system

Abstract

The system according to this embodiment aims to provide entertainment-related information based on the user's viewing and listening history. [Solution] The system according to the embodiment comprises a learning unit, a recommendation unit, a notification unit, and an information provision unit. The learning unit collects and analyzes the user's viewing or listening history. The recommendation unit recommends movies and music based on the data obtained by the learning unit. The notification unit notifies the user of entertainment-related schedules and events based on the user's interests. The information provision unit provides the latest information and background stories of celebrities and artists based on the user's interests.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, providing entertainment-related information based on a user's viewing history or listening history has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide entertainment-related information based on a user's viewing history or listening history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a recommendation unit, a notification unit, and an information provision unit. The learning unit collects and analyzes the user's viewing or listening history. The recommendation unit recommends movies and music based on the data obtained by the learning unit. The notification unit notifies the user of entertainment-related schedules and events based on the user's interests. The information provision unit provides the latest information and background stories of celebrities and artists based on the user's interests. [Effects of the Invention]

[0007] The system according to this embodiment can provide entertainment-related information based on the user's viewing and listening history. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant for the entertainment industry according to an embodiment of the present invention is a system that recommends movies and music based on the user's preferences, notifies the user of entertainment-related schedules and events, and provides the latest information and background stories of celebrities and artists. The AI ​​assistant for the entertainment industry learns the user's movie and music preferences and recommends movies and music based on those learned preferences. Furthermore, it notifies the user of entertainment-related schedules and events and provides the latest information and background stories of celebrities and artists. For example, the AI ​​assistant for the entertainment industry collects data on movies the user has watched and music the user has listened to, and the AI ​​analyzes this data. For example, by collecting information on the genre of movies the user has watched and the artists the AI ​​has listened to, the AI ​​can understand the user's preferences. Next, it recommends movies and music to the user based on the learned preferences. For example, if the user likes action movies, the AI ​​will recommend the latest action movies. Also, if the user likes the music of a particular artist, the AI ​​will recommend the artist's new songs. Furthermore, it provides notifications of entertainment-related schedules and events. For example, it will notify the user of the release date of a movie they are interested in or the date of a concert. This allows the user to stay informed about the latest entertainment information. Finally, it provides the latest information and background stories of celebrities and artists. For example, it can provide the latest news and interview articles about actors and artists that the user is interested in. This allows users to stay up-to-date on their favorite celebrities and artists. This system enables users to enjoy movies and music that suit their tastes and stay informed about the latest entertainment news. Furthermore, knowing the latest information and background stories of celebrities and artists can further enhance their enjoyment of entertainment. As a result, AI assistants in the entertainment industry can recommend movies and music based on user preferences, notify users of entertainment-related schedules and events, and provide the latest information and background stories of celebrities and artists.

[0029] The AI ​​assistant for the entertainment industry according to this embodiment comprises a learning unit, a recommendation unit, a notification unit, and an information provision unit. The learning unit collects and analyzes the user's viewing or listening history. For example, the learning unit collects data on movies the user has watched and music the user has listened to, and the AI ​​analyzes this data. For example, the learning unit collects information on the genres and artists of movies the user has watched, and the AI ​​analyzes this data to understand the user's preferences. The recommendation unit recommends movies and music based on the data obtained by the learning unit. For example, if the user likes action movies, the AI ​​will recommend the latest action movies. Also, if the user likes the music of a particular artist, the AI ​​will recommend the artist's new songs. The notification unit provides notifications of entertainment-related schedules and events based on the user's interests. For example, the notification unit will notify the user of the release date of a movie they are interested in or the date of a concert. This allows the user to stay informed about the latest entertainment information. The information provision unit provides the latest information and background stories of celebrities and artists based on the user's interests. The information provision section provides, for example, the latest news and interview articles about actors and artists that the user is interested in. This allows the user to always be informed about their favorite celebrities and artists. As a result, the AI ​​assistant for the entertainment industry according to this embodiment can recommend movies and music based on the user's preferences, notify them of entertainment-related schedules and events, and provide the latest information and background stories about celebrities and artists.

[0030] The learning unit collects and analyzes users' viewing or listening history. Specifically, it collects data on movies watched and music listened to by users, and the AI ​​analyzes this data. For example, by collecting information on the genres and artists of movies watched by users and having the AI ​​analyze this information, it is possible to understand the user's preferences. The learning unit utilizes natural language processing (NLP) and machine learning algorithms to analyze users' viewing and listening history in detail. For example, NLP can be used to analyze movie reviews and song lyrics to extract the user's emotions and preferences. Furthermore, machine learning algorithms can be used to analyze users' viewing and listening patterns and predict future viewing and listening trends. In addition, the learning unit can perform more accurate analysis by combining user demographic information and past behavioral data. For example, by considering information such as the user's age, gender, and place of residence, it can identify popular movies and music in specific age groups or regions and provide content that matches the user's preferences. As a result, the learning unit can analyze users' viewing and listening history in detail and accurately understand their preferences and interests.

