The user selects content they are interested in on their device. This selection serves as input, and the device supports the reservation and purchase process for the specified entertainment content. The data processing involved here includes receiving the selection information and creating a reservation completion notification based on that information.

[0804] Step 6:

[0805] The device immediately streams the selected content. Here, an API from an external streaming service is used to obtain a link for real-time playback of the selected movie or music, and playback begins on the user's device.

[0806] Step 7:

[0807] Once a user finishes viewing content, the device automatically generates a review using a natural language processing library (e.g., spaCy) based on the data collected during viewing. This generation process analyzes the content viewed and the user's reactions, and then outputs a review statement.

[0808] Step 8:

[0809] Users can review the generated review and revise it if necessary. After revision, the review can be shared on social networks. The device will follow the user's instructions to save and submit the review.

[0810] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0811] This invention provides a system that comprehensively supports users' entertainment experiences and enables the delivery of optimal content based on individual preferences and emotions. This system consists of multiple components, including a server, terminals, and an emotion engine.

[0812] Server Functions

[0813] The server collects the user's past entertainment history and analyzes their preferences using machine learning algorithms. The collected data includes movie viewing history, music playback history, and details of events attended. Furthermore, the server integrates emotional data from an emotion engine to understand the user's real-time emotional state.

[0814] This allows the server to generate optimal entertainment information based on the user's preferences and emotional state. This information includes details on recommended movies and events, suggested music playlists, and more, all customized to the user's current mood.

[0815] Device functions

[0816] The device presents entertainment information sent from the server to the user and makes it available through a visual interface. A key role here is that the device works in conjunction with an emotion engine to detect the user's emotions in real time from their facial expressions and tone of voice.

[0817] When a user selects entertainment information they are interested in, the device supports reservation and purchase functions, allowing for easy completion of the necessary procedures. It also provides an interface for receiving payment information, ensuring a smooth user experience.

[0818] Furthermore, after viewing, the device automatically generates a review and supports the process of sharing it on social networks. It also has a function to remind users of their next entertainment plans, allowing for efficient schedule management.

[0819] User actions

[0820] Users select content that interests them from the entertainment information displayed on their device, based on their emotions and preferences. During this process, an emotion engine analyzes the user's emotions in real time and suggests the most appropriate options based on their current state of mind.

[0821] For example, if a user wants to relax, the server suggests calming movies and music and sends them to the device. After the user selects a movie and books tickets, the emotion engine generates a further customized review based on the user's emotions while watching. This review may be posted to social media via the device.

[0822] As described above, the present invention is a system that personalizes entertainment based on the user's emotions and preferences, providing a consistent user experience from booking and review creation to managing future appointments.

[0823] The following describes the processing flow.

[0824] Step 1:

[0825] The server collects the user's past entertainment history. This history data includes records of movies the user has watched, music they have listened to, and events they have attended. The server retrieves this history information from a database.

[0826] Step 2:

[0827] The server works in conjunction with the emotion engine to detect the user's emotions in real time. Facial expression and voice data from when the user is using the device are analyzed through the emotion engine to recognize their emotional state. This information is then sent to the server.

[0828] Step 3:

[0829] Based on historical data collected by the server and real-time emotional states, a machine learning algorithm is used to analyze user preferences. The algorithm considers the user's current emotions and past preferences to suggest appropriate entertainment content.

[0830] Step 4:

[0831] The server generates entertainment recommendations for the user. This information includes suggestions for movies, music, and events that take into account the user's current mood. The generated information is sent to the terminal.

[0832] Step 5:

[0833] The device displays entertainment information received from the server to the user. The user can review and select the suggested content through the interface.

[0834] Step 6:

[0835] The device supports the reservation and purchase of entertainment content selected by the user. The device provides a form for entering necessary information and manages the reservation and payment process.

[0836] Step 7:

[0837] The user experiences the entertainment they selected. After the experience ends, the device re-analyzes the changes in their emotional state through an emotion engine. This analysis is used to evaluate the user's overall experience.

[0838] Step 8:

[0839] The device sends feedback to the server. The server automatically generates a review based on the feedback. The review is customized based on the user's emotional state and preferences.

