system

Abstract

The system according to this embodiment aims to recommend and notify users of personalized content based on their viewing history. [Solution] The system according to the embodiment comprises a collection unit, a recommendation unit, an identification unit, and a notification unit. The collection unit collects viewing history. The recommendation unit makes personalized recommendations based on the viewing history collected by the collection unit. The identification unit identifies the content recommended by the recommendation unit. The notification unit notifies the user of the content identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there is a problem that personalized content based on the user's viewing history has not been sufficiently recommended and notified appropriately.

[0005] The system according to the embodiment aims to recommend and notify personalized content based on the user's viewing history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a recommendation unit, an identification unit, and a notification unit. The collection unit collects viewing history. The recommendation unit makes personalized recommendations based on the viewing history collected by the collection unit. The identification unit identifies the content recommended by the recommendation unit. The notification unit notifies the user of the content identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend and notify users of personalized content based on their viewing history. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 three or more matters are connected and expressed by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The StreamGuide AI system according to an embodiment of the present invention is a system that notifies users in real time of video content such as sports, dramas, and anime based on their preferences, and enables them to reserve viewing. By registering categories and keywords of interest, the StreamGuide AI system allows users to receive real-time notifications when relevant videos are streamed. It also provides highlight videos and short movies, and centrally manages all video content. Using AI, it learns from the user's viewing history and preferences to provide personalized recommendations. Furthermore, the AI ​​automatically scans video platforms and identifies and notifies users of new content based on their registered interests. This allows users to catch their favorite content without missing anything, significantly improving their satisfaction with video viewing. It also enables time savings and efficient information gathering. Thus, the StreamGuide AI system can improve the user's viewing experience and enable efficient information gathering.

[0029] The StreamGuide AI system according to this embodiment comprises a collection unit, a recommendation unit, an identification unit, and a notification unit. The collection unit collects the user's viewing history. The viewing history includes, but is not limited to, the title of the content viewed, the viewing time, and the viewing frequency. The collection unit also collects metadata of the content viewed by the user and stores it as viewing history. The collection unit can also update the user's viewing history in real time. For example, the collection unit instantly adds newly viewed content to the viewing history. Furthermore, the collection unit can periodically back up the user's viewing history. For example, the collection unit stores the viewing history in cloud storage to ensure data security. The recommendation unit makes personalized recommendations based on the viewing history collected by the collection unit. The recommendation unit analyzes, for example, the user's past viewing history, interests, and behavioral patterns to recommend the most suitable content. For example, the recommendation unit recommends content similar to content the user has viewed in the past. The recommendation unit can also discover and recommend new content based on the user's interests. For example, the recommendation unit recommends new genres of content that the user might be interested in. Furthermore, the recommendation unit can learn the user's behavior patterns and make recommendations at the optimal time. For example, the recommendation unit recommends content according to the time of day the user is watching. The identification unit identifies the content recommended by the recommendation unit. The identification unit identifies content based on, for example, the content's metadata, tag information, and relevance to viewing history. For example, the identification unit identifies content based on its title and genre. The identification unit can also evaluate the relevance of content to viewing history to improve the accuracy of identification. For example, the identification unit prioritizes identifying content that is highly relevant to content the user has watched in the past. Furthermore, the identification unit can utilize the content's tag information to improve the accuracy of identification. For example, the identification unit identifies content based on tags assigned to it. The notification unit notifies the user of the content identified by the identification unit. The notification unit adjusts the timing of the notification, the notification method (email, push notification, etc.), and the details of the notification content before sending it.For example, the notification unit sends notifications according to the time the user is available to watch. The notification unit can also select the most suitable notification method for the user's device. For example, the notification unit sends push notifications to a smartphone. Furthermore, the notification unit can customize the notification content to provide the most relevant information to the user. For example, the notification unit adjusts the notification content based on the user's interests. As a result, the StreamGuide AI system according to this embodiment can improve the user's viewing experience.