[0031] The recommendation unit recommends movies and music based on data obtained by the learning unit. Specifically, if a user likes action movies, the AI ​​will recommend the latest action movies. Similarly, if a user likes the music of a particular artist, the AI ​​will recommend that artist's new songs. The recommendation unit uses collaborative filtering and content-based recommendation algorithms to provide users with the most suitable content. Collaborative filtering is a method that recommends movies and music watched or listened to by users with similar preferences, based on the viewing and listening history of other users. On the other hand, content-based recommendation algorithms analyze the characteristics of movies and music that a user has watched or listened to in the past and recommend content with similar characteristics. Furthermore, the recommendation unit can monitor user behavior in real time and dynamically update its recommendations. For example, if a user watches a new movie, the viewing history is immediately sent to the learning unit, and the recommendation algorithm is updated to recommend content based on the user's latest preferences. The recommendation unit can also collect user feedback and continuously improve the accuracy of its recommendation algorithms. As a result, the recommendation unit can recommend movies and music that match the user's preferences with high accuracy, improving the user's entertainment experience.

[0032] The notification unit provides notifications about entertainment-related schedules and events based on the user's interests. Specifically, it notifies users of release dates for movies they are interested in and concert dates for concerts they are interested in. The notification unit can integrate with the user's calendar and scheduling apps to automatically add important events. For example, as the release date of a movie the user is interested in approaches, the notification unit will add the information to the user's calendar and set a reminder. The notification unit can also use the user's location information to provide information about nearby events and concerts. For example, if the user is a fan of a particular artist, it will notify them when that artist's concert is held nearby. Furthermore, the notification unit can predict and notify users of new events and schedules that they might be interested in based on their past behavioral data. As a result, the notification unit ensures that users stay informed about the latest entertainment information and can enhance their entertainment experience.

[0033] The Information Department provides users with the latest information and background stories about celebrities and artists based on their interests. Specifically, it provides the latest news and interview articles about actors and artists that users are interested in. The Information Department collects the latest information from news sites, social media, and official websites on the internet and provides it to users. For example, if a user is interested in a particular actor, it will provide information about that actor's latest movie and interview articles. The Information Department can also create a customized news feed based on user interests, providing information that users are interested in in one place. Furthermore, the Information Department can continuously improve the accuracy and relevance of the information it provides based on user feedback. For example, if a user gives a high rating to a particular news article, it will prioritize providing articles with similar content. The Information Department can also provide relevant background stories and past interview articles based on user interests. In this way, the Information Department can keep users up-to-date on information about their favorite celebrities and artists, further enriching their entertainment experience.

[0034] The learning unit can analyze a user's past viewing and listening history and select the optimal data collection method. For example, the learning unit can identify genres that a user frequently watches and prioritize the collection of data related to those genres. It can also collect data during specific time periods if a user tends to watch during those times. Furthermore, if a user frequently watches on a particular device, the learning unit can prioritize data collection from that device. This allows the optimal data collection method to be selected by analyzing the user's past viewing and listening history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For instance, the learning unit can input the user's viewing and listening history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The learning unit can filter viewing and listening history data based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the learning unit will prioritize collecting viewing and listening history related to that hobby. The learning unit can also collect data related to an event after the user participates in that event. Furthermore, if the user is in a specific life stage (e.g., marriage, childbirth), the learning unit can collect data related to that life stage. This allows for the collection of more relevant data by filtering the data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the user's lifestyle and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0036] The learning unit can prioritize the collection of highly relevant viewing and listening history by considering the user's geographical location. For example, if the user is traveling, the learning unit can prioritize the collection of movies and music history related to that region. It can also prioritize the collection of movies and music history related to a specific city if the user lives in that city. Furthermore, if the user is interested in a particular country, the learning unit can prioritize the collection of movies and music history related to that country. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant history.

[0037] The learning unit can analyze a user's social media activity and collect relevant history when collecting viewing and listening history. For example, the learning unit can prioritize collecting the history of movies and music that the user has shared on social media. It can also collect history related to artists and actors that the user follows on social media. Furthermore, the learning unit can collect history related to groups and communities that the user participates in on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant history.

[0038] The recommendation system can adjust the level of detail in recommendations based on the importance of the movies and music. For example, it can provide detailed information about movies and music that the user is particularly interested in. It can also provide concise information about movies and music that the user is not very interested in. Furthermore, if the user has started to become interested in a new genre, the recommendation system can provide detailed information about that genre. In this way, by adjusting the level of detail in recommendations based on the importance of the movies and music, the system can provide the most suitable information for the user. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input movie and music importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recommendations.

[0039] The recommendation system can apply different recommendation algorithms depending on the category of film or music. For example, for action films, it might apply an algorithm that emphasizes thrills and excitement. For romance films, it might apply an algorithm that emphasizes emotions and relationships. Furthermore, for classical music, it might apply an algorithm that emphasizes historical background and composer information. By applying different recommendation algorithms depending on the category of film or music, more appropriate recommendations can be made. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input film and music category data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0040] The recommendation system can prioritize recommendations based on the release dates of movies and music. For example, it might prioritize recommending the latest movies and music. It can also prioritize recommending sequels or related works to movies and music that the user has previously watched or listened to. Furthermore, it can prioritize recommending movies and music that the user is interested in at a particular time. This allows the system to provide up-to-date information by prioritizing recommendations based on the release dates of movies and music. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system could input movie and music release date data into a generating AI and have the generating AI determine the recommendation priorities.