[0840] Step 9:

[0841] The server generates a review and sends it to the device. The device then allows the user to review the review, make any necessary corrections, and then suggests sharing it on social networks.

[0842] Step 10:

[0843] The device activates a feature that reminds you of your next entertainment event. The reminder notifies the user of information about their next scheduled entertainment activity.

[0844] (Example 2)

[0845] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0846] Traditional entertainment systems have faced challenges in providing highly satisfying entertainment experiences because they struggle to personalize them to adequately consider user preferences and emotions. Furthermore, they have difficulty consistently managing the user experience, resulting in problems with the smooth operation of everything from selecting, booking, and purchasing entertainment to sharing reviews.

[0847] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0848] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences, and means for detecting the user's real-time emotions. This makes it possible to generate optimal entertainment information based on the user's preferences and emotions and transmit it to the user terminal, thereby providing a consistent and personalized entertainment experience and improving user satisfaction.

[0849] A "user" refers to an individual who utilizes this system, and is the subject of analysis of their entertainment history, preferences, and real-time emotions.

[0850] "History" refers to a record of entertainment activities such as movies, music, and events that a user has accessed in the past, and it forms the basis of preference analysis.

[0851] "Preferences" refer to the trends in a user's preferences based on their past history, and are elements that are analyzed using machine learning techniques.

[0852] "Emotions" refer to the user's current emotional state and are collected through real-time detection using cameras and microphones.

[0853] "Generation" refers to the process of creating optimal entertainment information based on user preferences and emotions, utilizing machine learning and generative models.

[0854] A "terminal" refers to a device used to present entertainment information to a user, and is a device that can be operated through a user interface.

[0855] "Ratings" refer to reviews created by users after they have used a particular form of entertainment, and are automatically generated based on sentiment data and history.

[0856] An "information and communication network" refers to the technological infrastructure that enables terminals to exchange data with servers, and utilizes communication technologies, including the internet.

[0857] A "point system" is a mechanism for gamifying users' entertainment experiences, where points awarded for specific activities are accumulated and can be exchanged for rewards.

[0858] "Notifications" refer to a means of informing users of upcoming leisure activities, enabling them to manage their schedules efficiently.

[0859] This system includes multiple components—a server, a device, and an emotion engine—to personalize the user's entertainment experience. The server first collects the user's past entertainment history and stores it in a database. This data collection includes movie viewing history, music playback history, and event attendance history, and uses a specific database management system. The server leverages programming languages ​​such as Python and R, along with machine learning libraries like Scikit-learn and TensorFlow, to analyze user preferences. The results of this preference analysis are stored in a user profile and used to gain a detailed understanding of the user's preferences.

[0860] Next, the terminal has a function for real-time emotion detection. The terminal uses input devices such as a camera and microphone to analyze the user's facial expressions and voice tone using an emotion engine, and acquires real-time emotion data. Face recognition software and voice analysis technology are used for this analysis. The terminal visually presents content sent from the server to the user through a user interface. This information presentation includes an interface that allows for intuitive selection.

[0861] The server generates content using a generative AI model based on the user's preferences and real-time emotions. In this generation process, an example of a prompt is used: "Generate movies and music that best suit the user's emotions and preferences." The generated content is sent to the device as suggestions for movies, music, and events tailored to the user's mood and preferences.

[0862] Furthermore, after users view selected entertainment through their devices, they can share automatically generated reviews on social media. The review generation process takes into account the user's emotional data during viewing. In this way, the entire system works together to provide personalized entertainment experiences for individual users.

[0863] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0864] Step 1:

[0865] The server collects users' past entertainment data. As input, it retrieves information such as movie viewing history, music playback history, and events attended from a database. Based on this input data, it performs data cleaning and normalization to generate a consistent dataset. The refined data is then used for user preference analysis.

[0866] Step 2:

[0867] The server analyzes user preferences using machine learning algorithms based on collected historical data. It uses the dataset from the previous step as input and applies algorithms such as random forest and k-nearest neighbors. This analysis generates a user preference profile as output. This preference profile includes tendencies regarding which types of content users are most interested in.

