System

Abstract

An object of a system according to an embodiment is to recommend an optimal entertainment work based on a preference of a user.SOLUTION: In one embodiment, a system comprises a user profile data input, an analyzer, a recommender, an information provider, and a hybrid recommender. The user profile data input unit inputs user profile data. The analysis unit analyzes the user profile data input by the user profile data input unit. The recommendation unit recommends an optimal comic or movie based on the user profile data analyzed by the analysis unit. The information providing unit provides detailed information on the work recommended by the recommendation unit. The hybrid recommendation unit recommends a new comic or movie in which styles and genres are combined based on the user profile data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend the most suitable entertainment works based on the user's preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend the most suitable entertainment works based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a user profile data input unit, an analysis unit, a recommendation unit, an information provision unit, and a hybrid recommendation unit. The user profile data input unit inputs user profile data. The analysis unit analyzes the user profile data input by the user profile data input unit. The recommendation unit recommends optimal manga and movies based on the user profile data analyzed by the analysis unit. The information provision unit provides detailed information about the works recommended by the recommendation unit. The hybrid recommendation unit recommends new manga and movies that combine styles and genres based on the user profile data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable entertainment works based on the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The entertainment recommendation system according to an embodiment of the present invention uses a generative AI to analyze user profile data and provide optimal manga and movie recommendations, thereby providing users with an infinite number of entertainment combinations and deepening their attachment to the content.

[0029] An entertainment recommendation system according to an embodiment includes a user profile data input unit, an analysis unit, a recommendation unit, an information provision unit, and a hybrid recommendation unit. The user profile data input unit inputs the user's preferences and past browsing history. For example, the user inputs their favorite genres, favorite authors, and a list of previously viewed works. The analysis unit analyzes the user profile data input by the user profile data input unit. For example, the generation AI uses data mining techniques and machine learning algorithms to understand the user's preferences. The recommendation unit recommends optimal manga and movies based on the user profile data analyzed by the analysis unit. For example, the recommendation unit recommends works with a similar style or genre to works the user has previously viewed. The recommendation unit also suggests new and popular works that match the user's preferences. The information provision unit provides detailed information about the works recommended by the recommendation unit. For example, the information provision unit provides comments, interviews, and behind-the-scenes information from directors and writers. The hybrid recommendation unit recommends new manga and movies that combine different styles and genres based on the user profile data analyzed by the analysis unit. For example, it may recommend a movie that combines an action movie and a romance movie that the user likes. In this way, the entertainment recommendation system according to the embodiment provides the user with the most suitable manga and movie recommendations, and improves the user's entertainment experience by suggesting detailed information about the movies and movies in new genres.

[0030] The user profile data input unit can collect public information from social media accounts and reflect it in the profile data. For example, the user profile data input unit analyzes public posts from the user's social media accounts and reflects the user's interests and concerns in the profile data. For example, it extracts topics and hashtags that the user frequently posts to. The user profile data input unit also collects information about accounts and groups followed by the user from the user's social media accounts and reflects it in the profile data. For example, it estimates the user's preferences based on information about film directors and writers followed by the user. The user profile data input unit also collects data about posts that the user has "liked" or "shared" from the user's social media accounts and reflects it in the profile data. For example, it estimates the user's preferences based on information about movies and manga that the user has "liked." In this way, by reflecting information from social media in the profile data, the user's preferences can be more accurately understood.

[0031] The user profile data input unit can analyze the user's past events and travel history and reflect it in their entertainment preferences. The user profile data input unit, for example, analyzes the user's ticket purchase history for events they have attended in the past and reflects it in their entertainment preferences. For example, it estimates preferences based on their attendance history at concerts and film festivals. The user profile data input unit also analyzes information about travel destinations the user has visited in the past and reflects it in their entertainment preferences. For example, it recommends works related to a particular region or culture. The user profile data input unit also analyzes the user's history of workshops and seminars they have attended in the past and reflects it in their entertainment preferences. For example, it estimates preferences based on their participation history in filmmaking or manga creation workshops. This enables more personalized recommendations by analyzing the user's past event and travel history and reflecting it in their entertainment preferences.

[0032] The user profile data input unit can analyze purchase history and add preference trends to the profile data. The user profile data input unit, for example, analyzes the user's purchase history on an online shopping site and adds preference trends to the profile data. For example, preferences are estimated based on the genres of movies and manga purchased. The user profile data input unit also analyzes the user's purchase history at bookstores and movie theaters and adds preference trends to the profile data. For example, preferences are estimated based on information about purchased books and movie tickets. The user profile data input unit also analyzes the user's subscription service usage history and adds preference trends to the profile data. For example, preferences are estimated based on subscription magazines and streaming service viewing history. In this way, analyzing purchase history and adding preference trends to the profile data enables more accurate recommendations.