[0041] The recommendation system can adjust the order of recommendations based on the relevance of movies and music. For example, it can prioritize recommending works related to movies and music the user has watched. It can also prioritize recommending works related to genres the user is interested in. Furthermore, it can prioritize recommending works related to artists and actors the user follows. By adjusting the order of recommendations based on the relevance of movies and music, the system can provide the user with the most relevant information. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input movie and music relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0042] The notification unit can select the optimal notification method by referring to the user's past event participation history when sending a notification. For example, the notification unit can send notifications for relevant events based on the types of events the user has previously participated in. The notification unit can also select the optimal notification timing based on the time of day of events the user has previously participated in. Furthermore, the notification unit can send notifications for relevant events based on the location of events the user has previously participated in. This allows the notification unit to select the optimal notification method by referring to the user's past event participation history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's event participation history data into a generating AI and have the generating AI select the optimal notification method.

[0043] The notification unit can customize the content of notifications based on the user's current schedule. For example, the notification unit can send notifications for relevant events based on appointments registered in the user's calendar. It can also send event notifications during times when the user has free time in their schedule. Furthermore, the notification unit can select the optimal notification timing based on the user's schedule. This allows for more appropriate notifications by customizing the content based on the user's current schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's schedule data into a generating AI and have the generating AI customize the content of the notifications.

[0044] The notification unit can select the optimal notification method by considering the user's geographical location information when sending a notification. For example, if the user is in a specific region, the notification unit can send notifications about events related to that region. It can also send notifications about events related to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the notification unit can send notifications about events around their home. This allows the system to select the optimal notification method by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0045] The notification unit can analyze the user's social media activity and customize the content of notifications when sending them. For example, the notification unit can send notifications related to events the user has shared on social media. It can also send notifications related to artists or actors the user follows on social media. Furthermore, it can send notifications related to groups or communities the user participates in on social media. This allows for the customization of notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI customize the content of the notifications.

[0046] The information provision unit can provide the most relevant information by referring to the user's past interest history when providing information. For example, the information provision unit can provide the latest information on actors or artists that the user has been interested in in the past. It can also provide background stories related to movies or music that the user has watched or listened to in the past. Furthermore, the information provision unit can provide information related to events that the user has attended in the past. In this way, the information provision unit can provide the most relevant information by referring to the user's past interest history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input the user's interest history data into a generating AI and have the generating AI perform the task of providing the most relevant information.

[0047] The information provision unit can customize the content of information based on the user's current areas of interest when providing information. For example, the information provision unit can provide the latest information on genres that the user is currently interested in. It can also provide the latest news on artists or actors that the user is currently following. Furthermore, the information provision unit can provide information related to communities or groups that the user is currently participating in. This allows for more appropriate information to be provided by customizing the content of information based on the user's current areas of interest. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the information content.

[0048] The information provision unit can provide optimal information by considering the user's geographical location when providing information. For example, if the user is in a specific region, the information provision unit can provide the latest information related to that region. Furthermore, if the user is traveling, the information provision unit can provide the latest information related to their travel destination. Additionally, if the user is at home, the information provision unit can provide the latest information about their home area. This allows for the provision of optimal information by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal information.

[0049] The information provision unit can analyze the user's social media activity and customize the content of the information provided. For example, the information provision unit can provide the latest news related to the information the user has shared on social media. It can also provide the latest information about artists and actors the user follows on social media. Furthermore, it can provide the latest information related to groups and communities the user participates in on social media. In this way, the content of the information can be customized by analyzing the user's social media activity. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the information content.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also analyze users' learning history and provide educational content. For example, if a user is interested in a particular historical period, it can recommend movies and documentaries related to that period. Similarly, if a user is interested in a particular field of science, it can recommend movies and documentaries related to that field. Furthermore, if a user is learning a particular language, it can recommend movies and music produced in that language. This allows for learning support by providing educational content based on the user's learning history.

[0052] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also analyze users' social media activity and provide relevant entertainment. For example, they can recommend content related to movies and music that users have shared on social media. They can also recommend content related to artists and actors that users follow on social media. Furthermore, they can recommend content related to groups and communities that users participate in on social media. This allows for a more relevant experience by providing entertainment based on users' social media activity.

[0053] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also provide relevant entertainment considering the user's geographical location. For example, if a user is traveling, it can recommend movies and music related to that region. It can also recommend movies and music related to a specific city if the user lives in that city. Furthermore, if a user is interested in a particular country, it can recommend movies and music related to that country. This allows for a more relevant experience by providing entertainment based on the user's geographical location.