[0868] Step 3:

[0869] The device uses an emotion engine to detect the user's emotions in real time. It utilizes facial expression data and voice tone acquired through the camera and microphone as input. This data is processed using facial recognition technology and voice analysis to generate an output representing the user's current emotional state.

[0870] Step 4:

[0871] The server integrates the preference profile and sentiment data obtained in the previous step. Here, a generative AI model is used to generate optimal entertainment content based on the data provided as input. An example of a prompt is, "Request entertainment recommendations based on current sentiment and preference data." The output of this generation process provides information on movies, music, and events suitable for the user.

[0872] Step 5:

[0873] The device receives entertainment information sent from the server and displays it through the user interface. Specifically, it visually highlights categories and recommendation levels, generating a list that users can intuitively select from. This makes it easy for users to choose content that interests them.

[0874] Step 6:

[0875] After a user selects and watches entertainment, the device automatically generates a review. The input is a vast amount of viewing log data, including emotional data. This information is analyzed, and a review text reflecting the user's emotional response is generated as output. This review can be shared on social media with the user's permission.

[0876] (Application Example 2)

[0877] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0878] In users' entertainment activities, it is difficult to suggest content that best suits individual preferences from a diverse range of options. Furthermore, efficiently suggesting content in real time in response to emotional changes, as well as evaluating and sharing experiences, presents challenges. A system is needed to consistently support the improvement and management of the user experience.

[0879] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0880] In this invention, the server includes means for collecting the user's past entertainment history, means for analyzing the user's preferences and real-time emotions, and means for generating optimal entertainment information and transmitting it to the user's terminal. This makes it possible to suggest optimal content based on the user's past behavior and current psychological state.

[0881] "Means of collecting a user's past entertainment history" refers to methods of collecting and accumulating data such as movies a user has watched, music they have listened to, and events they have attended in the past.

[0882] "Methods for analyzing user preferences" refer to methods for analyzing the types of content a user likes based on their collected entertainment history.

[0883] "Means for generating entertainment information" refers to methods for constructing information that selects and suggests the most suitable entertainment content to users, based on collected and analyzed data.

[0884] "Means of transmitting information to a user's terminal" refers to the technology of distributing generated entertainment information to the user's electronic device.

[0885] "Methods for analyzing emotions in real time" refer to mechanisms that use the user's facial expressions, tone of voice, etc., to determine the user's current emotional state on the spot.

[0886] "Means to support reservations and purchases" refers to a system that assists users in easily reserving and purchasing their chosen entertainment.

[0887] "Methods for automatically generating reviews" refer to technologies that automatically create reviews based on the user's experience after viewing a product or service.

[0888] "Methods for sharing reviews on social networks" refers to the process of sharing automatically generated reviews on social media via the internet.

[0889] "A means of gamification based on a point system" refers to a mechanism that awards points when users watch or rate specific entertainment, and allows them to exchange those points for rewards or benefits.

[0890] "A way to remind users of their next entertainment plans" is a function that notifies users in advance of their planned entertainment activities, helping them to remember and carry them out.

[0891] The system implementing this invention aims to individually optimize the user's entertainment experience. The system consists of multiple components, including a server, a terminal, and an emotion engine.

[0892] The server comprehensively collects the user's past entertainment history and uses machine learning algorithms to analyze the user's preferences based on this data. The collected data is diverse, including movie viewing history, music playback history, and details of entertainment events attended. Furthermore, it integrates emotional data obtained in real time from the emotion engine to comprehensively understand the user's current emotional state. Based on this, the server generates optimal entertainment content based on the user's preferences and current emotions and sends it to the user's device.

[0893] The device visually presents entertainment information provided by the server to the user. It also features a function that works in conjunction with an emotion engine to analyze the user's facial expressions and tone of voice, recognizing emotions in real time. This allows for more personalized user selections. Once the user selects entertainment of interest from the provided information, the device provides an interface that efficiently assists with the reservation and purchase process.

[0894] After a user's viewing experience, the device automatically generates a review and supports review sharing on social media. It also assists with schedule management by reminding users of their next planned entertainment activity.