[0030] The data collection unit collects users' viewing history. Viewing history includes, but is not limited to, the title of the content viewed, viewing time, and viewing frequency. The data collection unit collects metadata of the content viewed by users and stores it as viewing history. Specifically, the data collection unit collects detailed metadata such as the title, genre, viewing start time, viewing end time, and viewing device of the content viewed by users. This allows for a detailed understanding of the user's viewing behavior. The data collection unit can also update the user's viewing history in real time. For example, the data collection unit instantly adds newly viewed content to the viewing history. This ensures that the user always has the most up-to-date viewing history. Furthermore, the data collection unit can periodically back up the user's viewing history. For example, the data collection unit stores the viewing history in cloud storage to ensure data security. Cloud storage minimizes the risk of data loss by providing data redundancy. In addition, the data collection unit can encrypt or anonymize viewing history to protect user privacy. This reduces the risk of the user's personal information being leaked to third parties. Through these functions, the data collection unit can efficiently and securely collect users' viewing history, thereby improving the overall performance and reliability of the system.

[0031] The recommendation department provides personalized recommendations based on viewing history collected by the data collection department. For example, the recommendation department analyzes a user's past viewing history, interests, and behavioral patterns to recommend the most suitable content. Specifically, it uses machine learning algorithms to analyze the user's viewing history and identify content that the user tends to prefer. For example, it recommends content similar to what the user has previously viewed. The recommendation department can also discover and recommend new content based on the user's interests. For example, it recommends content in new genres that the user might be interested in. Furthermore, the recommendation department can learn the user's behavioral patterns and make recommendations at the optimal time. For example, it recommends content based on the time of day the user is likely to be viewing it. This ensures that content is delivered at the time when the user is most likely to view it. Through these functions, the recommendation department can provide highly accurate and personalized content recommendations to users, improving the viewing experience. Additionally, the recommendation department can collect user feedback to continuously improve the accuracy of its recommendation algorithms. For example, it collects whether the user viewed the recommended content and their post-viewing evaluations, using this data as learning data for the algorithm. This allows the recommendation system to continue providing content that is more tailored to the user's preferences.

[0032] The identification unit identifies content recommended by the recommendation unit. The identification unit identifies content based on factors such as metadata, tag information, and relevance to viewing history. Specifically, the identification unit identifies content based on its title and genre. For example, it analyzes keywords and genre tags included in the content title to determine if it matches the user's interests. The identification unit can also evaluate the relevance of content to viewing history to improve identification accuracy. For example, it prioritizes identifying content that is highly relevant to content the user has previously viewed. This allows for efficient identification of content that the user is likely to be interested in. Furthermore, the identification unit can utilize content tag information to improve identification accuracy. For example, it identifies content based on tags assigned to it. Tag information indicates the content and characteristics, making it effective in improving identification accuracy. Through these functions, the identification unit can accurately identify recommended content and provide the user with the most suitable content. In addition, the identification unit can continuously evaluate the identification results and build a feedback loop to improve the accuracy of the identification algorithm. This allows the identification unit to achieve highly accurate content identification based on the latest information at all times, thereby improving the user's viewing experience.

[0033] The notification unit notifies users of content identified by the identification unit. The notification unit adjusts the timing of notifications, notification methods (email, push notifications, etc.), and details of the notification content. Specifically, the notification unit sends notifications at times when the user is available to view the content. For example, it might notify users of content when they are relaxed after work. The notification unit can also select the most suitable notification method for the user's device. For example, it might send push notifications to smartphones. Push notifications are effective in preventing users from missing viewing opportunities because they can be checked immediately. Furthermore, the notification unit can customize notification content to provide the most relevant information to the user. For example, it might adjust notification content based on the user's interests. This allows for effective notification of content that is likely to interest the user. Through these functions, the notification unit can improve the viewing experience by notifying users of the right content at the right time. Additionally, the notification unit can collect user feedback and continuously improve the accuracy of its notification algorithm. For example, it can analyze user behavior after receiving a notification (whether they viewed it, their evaluation, etc.) to evaluate the effectiveness of the notification. This allows the notification unit to provide more effective notifications to users and improve the viewing experience.