[0054] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also provide relevant entertainment by referencing the user's past event attendance history. For example, they can recommend new songs by artists related to concerts the user has attended in the past. They can also recommend sequels or related works to movies the user has watched in the past. Furthermore, they can recommend movies and music related to the genre of events the user has attended in the past. This allows for a more relevant experience by providing entertainment based on the user's past event attendance history.

[0055] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also adjust the timing of entertainment delivery based on the user's current schedule. For example, they can recommend relevant movies and music based on appointments registered in the user's calendar. They can also recommend movies and music during times when the user has free time in their schedule. Furthermore, they can select the optimal timing for entertainment delivery based on the user's schedule. This allows for a more appropriate experience by adjusting the timing of entertainment delivery based on the user's current schedule.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The learning unit collects and analyzes the user's viewing or listening history. For example, it collects data on movies the user has watched and music the user has listened to, and the AI ​​analyzes this data. This allows the AI ​​to understand the user's preferences. Step 2: The recommendation unit recommends movies and music based on the data obtained by the learning unit. For example, if the user likes action movies, the AI ​​will recommend the latest action movies. Also, if the user likes the music of a particular artist, the AI ​​will recommend that artist's new songs. Step 3: The notification section provides notifications about entertainment-related schedules and events based on the user's interests. For example, it notifies the user of release dates for movies they are interested in or concert dates. This ensures that the user stays informed about the latest entertainment news. Step 4: The information department provides the latest news and background stories of celebrities and artists based on the user's interests. For example, it provides the latest news and interview articles about actors and artists that the user is interested in. This allows users to stay up-to-date on information about their favorite celebrities and artists.

[0058] (Example of form 2) An AI assistant for the entertainment industry according to an embodiment of the present invention is a system that recommends movies and music based on the user's preferences, notifies the user of entertainment-related schedules and events, and provides the latest information and background stories of celebrities and artists. The AI ​​assistant for the entertainment industry learns the user's movie and music preferences and recommends movies and music based on those learned preferences. Furthermore, it notifies the user of entertainment-related schedules and events and provides the latest information and background stories of celebrities and artists. For example, the AI ​​assistant for the entertainment industry collects data on movies the user has watched and music the user has listened to, and the AI ​​analyzes this data. For example, by collecting information on the genre of movies the user has watched and the artists the AI ​​has listened to, the AI ​​can understand the user's preferences. Next, it recommends movies and music to the user based on the learned preferences. For example, if the user likes action movies, the AI ​​will recommend the latest action movies. Also, if the user likes the music of a particular artist, the AI ​​will recommend the artist's new songs. Furthermore, it provides notifications of entertainment-related schedules and events. For example, it will notify the user of the release date of a movie they are interested in or the date of a concert. This allows the user to stay informed about the latest entertainment information. Finally, it provides the latest information and background stories of celebrities and artists. For example, it can provide the latest news and interview articles about actors and artists that the user is interested in. This allows users to stay up-to-date on their favorite celebrities and artists. This system enables users to enjoy movies and music that suit their tastes and stay informed about the latest entertainment news. Furthermore, knowing the latest information and background stories of celebrities and artists can further enhance their enjoyment of entertainment. As a result, AI assistants in the entertainment industry can recommend movies and music based on user preferences, notify users of entertainment-related schedules and events, and provide the latest information and background stories of celebrities and artists.

[0059] The AI ​​assistant for the entertainment industry according to this embodiment comprises a learning unit, a recommendation unit, a notification unit, and an information provision unit. The learning unit collects and analyzes the user's viewing or listening history. For example, the learning unit collects data on movies the user has watched and music the user has listened to, and the AI ​​analyzes this data. For example, the learning unit collects information on the genres and artists of movies the user has watched, and the AI ​​analyzes this data to understand the user's preferences. The recommendation unit recommends movies and music based on the data obtained by the learning unit. For example, if the user likes action movies, the AI ​​will recommend the latest action movies. Also, if the user likes the music of a particular artist, the AI ​​will recommend the artist's new songs. The notification unit provides notifications of entertainment-related schedules and events based on the user's interests. For example, the notification unit will notify the user of the release date of a movie they are interested in or the date of a concert. This allows the user to stay informed about the latest entertainment information. The information provision unit provides the latest information and background stories of celebrities and artists based on the user's interests. The information provision section provides, for example, the latest news and interview articles about actors and artists that the user is interested in. This allows the user to always be informed about their favorite celebrities and artists. As a result, the AI ​​assistant for the entertainment industry according to this embodiment can recommend movies and music based on the user's preferences, notify them of entertainment-related schedules and events, and provide the latest information and background stories about celebrities and artists.

[0060] The learning unit collects and analyzes users' viewing or listening history. Specifically, it collects data on movies watched and music listened to by users, and the AI ​​analyzes this data. For example, by collecting information on the genres and artists of movies watched by users and having the AI ​​analyze this information, it is possible to understand the user's preferences. The learning unit utilizes natural language processing (NLP) and machine learning algorithms to analyze users' viewing and listening history in detail. For example, NLP can be used to analyze movie reviews and song lyrics to extract the user's emotions and preferences. Furthermore, machine learning algorithms can be used to analyze users' viewing and listening patterns and predict future viewing and listening trends. In addition, the learning unit can perform more accurate analysis by combining user demographic information and past behavioral data. For example, by considering information such as the user's age, gender, and place of residence, it can identify popular movies and music in specific age groups or regions and provide content that matches the user's preferences. As a result, the learning unit can analyze users' viewing and listening history in detail and accurately understand their preferences and interests.