[0033] The recommendation unit can make recommendations that take into account the viewing time period and viewing environment based on the user's past viewing history. For example, the recommendation unit analyzes the user's past viewing history and makes recommendations according to the viewing time period. For example, it suggests relaxing works to a user who often watches late at night. The recommendation unit also analyzes the user's viewing environment and recommends optimal works. For example, it suggests short films or episodes to a user who often watches during their commute. The recommendation unit also recommends works that are suitable for specific time periods or environments based on the user's viewing history. For example, it suggests feature-length films to a user who often watches on weekends. In this way, recommendations that take into account the viewing time period and viewing environment improve the user's viewing experience.

[0034] The recommendation unit can analyze the viewing history of the user's friends and family and make recommendations based on shared preferences. For example, the recommendation unit analyzes the viewing history of the user's friends and family and recommends works based on shared preferences. For example, it can suggest family movies that the whole family can enjoy. The recommendation unit also recommends works that share common interests based on the viewing history of the user's friends and family. For example, it can suggest action movies that can be enjoyed together with friends. The recommendation unit also analyzes the viewing history of the user's friends and family and recommends new or popular works based on shared preferences. For example, it can suggest new movies that the whole family will be interested in. In this way, the user's viewing experience is improved by analyzing the viewing history of friends and family and making recommendations based on shared preferences.

[0035] The recommendation unit can also simultaneously recommend music and podcasts based on the user's preferences. For example, the recommendation unit can recommend not only manga and movies, but also music and podcasts based on the user's profile data. For example, it can suggest music and podcasts in the user's favorite genre. The recommendation unit can also analyze the user's viewing history and recommend related music and podcasts. For example, it can suggest soundtracks for specific movies and related podcasts. The recommendation unit can also build a system that recommends music and podcasts based on the user's preferences. For example, it can suggest content related to the user's favorite artists or themes. This improves the user's entertainment experience by simultaneously recommending music and podcasts.

[0036] The recommendation unit can also provide recommendations for books and magazines according to the user's preferences. For example, the recommendation unit recommends not only manga and movies but also books and magazines based on the user's profile data. For example, it suggests books and magazines in the user's favorite genre. The recommendation unit also analyzes the user's viewing history and recommends related books and magazines. For example, it suggests books and magazines related to a specific movie or manga. The recommendation unit also builds a system that recommends books and magazines according to the user's preferences. For example, it suggests content related to the user's favorite author or theme. In this way, by providing book and magazine recommendations, the user's entertainment experience is improved.

[0037] The information providing unit can automatically collect historical and cultural background related to a work and provide it to the user. For example, the information providing unit automatically collects historical background related to a work and provides it to the user. For example, it explains historical facts and events behind a period drama film. The information providing unit also automatically collects cultural background related to a work and provides it to the user. For example, it provides background information for films and manga based on a particular culture or tradition. The information providing unit also comprehensively collects historical and cultural background related to a work and provides it to the user. For example, it provides detailed information about the region and era in which the work is set. In this way, providing historical and cultural background deepens the user's understanding of the work.

[0038] The information provision unit provides detailed technical information and information about the production process of a work, thereby deepening the user's understanding. The information provision unit, for example, provides detailed information about the production process of a work. For example, it provides footage from the film set and comments from the director. The information provision unit also provides detailed technical information about the work. For example, it explains the production process of special effects and CG. The information provision unit also provides comprehensive information about the production process and detailed technical information about the work. For example, it provides interviews with cast and staff and technical commentary. In this way, by providing detailed information about the production process and technical information, the user can deepen their understanding of the work.

[0039] The information providing unit can also simultaneously provide information about merchandise and events related to the work. The information providing unit, for example, provides information about merchandise related to the work. For example, it provides information about official movie merchandise and limited edition items. The information providing unit also provides information about events related to the work. For example, it provides information about movie premieres and fan meetings. The information providing unit also provides integrated information about merchandise and events related to the work. For example, it provides links to the movie's official website and online store. In this way, providing merchandise and event information deepens the user's interest in the work.

[0040] The information providing unit can provide information on fan communities and forums related to a work, thereby promoting interaction between users. The information providing unit, for example, provides information on fan communities related to a work. For example, it provides links to online forums and SNS groups. The information providing unit also provides information on fan events related to a work. For example, it provides information on fan meetings and cosplay events. The information providing unit also provides integrated information on fan communities and forums related to a work, promoting interaction between users. For example, it provides links to sites for posting fan art and fan fiction. In this way, providing information on fan communities and forums promotes interaction between users.