[0895] For example, if a user wants to relax that day, the server suggests calming movies and music and sends them to the device. This information is used when the user chooses entertainment and purchases tickets or streaming services. The server and device utilize a generative AI model to provide optimal content in response to requests such as, "If my mood today calls for relaxation, please recommend the best movie content for me."

[0896] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0897] Step 1:

[0898] The server collects the user's past entertainment history from a database. The input data collected includes details of movies the user has watched, music they have listened to, and events they have attended. This data is output as foundational data for analyzing the user's preferences.

[0899] Step 2:

[0900] The server applies machine learning algorithms to the collected historical data to analyze user preferences. The input for this preference analysis is the entertainment history obtained in the previous step, and the output is a feature vector representing the user's preferences. This feature vector forms the basis for the next content suggestion.

[0901] Step 3:

[0902] The server uses emotion data acquired in real time from the emotion engine as input to determine the user's current emotional state. This emotion data includes the user's facial expressions and voice tone, and calculations are performed based on this data to identify the emotional state, with the result being output.

[0903] Step 4:

[0904] The server integrates the results of preference analysis with real-time sentiment data and utilizes a generative AI model to generate optimal entertainment information. The inputs for this step are feature vectors and sentiment states, and the output is a list of content to be presented to the user.

[0905] Step 5:

[0906] The terminal receives entertainment information sent from the server and presents it to the user through a visual interface. The input is a list of content from the server, and the output is a screen display formatted for easy user understanding.

[0907] Step 6:

[0908] The user selects content of interest from the entertainment information displayed on the device. The selected content is then used as input for the device to assist with the subsequent purchase process.

[0909] Step 7:

[0910] The terminal supports the reservation and purchase process based on the user's selection. It receives the reservation and payment information entered by the user and completes the process by outputting it.

[0911] Step 8:

[0912] The device automatically generates a review after the user's viewing experience is complete and provides a sharing interface for social media. The input for this step is the user's viewing information and sentiment data, and the output is a formatted review text.

[0913] 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.

[0914] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0915] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0916] 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.

[0917] Figure 9 shows an 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.

[0918] 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.

[0919] 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.

[0920] 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, motorcycles, etc., 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, for example, based 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.

[0921] 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."

[0922] 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.

[0923] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0924] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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 the like 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.

[0933] 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.

[0934] The following is further disclosed regarding the embodiments described above.

[0935] (Claim 1)

[0936] A means of collecting a user's past entertainment history,

[0937] A means of analyzing user preferences based on the aforementioned history,

[0938] A means of generating entertainment information to suggest to users,

[0939] A means of sending the generated information to the user's terminal,

[0940] A means to support the reservation and purchase of entertainment selected by the user,

[0941] A method for automatically generating reviews after a user has viewed the content,

[0942] Means for sharing the aforementioned review on social networks,

[0943] A means of gamifying the user's entertainment experience based on a point system,

[0944] A way to be reminded of the next entertainment event,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, wherein the means for analyzing the user's preferences is a machine learning algorithm.

[0948] (Claim 3)

[0949] The system according to claim 1, wherein the point system awards points when a user watches or rates specific entertainment, and the points can be exchanged for rewards.

[0950] "Example 1"

[0951] (Claim 1)

[0952] A means of collecting a user's past information history,

[0953] A means of analyzing user preferences based on the aforementioned history,

[0954] A means of generating content information to suggest to the user,

[0955] A means for transmitting the generated information to the user's device,

[0956] A means to support the reservation and purchase of content selected by the user,

[0957] A means of automatically generating ratings after a user has used the service,

[0958] A means for sharing the aforementioned evaluation on an information sharing network,

[0959] A means of creating personalized recommendations for users using a generative AI model,

[0960] A means of notifying about the next content schedule,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, wherein the means for analyzing the user's preferences is a computational learning algorithm.

[0964] (Claim 3)

[0965] The system according to claim 1, which provides more personalized recommendations by generating prompt sentences using a generative AI model.