[0061] The recommendation unit recommends movies and music based on data obtained by the learning unit. Specifically, if a user likes action movies, the AI ​​will recommend the latest action movies. Similarly, if a user likes the music of a particular artist, the AI ​​will recommend that artist's new songs. The recommendation unit uses collaborative filtering and content-based recommendation algorithms to provide users with the most suitable content. Collaborative filtering is a method that recommends movies and music watched or listened to by users with similar preferences, based on the viewing and listening history of other users. On the other hand, content-based recommendation algorithms analyze the characteristics of movies and music that a user has watched or listened to in the past and recommend content with similar characteristics. Furthermore, the recommendation unit can monitor user behavior in real time and dynamically update its recommendations. For example, if a user watches a new movie, the viewing history is immediately sent to the learning unit, and the recommendation algorithm is updated to recommend content based on the user's latest preferences. The recommendation unit can also collect user feedback and continuously improve the accuracy of its recommendation algorithms. As a result, the recommendation unit can recommend movies and music that match the user's preferences with high accuracy, improving the user's entertainment experience.

[0062] The notification unit provides notifications about entertainment-related schedules and events based on the user's interests. Specifically, it notifies users of release dates for movies they are interested in and concert dates for concerts they are interested in. The notification unit can integrate with the user's calendar and scheduling apps to automatically add important events. For example, as the release date of a movie the user is interested in approaches, the notification unit will add the information to the user's calendar and set a reminder. The notification unit can also use the user's location information to provide information about nearby events and concerts. For example, if the user is a fan of a particular artist, it will notify them when that artist's concert is held nearby. Furthermore, the notification unit can predict and notify users of new events and schedules that they might be interested in based on their past behavioral data. As a result, the notification unit ensures that users stay informed about the latest entertainment information and can enhance their entertainment experience.

[0063] The Information Department provides users with the latest information and background stories about celebrities and artists based on their interests. Specifically, it provides the latest news and interview articles about actors and artists that users are interested in. The Information Department collects the latest information from news sites, social media, and official websites on the internet and provides it to users. For example, if a user is interested in a particular actor, it will provide information about that actor's latest movie and interview articles. The Information Department can also create a customized news feed based on user interests, providing information that users are interested in in one place. Furthermore, the Information Department can continuously improve the accuracy and relevance of the information it provides based on user feedback. For example, if a user gives a high rating to a particular news article, it will prioritize providing articles with similar content. The Information Department can also provide relevant background stories and past interview articles based on user interests. In this way, the Information Department can keep users up-to-date on information about their favorite celebrities and artists, further enriching their entertainment experience.

[0064] The learning unit can estimate the user's emotions and adjust the timing of collecting viewing and listening history based on the estimated emotions. For example, if the user is relaxed, the learning unit can collect viewing and listening history in real time and analyze it immediately. If the user is stressed, the learning unit can delay the collection timing and collect data when the user is calm. Furthermore, if the user is busy, the learning unit can set the collection timing to nighttime or weekends to reduce the user's burden. This allows for more appropriate data collection by adjusting the timing of viewing and listening history collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The learning unit can analyze a user's past viewing and listening history and select the optimal data collection method. For example, the learning unit can identify genres that a user frequently watches and prioritize the collection of data related to those genres. It can also collect data during specific time periods if a user tends to watch during those times. Furthermore, if a user frequently watches on a particular device, the learning unit can prioritize data collection from that device. This allows the optimal data collection method to be selected by analyzing the user's past viewing and listening history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For instance, the learning unit can input the user's viewing and listening history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The learning unit can filter viewing and listening history data based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the learning unit will prioritize collecting viewing and listening history related to that hobby. The learning unit can also collect data related to an event after the user participates in that event. Furthermore, if the user is in a specific life stage (e.g., marriage, childbirth), the learning unit can collect data related to that life stage. This allows for the collection of more relevant data by filtering the data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the user's lifestyle and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0067] The learning unit can estimate the user's emotions and determine the priority of the history to collect based on the estimated user emotions. For example, if the user is excited, the learning unit will prioritize collecting history of action movies or upbeat music. It can also prioritize collecting history of relaxing movies and music if the user is relaxed. Furthermore, if the user is sad, it can prioritize collecting history of emotional movies and music. This allows for more appropriate data collection by prioritizing the history to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The learning unit can prioritize the collection of highly relevant viewing and listening history by considering the user's geographical location. For example, if the user is traveling, the learning unit can prioritize the collection of movies and music history related to that region. It can also prioritize the collection of movies and music history related to a specific city if the user lives in that city. Furthermore, if the user is interested in a particular country, the learning unit can prioritize the collection of movies and music history related to that country. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant history.