[0041] The hybrid recommendation unit can suggest different genre combinations based on the user's past viewing history. For example, the hybrid recommendation unit analyzes the user's past viewing history and suggests different genre combinations. For example, if the user likes action movies and romance movies, it suggests works that combine action and romance. The hybrid recommendation unit also suggests different genre combinations based on the user's viewing history. For example, if the user likes horror movies and comedy movies, it suggests works that combine horror and comedy. The hybrid recommendation unit also analyzes the user's viewing history and suggests different genre combinations. For example, if the user likes science fiction movies and drama movies, it suggests works that combine science fiction and drama. In this way, by suggesting different genre combinations, the hybrid recommendation unit provides the user with a new viewing experience.

[0042] The hybrid recommendation unit can analyze the viewing history of the user's friends and family and make hybrid recommendations based on shared preferences. For example, the hybrid recommendation unit analyzes the viewing history of the user's friends and family and makes hybrid recommendations based on shared preferences. For example, it suggests family movies that the whole family can enjoy. The hybrid recommendation unit also suggests works that share common interests based on the viewing history of the user's friends and family. For example, it suggests works that combine action and comedy that can be enjoyed together with friends. The hybrid recommendation unit also analyzes the viewing history of the user's friends and family and suggests new or popular works based on shared preferences. For example, it suggests new movies that the whole family will be interested in. In this way, the user's viewing experience is improved by analyzing the viewing history of friends and family and making hybrid recommendations based on shared preferences.

[0043] The hybrid recommendation unit can provide hybrid recommendations of different media. For example, the hybrid recommendation unit provides hybrid recommendations of manga and movies based on user profile data. For example, it may suggest movie adaptations of manga that the user likes. The hybrid recommendation unit may also analyze the user's viewing history and provide hybrid recommendations of music and podcasts. For example, it may suggest podcasts that include interviews of artists that the user likes. The hybrid recommendation unit may also provide hybrid recommendations of different media according to the user's preferences. For example, it may suggest manga related to the soundtrack of a movie that the user likes. In this way, by providing hybrid recommendations of different media, a new viewing experience is provided to the user.

[0044] The hybrid recommendation unit can make recommendations that combine works from different cultures according to the user's preferences. The hybrid recommendation unit makes recommendations that combine works from different cultures based on, for example, user profile data. For example, it proposes works that combine Japanese anime and American movies. The hybrid recommendation unit also analyzes the user's viewing history and makes recommendations that combine works from different cultures. For example, it proposes works that combine Korean dramas and French movies. The hybrid recommendation unit also makes recommendations that combine works from different cultures according to the user's preferences. For example, it proposes works that combine Indian movies and British television dramas. In this way, by making recommendations that combine works from different cultures, a new viewing experience is provided to the user.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The user profile data input unit can collect the user's health data and reflect it in entertainment preferences. For example, it can analyze the user's exercise and sleep data obtained from a fitness tracker to suggest relaxing or energetic works. It can also analyze the user's food records to recommend works that can be enjoyed after a specific meal. It can also measure the user's stress level and suggest entertainment that helps relieve stress. This provides personalized recommendations based on the user's health status.

[0047] The user profile data input unit can collect information about the user's hobbies and special skills and reflect this in entertainment preferences. For example, it can recommend works related to the user's hobbies, such as sports or art. It can also suggest works related to the user's special skills, such as playing musical instruments or cooking. It can also recommend related entertainment based on information about the clubs and circles the user participates in. This provides personalized recommendations based on the user's hobbies and special skills.

[0048] The user profile data input unit can analyze a user's learning history and reflect it in their entertainment preferences. For example, it can recommend works related to what the user learned in online courses or seminars. It can also suggest related documentaries and movies based on the academic books and research papers the user has read. It can also recommend entertainment related to the user's academic fields of interest. This provides personalized recommendations based on the user's learning history.

[0049] The user profile data input unit can reflect entertainment preferences based on the user's life events. For example, when a user celebrates a life event such as marriage or childbirth, related works can be recommended. It can also suggest works that will refresh the user when the user experiences a major change such as moving or changing jobs. It can also recommend entertainment that the user can enjoy on specific anniversaries or holidays. This provides personalized recommendations based on the user's life events.

[0050] The user profile data input unit can collect information about the user's pet and reflect it in entertainment preferences. For example, it can recommend pet-related works based on the type and personality of the user's pet. It can also suggest entertainment that the user can enjoy with their pet. It can also recommend related documentaries and movies based on the user's information about pet training and care. This provides personalized recommendations based on the user's pet.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: In the user profile data input section, the user inputs their preferences and past browsing history, such as their favorite genre, favorite author, and a list of works they have viewed in the past. Step 2: The analysis unit analyzes the user profile data input by the user profile data input unit. For example, the generation AI uses data mining techniques and machine learning algorithms to understand the user's preferences. Step 3: The recommendation unit recommends the most suitable manga or movies based on the user profile data analyzed by the analysis unit. For example, it may recommend works in a style or genre similar to works the user has previously watched. It may also suggest new or popular works that match the user's preferences. Step 4: The information provider provides detailed information about the works recommended by the recommendation provider, such as comments from directors and writers, interviews, and behind-the-scenes information. Step 5: The hybrid recommendation unit recommends new manga and movies that combine different styles and genres based on the user profile data analyzed by the analysis unit. For example, it might suggest a movie that combines a user's favorite action movie and a romance movie.