[0034] The service provider can provide highlight videos and short movies. For example, the service provider can provide highlight videos of sports matches. For example, the service provider can edit the important scenes of a match and create a highlight video that can be viewed in a short amount of time. The service provider can also provide short movies of drama episodes. For example, the service provider can extract the important scenes of an episode and create a short movie that can be viewed in a short amount of time. Furthermore, the service provider can provide highlight videos of anime. For example, the service provider can edit the important scenes of an anime and create a highlight video that can be viewed in a short amount of time. By providing users with highlight videos and short movies, the viewing experience can be diversified. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generation AI perform the editing of highlight videos and short movies.

[0035] The management unit can manage viewing reservations. The management unit can, for example, provide an interface for users to make viewing reservations. For example, the management unit can allow users to select the content they want to watch and make a viewing reservation. The management unit can also provide notifications for viewing reservations. For example, the management unit can send a notification to the user when the time for a viewing reservation is approaching. Furthermore, the management unit can also cancel or change viewing reservations. For example, the management unit can allow users to cancel viewing reservations or change viewing times. This allows users to efficiently plan their viewing by managing their viewing reservations. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can have a generation AI perform the management of viewing reservations.

[0036] The data collection unit can analyze the user's past viewing history and select the optimal data collection method. For example, the data collection unit can analyze patterns in content the user has previously viewed and determine the optimal timing for collection. For example, the data collection unit can identify viewing tendencies during specific time periods from the user's viewing history and concentrate data collection during those times. For example, the data collection unit can prioritize collecting content that is viewed frequently based on the user's viewing history. In this way, the optimal data collection method can be selected by analyzing the user's past viewing history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's viewing history data into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter viewing history based on the user's current interests. For example, the data collection unit can prioritize collecting content in genres that the user is currently interested in. For example, the data collection unit can collect relevant viewing history based on keywords the user has recently searched for. For example, the data collection unit can analyze trends in online communities the user participates in and collect relevant viewing history. This allows for the collection of highly relevant data by filtering viewing history based on the user's current interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user interest data into a generating AI and have the generating AI perform the filtering.

[0038] The collection unit can prioritize the collection of highly relevant viewing history by considering the user's geographical location information when collecting viewing history. For example, if the user is in a specific region, the collection unit will prioritize the collection of viewing history of content related to that region. For example, if the user is traveling, the collection unit will prioritize the collection of viewing history of content related to the travel destination. For example, if the user is at home, the collection unit will prioritize the collection of viewing history related to events and news around the user's home. In this way, by considering the user's geographical location information, the collection unit can prioritize the collection of highly relevant viewing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0039] The data collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the data collection unit can collect relevant viewing history based on content shared by the user on social media. For example, the data collection unit can analyze the content posted by accounts that the user follows and collect relevant viewing history. For example, the data collection unit can analyze the trends of groups and communities that the user participates in and collect relevant viewing history. In this way, relevant viewing history can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant history.

[0040] The recommendation system can adjust the level of detail of recommendations based on the importance of the content. For example, for important content, the recommendation system will provide recommendations with detailed descriptions. For general content, the recommendation system will provide recommendations with concise descriptions. For content that the user is particularly interested in, the recommendation system will provide recommendations with detailed information. By adjusting the level of detail of recommendations based on the importance of the content, 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 using AI. For example, the recommendation system can input content importance data into a generating AI and have the generating AI perform the adjustment of recommendation detail.

[0041] The recommendation system can apply different recommendation algorithms depending on the content category during the recommendation process. For example, for sports content, the recommendation system might include match highlights and player interviews. For drama content, it might include episode summaries and cast information. For anime content, it might include character introductions and related merchandise information. By applying different recommendation algorithms depending on the content category, more accurate recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input content category data into a generating AI and have the generating AI apply different recommendation algorithms.

[0042] The recommendation system can determine recommendation priorities based on the content's release date. For example, it might prioritize newly released content. For example, it might prioritize sequels to content the user has previously watched. For example, it might prioritize content related to a specific event or season. By prioritizing recommendations based on the content's release date, it becomes possible to provide recommendations at the optimal time for the user. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input content release date data into a generating AI and have the generating AI determine the recommendation priorities.