[0069] The learning unit can analyze a user's social media activity and collect relevant history when collecting viewing and listening history. For example, the learning unit can prioritize collecting the history of movies and music that the user has shared on social media. It can also collect history related to artists and actors that the user follows on social media. Furthermore, the learning unit can collect history related to groups and communities that the user participates in on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant history.

[0070] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system might recommend movies or music in a calm tone. If the user is excited, it might recommend movies or music in an energetic tone. Furthermore, if the user is sad, it might recommend movies or music in a comforting tone. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The recommendation system can adjust the level of detail in recommendations based on the importance of the movies and music. For example, it can provide detailed information about movies and music that the user is particularly interested in. It can also provide concise information about movies and music that the user is not very interested in. Furthermore, if the user has started to become interested in a new genre, the recommendation system can provide detailed information about that genre. In this way, by adjusting the level of detail in recommendations based on the importance of the movies and music, the system can provide the most suitable information for the user. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input movie and music importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recommendations.

[0072] The recommendation system can apply different recommendation algorithms depending on the category of film or music. For example, for action films, it might apply an algorithm that emphasizes thrills and excitement. For romance films, it might apply an algorithm that emphasizes emotions and relationships. Furthermore, for classical music, it might apply an algorithm that emphasizes historical background and composer information. By applying different recommendation algorithms depending on the category of film or music, more appropriate recommendations can be made. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input film and music category data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0073] The recommendation section can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation section will provide short, concise recommendations. If the user is relaxed, the recommendation section can provide longer recommendations with detailed explanations. Furthermore, if the user is excited, the recommendation section can provide recommendations with visually stimulating effects. By adjusting the length of recommendations according to the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The recommendation system can prioritize recommendations based on the release dates of movies and music. For example, it might prioritize recommending the latest movies and music. It can also prioritize recommending sequels or related works to movies and music that the user has previously watched or listened to. Furthermore, it can prioritize recommending movies and music that the user is interested in at a particular time. This allows the system to provide up-to-date information by prioritizing recommendations based on the release dates of movies and music. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system could input movie and music release date data into a generating AI and have the generating AI determine the recommendation priorities.

[0075] The recommendation system can adjust the order of recommendations based on the relevance of movies and music. For example, it can prioritize recommending works related to movies and music the user has watched. It can also prioritize recommending works related to genres the user is interested in. Furthermore, it can prioritize recommending works related to artists and actors the user follows. By adjusting the order of recommendations based on the relevance of movies and music, the system can provide the user with the most relevant information. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input movie and music relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0076] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit can send a notification immediately. If the user is stressed, the notification unit can also delay sending the notification. Furthermore, if the user is busy, the notification unit can send notifications at night or on weekends. This allows for more appropriate notifications by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The notification unit can select the optimal notification method by referring to the user's past event participation history when sending a notification. For example, the notification unit can send notifications for relevant events based on the types of events the user has previously participated in. The notification unit can also select the optimal notification timing based on the time of day of events the user has previously participated in. Furthermore, the notification unit can send notifications for relevant events based on the location of events the user has previously participated in. This allows the notification unit to select the optimal notification method by referring to the user's past event participation history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's event participation history data into a generating AI and have the generating AI select the optimal notification method.

[0078] The notification unit can customize the content of notifications based on the user's current schedule. For example, the notification unit can send notifications for relevant events based on appointments registered in the user's calendar. It can also send event notifications during times when the user has free time in their schedule. Furthermore, the notification unit can select the optimal notification timing based on the user's schedule. This allows for more appropriate notifications by customizing the content based on the user's current schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's schedule data into a generating AI and have the generating AI customize the content of the notifications.

[0079] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is excited, the notification unit will prioritize sending important notifications. It can also send normal notifications if the user is relaxed. Furthermore, if the user is sad, the notification unit can prioritize sending comforting notifications. This allows for more appropriate notifications by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The notification unit can select the optimal notification method by considering the user's geographical location information when sending a notification. For example, if the user is in a specific region, the notification unit can send notifications about events related to that region. It can also send notifications about events related to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the notification unit can send notifications about events around their home. This allows the system to select the optimal notification method by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0081] The notification unit can analyze the user's social media activity and customize the content of notifications when sending them. For example, the notification unit can send notifications related to events the user has shared on social media. It can also send notifications related to artists or actors the user follows on social media. Furthermore, it can send notifications related to groups or communities the user participates in on social media. This allows for the customization of notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI customize the content of the notifications.

[0082] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information delivery unit can deliver information in a calm tone. If the user is excited, the information delivery unit can deliver information in an energetic tone. Furthermore, if the user is sad, the information delivery unit can deliver information in a comforting tone. By adjusting the method of information delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The information provision unit can provide the most relevant information by referring to the user's past interest history when providing information. For example, the information provision unit can provide the latest information on actors or artists that the user has been interested in in the past. It can also provide background stories related to movies or music that the user has watched or listened to in the past. Furthermore, the information provision unit can provide information related to events that the user has attended in the past. In this way, the information provision unit can provide the most relevant information by referring to the user's past interest history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input the user's interest history data into a generating AI and have the generating AI perform the task of providing the most relevant information.