[0053] (Example 2) The entertainment recommendation system according to an embodiment of the present invention uses a generative AI to analyze user profile data and provide optimal manga and movie recommendations, thereby providing users with an infinite number of entertainment combinations and deepening their attachment to the content.

[0054] An entertainment recommendation system according to an embodiment includes a user profile data input unit, an analysis unit, a recommendation unit, an information provision unit, and a hybrid recommendation unit. The user profile data input unit inputs the user's preferences and past browsing history. For example, the user inputs their favorite genres, favorite authors, and a list of previously viewed works. The analysis unit analyzes the user profile data input by the user profile data input unit. For example, the generation AI uses data mining techniques and machine learning algorithms to understand the user's preferences. The recommendation unit recommends optimal manga and movies based on the user profile data analyzed by the analysis unit. For example, the recommendation unit recommends works with a similar style or genre to works the user has previously viewed. The recommendation unit also suggests new and popular works that match the user's preferences. The information provision unit provides detailed information about the works recommended by the recommendation unit. For example, the information provision unit provides comments, interviews, and behind-the-scenes information from directors and writers. The hybrid recommendation unit recommends new manga and movies that combine different styles and genres based on the user profile data analyzed by the analysis unit. For example, it may recommend a movie that combines an action movie and a romance movie that the user likes. In this way, the entertainment recommendation system according to the embodiment provides the user with the most suitable manga and movie recommendations, and improves the user's entertainment experience by suggesting detailed information about the movies and movies in new genres.

[0055] The user profile data input unit can use an emotion estimation function to analyze the emotional state at the time of input and correct the profile data based on the emotional state. For example, when a user inputs profile data, the user profile data input unit analyzes the emotional state in real time using facial expression recognition technology. For example, the user's facial expression is read through a camera and an emotion score is calculated. The user profile data input unit also analyzes the emotional state using voice analysis technology when the user inputs profile data. For example, the tone and pitch of the user's voice are analyzed through a microphone and an emotion score is calculated. The user profile data input unit also analyzes the speed and strength of keyboard input when the user inputs profile data to estimate the emotional state. For example, the emotion score is calculated based on the rhythm and force of input. This allows for more accurate recommendations by correcting the profile data based on the user's emotional state.

[0056] The user profile data input unit can collect public information from social media accounts and reflect it in the profile data. For example, the user profile data input unit analyzes public posts from the user's social media accounts and reflects the user's interests and concerns in the profile data. For example, it extracts topics and hashtags that the user frequently posts to. The user profile data input unit also collects information about accounts and groups followed by the user from the user's social media accounts and reflects it in the profile data. For example, it estimates the user's preferences based on information about film directors and writers followed by the user. The user profile data input unit also collects data about posts that the user has "liked" or "shared" from the user's social media accounts and reflects it in the profile data. For example, it estimates the user's preferences based on information about movies and manga that the user has "liked." In this way, by reflecting information from social media in the profile data, the user's preferences can be more accurately understood.

[0057] The user profile data input unit can collect biometric information in real time, analyze the emotional state, and reflect the result in the profile data. For example, when the user inputs the profile data, the user profile data input unit measures the heart rate in real time using a wearable device and analyzes the emotional state. For example, the emotional score is calculated based on fluctuations in the heart rate. Furthermore, when the user inputs the profile data, the user profile data input unit measures brain waves in real time using an electroencephalogram sensor and analyzes the emotional state. For example, the emotional score is calculated based on a specific electroencephalogram pattern. Furthermore, when the user inputs the profile data, the user profile data input unit measures electrodermal activity and analyzes the emotional state. For example, the emotional score is calculated based on fluctuations in the electrical resistance of the skin. In this way, by analyzing the emotional state using biometric information and reflecting it in the profile data, more accurate recommendations are possible.

[0058] The user profile data input unit can analyze the user's past events and travel history and reflect it in their entertainment preferences. The user profile data input unit, for example, analyzes the user's ticket purchase history for events they have attended in the past and reflects it in their entertainment preferences. For example, it estimates preferences based on their attendance history at concerts and film festivals. The user profile data input unit also analyzes information about travel destinations the user has visited in the past and reflects it in their entertainment preferences. For example, it recommends works related to a particular region or culture. The user profile data input unit also analyzes the user's history of workshops and seminars they have attended in the past and reflects it in their entertainment preferences. For example, it estimates preferences based on their participation history in filmmaking or manga creation workshops. This enables more personalized recommendations by analyzing the user's past event and travel history and reflecting it in their entertainment preferences.