[0043] The recommendation system can adjust the order of recommendations based on the relevance of the content. For example, the recommendation system may prioritize recommending content that is highly relevant to content the user has previously viewed. For example, the recommendation system may prioritize recommending content in genres that the user is interested in. For example, the recommendation system may prioritize recommending content shared by accounts that the user follows. By adjusting the order of recommendations based on the relevance of the content, it becomes possible to recommend content in the order that is most optimal for the user. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system may input content relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0044] The identification unit can improve the accuracy of its identification by considering the interrelationships of content during the identification process. For example, when identifying episodes of the same series, the identification unit considers the relationship with preceding and succeeding episodes. For example, when identifying content of the same genre, the identification unit considers the relationship with other related content. For example, when identifying content featuring the same actors, the identification unit considers the relationship with other works featuring the same actors. This improves the accuracy of identification by considering the interrelationships of content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input interrelationship data of content into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0045] The identification unit can perform identification by considering the attribute information of the content provider. For example, when identifying content produced by a specific studio, the identification unit considers the studio's attribute information. For example, when identifying content produced by a specific director, the identification unit considers the director's attribute information. For example, when identifying content provided by a specific distribution platform, the identification unit considers the platform's attribute information. This improves the accuracy of identification by considering the attribute information of the content provider. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input provider attribute information data into a generating AI and have the generating AI perform the identification.

[0046] The identification unit can perform identification while considering the geographical distribution of the content. For example, when identifying content popular in a particular region, the identification unit considers the attribute information of that region. For example, when identifying content produced in a particular country, the identification unit considers the attribute information of that country. For example, when identifying content related to an event held in a particular city, the identification unit considers the attribute information of that city. This improves the accuracy of identification by considering the geographical distribution of the content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into a generating AI and have the generating AI perform the identification.

[0047] The identification unit can improve the accuracy of its identification by referring to relevant literature during the identification process. For example, the identification unit can improve the accuracy of its identification by referring to relevant review articles during content identification. For example, the identification unit can improve the accuracy of its identification by referring to relevant academic papers during content identification. For example, the identification unit can improve the accuracy of its identification by referring to relevant news articles during content identification. This improves the accuracy of identification by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0048] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has preferred to receive in the past. For example, the notification unit may avoid selecting notification methods that the user has ignored in the past. For example, the notification unit may select the optimal notification method for a specific time period from the user's past notification history. In this way, the optimal notification method can be selected by referring to the user's past notification 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 may input the user's notification history data into a generating AI and have the generating AI perform the selection of the optimal notification method.

[0049] The notification unit can adjust the timing of notifications based on the user's current situation. For example, if the user is working, the notification unit will send notifications sparingly. If the user is on a break, the notification unit will send notifications more actively. If the user is on the move, the notification unit will send notifications in real time. By adjusting the timing of notifications based on the user's current situation, it becomes possible to send notifications at a more appropriate time. 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 current situation data into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0050] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. For example, if the user is using a tablet, the notification unit will prioritize in-app notifications. For example, if the user is using a smartwatch, the notification unit will prioritize vibration notifications. In this way, the notification unit can select the optimal notification method by considering the user's device 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 device information into a generating AI and have the generating AI select the optimal notification method.

[0051] The notification unit can analyze the user's social media activity and customize the content of notifications when they are sent. For example, the notification unit can send relevant notifications based on content the user has shared on social media. For example, the notification unit can analyze the content of posts from accounts the user follows and send relevant notifications. For example, the notification unit can analyze the trends of groups and communities the user participates in and send relevant notifications. In this way, the content of notifications can be customized 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 without AI. For example, the notification unit can input the user's social media data into a generating AI and have the generating AI customize the content of the notifications.

[0052] The service provider can select the optimal service delivery method by referring to the user's past viewing history at the time of delivery. For example, the service provider can analyze patterns of content the user has previously viewed and select the optimal service delivery method. For example, the service provider can identify viewing tendencies at specific time periods from the user's viewing history and concentrate service delivery during those times. For example, the service provider can prioritize providing content that is viewed frequently based on the user's viewing history. In this way, the service provider can select the optimal service delivery method by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's viewing history data into a generating AI and have the generating AI select the optimal service delivery method.