[0084] The information provision unit can customize the content of information based on the user's current areas of interest when providing information. For example, the information provision unit can provide the latest information on genres that the user is currently interested in. It can also provide the latest news on artists or actors that the user is currently following. Furthermore, the information provision unit can provide information related to communities or groups that the user is currently participating in. This allows for more appropriate information to be provided by customizing the content of information based on the user's current areas of interest. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the information content.

[0085] The information provision unit can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is excited, the information provision unit will prioritize providing important information. It can also provide normal information if the user is relaxed. Furthermore, if the user is sad, the information provision unit can prioritize providing comforting information. This allows for more appropriate information provision by prioritizing information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, or not. For example, the information provision unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The information provision unit can provide optimal information by considering the user's geographical location when providing information. For example, if the user is in a specific region, the information provision unit can provide the latest information related to that region. Furthermore, if the user is traveling, the information provision unit can provide the latest information related to their travel destination. Additionally, if the user is at home, the information provision unit can provide the latest information about their home area. This allows for the provision of optimal information by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal information.

[0087] The information provision unit can analyze the user's social media activity and customize the content of the information provided. For example, the information provision unit can provide the latest news related to the information the user has shared on social media. It can also provide the latest information about artists and actors the user follows on social media. Furthermore, it can provide the latest information related to groups and communities the user participates in on social media. In this way, the content of the information can be customized by analyzing the user's social media activity. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the information content.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also monitor the user's health and provide appropriate entertainment. For example, if a user is tired, it can recommend relaxing movies or music. If the user is energetic, it can recommend action movies or upbeat music. Furthermore, if the user is stressed, it can provide content that helps reduce stress. This allows for a more personalized experience by providing entertainment tailored to the user's health state.

[0090] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also analyze users' learning history and provide educational content. For example, if a user is interested in a particular historical period, it can recommend movies and documentaries related to that period. Similarly, if a user is interested in a particular field of science, it can recommend movies and documentaries related to that field. Furthermore, if a user is learning a particular language, it can recommend movies and music produced in that language. This allows for learning support by providing educational content based on the user's learning history.

[0091] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also estimate the user's emotions and change the entertainment genre based on those emotions. For example, if the user is sad, it can recommend a comedy movie or upbeat music. If the user is excited, it can recommend a thriller movie or energetic music. Furthermore, if the user is relaxed, it can recommend relaxing movies and music. This allows for a more appropriate experience by providing entertainment that matches the user's emotions.

[0092] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also analyze users' social media activity and provide relevant entertainment. For example, they can recommend content related to movies and music that users have shared on social media. They can also recommend content related to artists and actors that users follow on social media. Furthermore, they can recommend content related to groups and communities that users participate in on social media. This allows for a more relevant experience by providing entertainment based on users' social media activity.

[0093] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also provide relevant entertainment considering the user's geographical location. For example, if a user is traveling, it can recommend movies and music related to that region. It can also recommend movies and music related to a specific city if the user lives in that city. Furthermore, if a user is interested in a particular country, it can recommend movies and music related to that country. This allows for a more relevant experience by providing entertainment based on the user's geographical location.

[0094] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also estimate the user's emotions and adjust how entertainment is delivered based on those emotions. For example, if the user is relaxed, it can recommend movies and music in a calm tone. If the user is excited, it can recommend movies and music in an energetic tone. Furthermore, if the user is sad, it can recommend movies and music in a comforting tone. This allows for a more appropriate experience by adjusting how entertainment is delivered according to the user's emotions.

[0095] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also provide relevant entertainment by referencing the user's past event attendance history. For example, they can recommend new songs by artists related to concerts the user has attended in the past. They can also recommend sequels or related works to movies the user has watched in the past. Furthermore, they can recommend movies and music related to the genre of events the user has attended in the past. This allows for a more relevant experience by providing entertainment based on the user's past event attendance history.

[0096] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also estimate the user's emotions and prioritize entertainment based on those emotions. For example, if the user is excited, it can prioritize recommending action movies and upbeat music. If the user is relaxed, it can prioritize recommending relaxing movies and music. Furthermore, if the user is sad, it can prioritize recommending emotional movies and music. By prioritizing entertainment according to the user's emotions, it can provide a more appropriate experience.

[0097] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also adjust the timing of entertainment delivery based on the user's current schedule. For example, they can recommend relevant movies and music based on appointments registered in the user's calendar. They can also recommend movies and music during times when the user has free time in their schedule. Furthermore, they can select the optimal timing for entertainment delivery based on the user's schedule. This allows for a more appropriate experience by adjusting the timing of entertainment delivery based on the user's current schedule.