[0059] The user profile data input unit can analyze purchase history and add preference trends to the profile data. The user profile data input unit, for example, analyzes the user's purchase history on an online shopping site and adds preference trends to the profile data. For example, preferences are estimated based on the genres of movies and manga purchased. The user profile data input unit also analyzes the user's purchase history at bookstores and movie theaters and adds preference trends to the profile data. For example, preferences are estimated based on information about purchased books and movie tickets. The user profile data input unit also analyzes the user's subscription service usage history and adds preference trends to the profile data. For example, preferences are estimated based on subscription magazines and streaming service viewing history. In this way, analyzing purchase history and adding preference trends to the profile data enables more accurate recommendations.

[0060] The user profile data input unit can use an emotion estimation function to analyze the emotions of a user when entering data in real time and provide an interface for eliciting positive emotions. For example, when a user enters profile data, the user profile data input unit uses the emotion estimation function to analyze the emotions in real time and provide an interface for eliciting positive emotions. For example, the user profile data input unit displays an animation or message that makes the user smile. Furthermore, when a user enters profile data, the user profile data input unit uses the emotion estimation function to analyze the emotions in real time and provide an audio guide for eliciting positive emotions. For example, encouraging words or positive music is played. Furthermore, when a user enters profile data, the user profile data input unit uses the emotion estimation function to analyze the emotions in real time and provide visual feedback for eliciting positive emotions. For example, colors and designs can be used to enhance the user's mood. This improves the user's input experience by providing an interface that analyzes the user's emotions in real time and elicits positive emotions.

[0061] The recommendation unit can use the emotion estimation function to make recommendations according to the user's emotional state. For example, the recommendation unit analyzes the user's emotional state in real time and recommends the most suitable manga or movie based on the results. For example, if the user is feeling stressed, the recommendation unit suggests a relaxing movie. The recommendation unit also analyzes the user's emotional state and recommends a movie that matches the emotion. For example, if the user is feeling happy, the recommendation unit suggests a comedy movie. The recommendation unit also analyzes the user's emotional state and adjusts the recommendation according to changes in emotion. For example, if the user is feeling sad, the recommendation unit suggests a moving movie. In this way, recommendations according to the user's emotional state are made, thereby improving user satisfaction.

[0062] The recommendation unit can make recommendations that take into account the viewing time period and viewing environment based on the user's past viewing history. For example, the recommendation unit analyzes the user's past viewing history and makes recommendations according to the viewing time period. For example, it suggests relaxing works to a user who often watches late at night. The recommendation unit also analyzes the user's viewing environment and recommends optimal works. For example, it suggests short films or episodes to a user who often watches during their commute. The recommendation unit also recommends works that are suitable for specific time periods or environments based on the user's viewing history. For example, it suggests feature-length films to a user who often watches on weekends. In this way, recommendations that take into account the viewing time period and viewing environment improve the user's viewing experience.

[0063] The recommendation unit can analyze the viewing history of the user's friends and family and make recommendations based on shared preferences. For example, the recommendation unit analyzes the viewing history of the user's friends and family and recommends works based on shared preferences. For example, it can suggest family movies that the whole family can enjoy. The recommendation unit also recommends works that share common interests based on the viewing history of the user's friends and family. For example, it can suggest action movies that can be enjoyed together with friends. The recommendation unit also analyzes the viewing history of the user's friends and family and recommends new or popular works based on shared preferences. For example, it can suggest new movies that the whole family will be interested in. In this way, the user's viewing experience is improved by analyzing the viewing history of friends and family and making recommendations based on shared preferences.

[0064] The recommendation unit can also simultaneously recommend music and podcasts based on the user's preferences. For example, the recommendation unit can recommend not only manga and movies, but also music and podcasts based on the user's profile data. For example, it can suggest music and podcasts in the user's favorite genre. The recommendation unit can also analyze the user's viewing history and recommend related music and podcasts. For example, it can suggest soundtracks for specific movies and related podcasts. The recommendation unit can also build a system that recommends music and podcasts based on the user's preferences. For example, it can suggest content related to the user's favorite artists or themes. This improves the user's entertainment experience by simultaneously recommending music and podcasts.

[0065] The recommendation unit can also provide recommendations for books and magazines according to the user's preferences. For example, the recommendation unit recommends not only manga and movies but also books and magazines based on the user's profile data. For example, it suggests books and magazines in the user's favorite genre. The recommendation unit also analyzes the user's viewing history and recommends related books and magazines. For example, it suggests books and magazines related to a specific movie or manga. The recommendation unit also builds a system that recommends books and magazines according to the user's preferences. For example, it suggests content related to the user's favorite author or theme. In this way, by providing book and magazine recommendations, the user's entertainment experience is improved.