[0966] "Application Example 1"

[0967] (Claim 1)

[0968] A means of collecting a user's past entertainment history,

[0969] A means of analyzing user preferences based on the aforementioned history,

[0970] A means of generating entertainment information to suggest to users,

[0971] A means of sending the generated information to the user's terminal,

[0972] A means to support the reservation and purchase of entertainment selected by the user,

[0973] A method for automatically generating reviews after a user has viewed the content,

[0974] Means for sharing the aforementioned review on social networks,

[0975] A means of gamifying the user's entertainment experience based on a point system,

[0976] A way to be reminded of the next entertainment event,

[0977] A means of instantly playing content on a device,

[0978] A means to enable users to review, edit, and share reviews,

[0979] A system that includes this.

[0980] (Claim 2)

[0981] The means for analyzing the user's preferences is to use a machine learning algorithm.

[0982] The system according to claim 1, wherein the means for immediately playing the aforementioned content utilizes streaming from an external service.

[0983] (Claim 3)

[0984] The aforementioned point system awards points when a user watches or rates specific entertainment.

[0985] The system according to claim 1, wherein points can also be applied when using the instant playback function of the aforementioned content.

[0986] "Example 2 of combining an emotion engine"

[0987] (Claim 1)

[0988] A means of collecting a user's past entertainment history,

[0989] A means of analyzing user preferences based on the aforementioned history,

[0990] A means of detecting the user's emotions in real time,

[0991] A means for generating entertainment information based on the aforementioned preferences and emotions,

[0992] A means for transmitting the generated information to the user's terminal device,

[0993] A means to support the reservation and purchase of entertainment selected by the user,

[0994] A method for automatically generating ratings after a user has viewed the content,

[0995] A means for sharing the aforementioned evaluation over an information and communication network,

[0996] A means of providing a points system to enhance the user's entertainment experience,

[0997] A means of notifying about the next planned entertainment event,

[0998] A system that includes this.

[0999] (Claim 2)

[1000] The system according to claim 1, wherein the means for analyzing the user's preferences is a machine learning method.

[1001] (Claim 3)

[1002] The system according to claim 1, wherein the point system may award points that can be exchanged for rewards when a person watches or evaluates specific entertainment.

[1003] "Application example 2 when combining with an emotional engine"

[1004] (Claim 1)

[1005] A means of collecting a user's past entertainment history,

[1006] A means of analyzing user preferences based on the aforementioned history,

[1007] A means of generating entertainment information to suggest to users,

[1008] A means of sending the generated information to the user's terminal,

[1009] A means of analyzing users' emotions in real time,

[1010] A means of suggesting optimal entertainment information based on real-time sentiment analysis results,

[1011] A means to support the reservation and purchase of entertainment selected by the user,

[1012] A method for automatically generating reviews after a user has viewed the content,

[1013] Means for sharing the aforementioned review on social networks,

[1014] A means of gamifying the user's entertainment experience based on a point system,

[1015] A way to be reminded of the next entertainment event,

[1016] A system that includes this.

[1017] (Claim 2)

[1018] The system according to claim 1, wherein the means for analyzing the user's preferences is a machine learning algorithm.

[1019] (Claim 3)

[1020] The system according to claim 1, wherein the point system awards points when a user watches or rates specific entertainment, and the points can be exchanged for rewards. [Explanation of Symbols]

[1021] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting a user's past entertainment history, A means of analyzing user preferences based on the aforementioned history, A means of generating entertainment information to suggest to users, A means of sending the generated information to the user's terminal, A means to support the reservation and purchase of entertainment selected by the user, A method for automatically generating reviews after a user has viewed the content, Means for sharing the aforementioned review on social networks, A means of gamifying the user's entertainment experience based on a point system, A way to be reminded of the next entertainment event, A means of instantly playing content on a device, A means to enable users to review, edit, and share reviews, A system that includes this.

2. The means for analyzing the user's preferences is to use a machine learning algorithm. The system according to claim 1, wherein the means for immediately playing the aforementioned content utilizes streaming from an external service.

3. The aforementioned point system awards points when a user watches or rates specific entertainment. The system according to claim 1, wherein points can also be applied when using the instant playback function of the aforementioned content.

Images