[0098] AI assistants in the entertainment industry can not only recommend movies and music based on user preferences, but also estimate the user's emotions and adjust the level of detail in the entertainment based on those emotions. For example, if the user is in a hurry, it can recommend short, to-the-point movies or music. If the user is relaxed, it can recommend movies or music with detailed descriptions. Furthermore, if the user is excited, it can recommend movies or music with visually stimulating effects. This allows for a more appropriate experience by adjusting the level of detail in the entertainment according to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The learning unit collects and analyzes the user's viewing or listening history. For example, it collects data on movies the user has watched and music the user has listened to, and the AI ​​analyzes this data. This allows the AI ​​to understand the user's preferences. Step 2: The recommendation unit recommends movies and music based on the data obtained by the learning unit. For example, if the user likes action movies, the AI ​​will recommend the latest action movies. Also, if the user likes the music of a particular artist, the AI ​​will recommend that artist's new songs. Step 3: The notification section provides notifications about entertainment-related schedules and events based on the user's interests. For example, it notifies the user of release dates for movies they are interested in or concert dates. This ensures that the user stays informed about the latest entertainment news. Step 4: The information department provides the latest news and background stories of celebrities and artists based on the user's interests. For example, it provides the latest news and interview articles about actors and artists that the user is interested in. This allows users to stay up-to-date on information about their favorite celebrities and artists.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] For example, the learning unit collects the user's viewing and listening history using the camera 42 and microphone 38B of the smart device 14, and this history is analyzed by the specific processing unit 290 of the data processing unit 12. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends movies and music based on the data obtained from the learning unit. For example, the notification unit is implemented by the control unit 46A of the smart device 14 and provides notifications of entertainment-related schedules and events based on the user's interests. For example, the information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the latest information and background stories of celebrities and artists based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] For example, the learning unit collects the user's viewing and listening history using the camera 42 and microphone 238 of the smart glasses 214, and this history is analyzed by the specific processing unit 290 of the data processing unit 12. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends movies and music based on the data obtained from the learning unit. For example, the notification unit is implemented by the control unit 46A of the smart glasses 214 and provides notifications of entertainment-related schedules and events based on the user's interests. For example, the information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the latest information and background stories of celebrities and artists based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] For example, the learning unit collects the user's viewing and listening history using the camera 42 and microphone 238 of the headset terminal 314, and this history is analyzed by the specific processing unit 290 of the data processing unit 12. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends movies and music based on the data obtained from the learning unit. For example, the notification unit is implemented by the control unit 46A of the headset terminal 314 and provides notifications of entertainment-related schedules and events based on the user's interests. For example, the information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the latest information and background stories of celebrities and artists based on the user's interests. The correspondence between each unit and the device and control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] For example, the learning unit collects the user's viewing and listening history using the camera 42 and microphone 238 of the robot 414, and this history is analyzed by the specific processing unit 290 of the data processing unit 12. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends movies and music based on the data obtained from the learning unit. For example, the notification unit is implemented by the control unit 46A of the robot 414 and notifies the user of entertainment-related schedules and events based on the user's interests. For example, the information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the latest information and background stories of celebrities and artists based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A learning unit that collects and analyzes the user's viewing or listening history, Based on the data obtained by the aforementioned learning department, there is a recommendation department that recommends movies and music, A notification unit that provides notifications about entertainment-related schedules and events based on the user's interests, It includes an information provision department that provides the latest information and background stories of celebrities and artists based on user interests. A system characterized by the following features. (Note 2) The aforementioned learning unit, The system estimates the user's emotions and adjusts the timing of collecting viewing and listening history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Analyze the user's past viewing and listening history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, When collecting viewing and listening history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It estimates the user's emotions and determines the priority of the history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, When collecting viewing and listening history, the system prioritizes collecting relevant history based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, When collecting viewing and listening history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the movies and music. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the category of movies or music. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, When making recommendations, we prioritize them based on the release dates of the movies and music. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the movies and music. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, When sending a notification, the system will refer to the user's past event participation history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to customize the content of the notifications. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, When providing information, we refer to the user's past interest history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, When providing information, customize the content of the information based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information provision unit, When providing information, the content of the information is customized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that collects and analyzes the user's viewing or listening history, Based on the data obtained by the aforementioned learning department, there is a recommendation department that recommends movies and music, A notification unit that provides notifications about entertainment-related schedules and events based on the user's interests, It includes an information provision department that provides the latest information and background stories of celebrities and artists based on user interests. A system characterized by the following features.

2. The aforementioned learning unit, The system estimates the user's emotions and adjusts the timing of collecting viewing and listening history based on those estimated emotions. The system according to feature 1.

3. The aforementioned learning unit, Analyze the user's past viewing and listening history to select the optimal data collection method. The system according to feature 1.

4. The aforementioned learning unit, When collecting viewing and listening history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned learning unit, It estimates the user's emotions and determines the priority of the history to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned learning unit, When collecting viewing and listening history, the system prioritizes collecting relevant history based on the user's geographical location. The system according to feature 1.

7. The aforementioned learning unit, When collecting viewing and listening history, the system analyzes the user's social media activity and collects relevant history. The system according to feature 1.

8. The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system according to feature 1.

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