[0066] The recommendation unit can use the emotion estimation function to analyze the emotions felt by the user while watching in real time and suggest the next work to watch. For example, the recommendation unit analyzes the emotions felt by the user while watching in real time and suggests the next work to watch based on the results. For example, if the user is moved, it suggests an emotional work. The recommendation unit also analyzes the emotions felt by the user while watching and suggests the next work to watch depending on changes in emotions. For example, if the user is excited, it suggests an action movie. The recommendation unit also analyzes the emotions felt by the user while watching and suggests the next work to watch depending on the emotional state. For example, if the user is relaxed, it suggests a comedy movie. In this way, the user's viewing experience is improved by analyzing the emotions felt while watching in real time and suggesting the next work to watch.

[0067] The information providing unit can use the emotion estimation function to provide detailed information that is likely to interest the user preferentially. For example, the information providing unit analyzes the emotions felt by the user while watching a work in real time, and provides detailed information that is likely to interest the user based on the results. For example, if the user is moved, it suggests behind-the-scenes stories and interviews. The information providing unit also analyzes the user's emotional state and provides detailed information that matches the emotion preferentially. For example, if the user is excited, it suggests making-of footage of an action scene. The information providing unit also analyzes the user's emotional state and provides detailed information according to changes in emotion. For example, if the user is relaxed, it suggests cast interviews and behind-the-scenes footage. In this way, by providing detailed information that is likely to interest the user preferentially, the user's understanding of and interest in the work is deepened.

[0068] The information providing unit can automatically collect historical and cultural background related to a work and provide it to the user. For example, the information providing unit automatically collects historical background related to a work and provides it to the user. For example, it explains historical facts and events behind a period drama film. The information providing unit also automatically collects cultural background related to a work and provides it to the user. For example, it provides background information for films and manga based on a particular culture or tradition. The information providing unit also comprehensively collects historical and cultural background related to a work and provides it to the user. For example, it provides detailed information about the region and era in which the work is set. In this way, providing historical and cultural background deepens the user's understanding of the work.

[0069] The information provision unit provides detailed technical information and information about the production process of a work, thereby deepening the user's understanding. The information provision unit, for example, provides detailed information about the production process of a work. For example, it provides footage from the film set and comments from the director. The information provision unit also provides detailed technical information about the work. For example, it explains the production process of special effects and CG. The information provision unit also provides comprehensive information about the production process and detailed technical information about the work. For example, it provides interviews with cast and staff and technical commentary. In this way, by providing detailed information about the production process and technical information, the user can deepen their understanding of the work.

[0070] The information providing unit can also simultaneously provide information about merchandise and events related to the work. The information providing unit, for example, provides information about merchandise related to the work. For example, it provides information about official movie merchandise and limited edition items. The information providing unit also provides information about events related to the work. For example, it provides information about movie premieres and fan meetings. The information providing unit also provides integrated information about merchandise and events related to the work. For example, it provides links to the movie's official website and online store. In this way, providing merchandise and event information deepens the user's interest in the work.

[0071] The information providing unit can provide information on fan communities and forums related to a work, thereby promoting interaction between users. The information providing unit, for example, provides information on fan communities related to a work. For example, it provides links to online forums and SNS groups. The information providing unit also provides information on fan events related to a work. For example, it provides information on fan meetings and cosplay events. The information providing unit also provides integrated information on fan communities and forums related to a work, promoting interaction between users. For example, it provides links to sites for posting fan art and fan fiction. In this way, providing information on fan communities and forums promotes interaction between users.

[0072] The information providing unit can use the emotion estimation function to provide detailed information that is likely to interest the user in real time. For example, the information providing unit analyzes the user's emotional state in real time and provides detailed information that is likely to interest the user based on the results. For example, if the user is excited, it suggests making-of footage of an action scene. The information providing unit also analyzes the user's emotional state and provides detailed information that matches the emotion in real time. For example, if the user is moved, it suggests behind-the-scenes stories and interviews. The information providing unit also analyzes the user's emotional state and provides detailed information in real time according to changes in emotion. For example, if the user is relaxed, it suggests cast interviews and behind-the-scenes footage. In this way, by providing detailed information that is likely to interest the user in real time, the user's understanding of and interest in the work is deepened.

[0073] The hybrid recommendation unit can use the emotion estimation function to make hybrid recommendations according to the user's emotional state. For example, the hybrid recommendation unit analyzes the user's emotional state in real time and makes optimal hybrid recommendations based on the results. For example, if the user is feeling stressed, it suggests works that are relaxing. The hybrid recommendation unit also analyzes the user's emotional state and makes hybrid recommendations that match the user's emotions. For example, if the user is feeling happy, it suggests works that combine comedy and action. The hybrid recommendation unit also analyzes the user's emotional state and adjusts the hybrid recommendation according to changes in emotions. For example, if the user is feeling sad, it suggests moving works. In this way, by making hybrid recommendations according to the user's emotional state, user satisfaction is improved.

[0074] The hybrid recommendation unit can suggest different genre combinations based on the user's past viewing history. For example, the hybrid recommendation unit analyzes the user's past viewing history and suggests different genre combinations. For example, if the user likes action movies and romance movies, it suggests works that combine action and romance. The hybrid recommendation unit also suggests different genre combinations based on the user's viewing history. For example, if the user likes horror movies and comedy movies, it suggests works that combine horror and comedy. The hybrid recommendation unit also analyzes the user's viewing history and suggests different genre combinations. For example, if the user likes science fiction movies and drama movies, it suggests works that combine science fiction and drama. In this way, by suggesting different genre combinations, the hybrid recommendation unit provides the user with a new viewing experience.

[0075] The hybrid recommendation unit can analyze the viewing history of the user's friends and family and make hybrid recommendations based on shared preferences. For example, the hybrid recommendation unit analyzes the viewing history of the user's friends and family and makes hybrid recommendations based on shared preferences. For example, it suggests family movies that the whole family can enjoy. The hybrid recommendation unit also suggests works that share common interests based on the viewing history of the user's friends and family. For example, it suggests works that combine action and comedy that can be enjoyed together with friends. The hybrid recommendation unit also analyzes the viewing history of the user's friends and family and suggests new or popular works based on shared preferences. For example, it suggests new movies that the whole family will be interested in. In this way, the user's viewing experience is improved by analyzing the viewing history of friends and family and making hybrid recommendations based on shared preferences.

[0076] The hybrid recommendation unit can provide hybrid recommendations of different media. For example, the hybrid recommendation unit provides hybrid recommendations of manga and movies based on user profile data. For example, it may suggest movie adaptations of manga that the user likes. The hybrid recommendation unit may also analyze the user's viewing history and provide hybrid recommendations of music and podcasts. For example, it may suggest podcasts that include interviews of artists that the user likes. The hybrid recommendation unit may also provide hybrid recommendations of different media according to the user's preferences. For example, it may suggest manga related to the soundtrack of a movie that the user likes. In this way, by providing hybrid recommendations of different media, a new viewing experience is provided to the user.

[0077] The hybrid recommendation unit can make recommendations that combine works from different cultures according to the user's preferences. The hybrid recommendation unit makes recommendations that combine works from different cultures based on, for example, user profile data. For example, it proposes works that combine Japanese anime and American movies. The hybrid recommendation unit also analyzes the user's viewing history and makes recommendations that combine works from different cultures. For example, it proposes works that combine Korean dramas and French movies. The hybrid recommendation unit also makes recommendations that combine works from different cultures according to the user's preferences. For example, it proposes works that combine Indian movies and British television dramas. In this way, by making recommendations that combine works from different cultures, a new viewing experience is provided to the user.

[0078] The hybrid recommendation unit can use the emotion estimation function to provide hybrid recommendations that are likely to be of most interest to the user in real time. For example, the hybrid recommendation unit analyzes the user's emotional state in real time and provides hybrid recommendations that are likely to be of most interest to the user based on the results. For example, if the user is excited, works that combine action and comedy are suggested. The hybrid recommendation unit also analyzes the user's emotional state and provides hybrid recommendations that match the user's emotions in real time. For example, if the user is moved, works that combine drama and romance are suggested. The hybrid recommendation unit also analyzes the user's emotional state and provides hybrid recommendations in real time according to changes in emotions. For example, if the user is relaxed, works that combine comedy and fantasy are suggested. This improves the user's viewing experience by providing hybrid recommendations that are likely to be of most interest to the user in real time.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The user profile data input unit can collect the user's health data and reflect it in entertainment preferences. For example, it can analyze the user's exercise and sleep data obtained from a fitness tracker to suggest relaxing or energetic works. It can also analyze the user's food records to recommend works that can be enjoyed after a specific meal. It can also measure the user's stress level and suggest entertainment that helps relieve stress. This provides personalized recommendations based on the user's health status.

[0081] The user profile data input unit can collect information about the user's hobbies and special skills and reflect this in entertainment preferences. For example, it can recommend works related to the user's hobbies, such as sports or art. It can also suggest works related to the user's special skills, such as playing musical instruments or cooking. It can also recommend related entertainment based on information about the clubs and circles the user participates in. This provides personalized recommendations based on the user's hobbies and special skills.

[0082] The user profile data input unit can analyze a user's learning history and reflect it in their entertainment preferences. For example, it can recommend works related to what the user learned in online courses or seminars. It can also suggest related documentaries and movies based on the academic books and research papers the user has read. It can also recommend entertainment related to the user's academic fields of interest. This provides personalized recommendations based on the user's learning history.

[0083] The user profile data input unit can reflect entertainment preferences based on the user's life events. For example, when a user celebrates a life event such as marriage or childbirth, related works can be recommended. It can also suggest works that will refresh the user when the user experiences a major change such as moving or changing jobs. It can also recommend entertainment that the user can enjoy on specific anniversaries or holidays. This provides personalized recommendations based on the user's life events.

[0084] The user profile data input unit can collect information about the user's pet and reflect it in entertainment preferences. For example, it can recommend pet-related works based on the type and personality of the user's pet. It can also suggest entertainment that the user can enjoy with their pet. It can also recommend related documentaries and movies based on the user's information about pet training and care. This provides personalized recommendations based on the user's pet.

[0085] The recommendation unit can suggest entertainment to uplift the user's emotions based on the user's emotional state. For example, if the user is feeling down, the recommendation unit can suggest uplifting comedy movies or music. If the user is excited, the recommendation unit can suggest action movies or energetic music. Furthermore, if the user wants to relax, the recommendation unit can suggest relaxing documentaries or nature sounds. In this way, entertainment based on the user's emotional state can be provided, thereby uplifting the user's emotions.

[0086] The recommendation unit can suggest entertainment to stabilize emotions based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing movies or music. If the user is feeling anxious, it can suggest documentaries or podcasts that will give a sense of security. Furthermore, if the user is feeling angry, it can suggest meditation music or nature videos to calm the user. In this way, entertainment based on the user's emotional state can be provided, stabilizing emotions.

[0087] The recommendation unit can suggest entertainment for sharing emotions based on the user's emotional state. For example, if the user is feeling happy, it can suggest comedy movies or music that can be enjoyed with friends and family. If the user is moved, it can also suggest documentaries or movies that can share the emotion. Furthermore, if the user is excited, it can also suggest action movies or sporting events that can be enjoyed together. In this way, entertainment based on the user's emotional state can be provided, allowing emotions to be shared.

[0088] The recommendation unit can suggest entertainment for exploring emotions based on the user's emotional state. For example, if the user wants to explore themselves, documentaries and movies about emotions can be suggested. If the user wants to deepen their understanding of emotions, podcasts and books about psychology can be suggested. Furthermore, if the user wants to learn how to express emotions, workshops and seminars on emotional expression can be suggested. In this way, entertainment based on the user's emotional state can be provided, allowing the user to explore emotions.

[0089] The recommendation unit can suggest entertainment to soothe the user's emotions based on the user's emotional state. For example, if the user is feeling sad, it can suggest movies or music that will soothe the user. If the user is feeling tired, it can suggest refreshing documentaries or nature videos. Furthermore, if the user is feeling lonely, it can suggest podcasts or books that will resonate with the user. In this way, entertainment based on the user's emotional state can be provided, helping to soothe the user's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: In the user profile data input section, the user inputs their preferences and past browsing history, such as their favorite genre, favorite author, and a list of works they have viewed in the past. Step 2: The analysis unit analyzes the user profile data input by the user profile data input unit. For example, the generation AI uses data mining techniques and machine learning algorithms to understand the user's preferences. Step 3: The recommendation unit recommends the most suitable manga or movies based on the user profile data analyzed by the analysis unit. For example, it may recommend works in a style or genre similar to works the user has previously watched. It may also suggest new or popular works that match the user's preferences. Step 4: The information provider provides detailed information about the works recommended by the recommendation provider, such as comments from directors and writers, interviews, and behind-the-scenes information. Step 5: The hybrid recommendation unit recommends new manga and movies that combine different styles and genres based on the user profile data analyzed by the analysis unit. For example, it might suggest a movie that combines a user's favorite action movie and a romance movie.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

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

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user profile data input unit for inputting user profile data; an analysis unit that analyzes the user profile data input by the user profile data input unit; a recommendation unit that recommends optimal manga and movies based on the user profile data analyzed by the analysis unit; an information providing unit that provides detailed information about the work recommended by the recommendation unit; a hybrid recommendation unit that recommends new manga and movies that combine styles and genres based on the user profile data analyzed by the analysis unit. A system characterized by:

2. The user profile data input unit includes: Analyzes the emotional state at the time of input and corrects profile data based on said emotional state 2. The system of claim 1.

3. The user profile data input unit includes: Collect public information from your social media accounts and incorporate it into your profile data 2. The system of claim 1.

4. The user profile data input unit includes: Collect biometric information in real time, analyze emotional states, and reflect them in profile data 2. The system of claim 1.

5. The user profile data input unit includes: Analyzing users' past events and travel history to reflect their entertainment preferences 2. The system of claim 1.

6. The user profile data input unit includes: Analyze purchase history and add the preferences to the profile data 2. The system of claim 1.

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