[0053] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider will prioritize providing content related to that region. For example, if the user is traveling, the service provider will prioritize providing content related to the travel destination. For example, if the user is at home, the service provider will prioritize providing content related to events and news around the user's home. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0054] The management department can select the optimal management method by referring to the user's past viewing reservation history during management. For example, the management department can analyze patterns of content that users have previously reserved to view and select the optimal management method. For example, the management department can identify a tendency for users to reserve content during specific time slots from their viewing reservation history and concentrate management during those time slots. For example, the management department can prioritize managing content that is frequently viewed based on the user's viewing reservation history. In this way, the optimal management method can be selected by referring to the user's past viewing reservation history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's viewing reservation history data into a generating AI and have the generating AI select the optimal management method.

[0055] The management unit can select the optimal management method during management, taking into account the user's device information. For example, if the user is using a smartphone, the management unit provides a management method optimized for smartphones. For example, if the user is using a tablet, the management unit provides a management method optimized for tablets. For example, if the user is using a smartwatch, the management unit provides a management method optimized for smartwatches. This allows the management unit to select the optimal management method by taking into account the user's device information. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal management method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can collect not only the user's viewing history but also the user's voice commands. For example, the data collection unit can record the content that the user instructed to watch by voice and add it to the viewing history. For example, the data collection unit can collect keywords that the user searched for by voice and reflect the relevant content in the viewing history. For example, the data collection unit can collect rating information of content that the user rated by voice and integrate it into the viewing history. In this way, by collecting voice commands, the user's viewing history can be understood in more detail.

[0058] The service provider can offer interactive content based on the user's viewing history. For example, the service provider can offer an interactive drama where the user can choose options while watching. The service provider can offer an interactive quiz show where the user can answer quizzes while watching. The service provider can offer an interactive anime where the user can choose how the story unfolds while watching. In this way, the service provider can enrich the user's viewing experience by offering interactive content.

[0059] The management department can set viewing reminders in addition to user viewing reservations. For example, the management department can set reminders for content that users have reserved for viewing and send notifications before the viewing time. The management department can set reminders for content that users want to watch and send notifications when it becomes available. The management department can set reminders for the next episode of a series that a user is currently watching and send notifications when it becomes available. This ensures that users don't miss out on content they want to watch by setting viewing reminders.

[0060] The data collection unit can collect information about the user's viewing environment in addition to their past viewing history. For example, the data collection unit can collect information about the type of device the user is using to view content. For example, the data collection unit can collect information about the location where the user is viewing content. For example, the data collection unit can collect information about the time of day the user is viewing content. By collecting information about the viewing environment, it becomes possible to understand the user's viewing history in more detail.

[0061] The data collection unit can filter viewing history based on the user's current interests. For example, it can prioritize collecting content in genres the user is currently interested in. For example, it can collect relevant viewing history based on keywords the user has recently searched for. For example, it can analyze trends in online communities the user participates in and collect relevant viewing history. By filtering viewing history based on the user's current interests, it is possible to collect highly relevant data.

[0062] The data collection unit can prioritize the collection of highly relevant viewing history by considering the user's geographical location when collecting viewing history. For example, if the user is in a specific region, the data collection unit will prioritize the collection of viewing history of content related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of viewing history of content related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of viewing history related to events and news around the user's home. In this way, by considering the user's geographical location, the data collection unit can prioritize the collection of highly relevant viewing history.

[0063] The data collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the data collection unit can collect relevant viewing history based on content shared by the user on social media. For example, the data collection unit can analyze the content posted by accounts that the user follows and collect relevant viewing history. For example, the data collection unit can analyze the trends of groups and communities that the user participates in and collect relevant viewing history. In this way, relevant viewing history can be collected by analyzing the user's social media activity.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects the user's viewing history. This viewing history includes the title of the content viewed, viewing time, and viewing frequency. The data collection unit collects metadata of the content viewed by the user and stores it as viewing history. The data collection unit also updates the user's viewing history in real time, instantly adding newly viewed content to the viewing history. Furthermore, the data collection unit ensures data security by regularly backing up the viewing history and saving it to cloud storage. Step 2: The recommendation unit provides personalized recommendations based on the viewing history collected by the data collection unit. The recommendation unit analyzes the user's past viewing history, interests, and behavioral patterns to recommend the most suitable content. For example, it recommends content similar to what the user has watched in the past, or new genres of content based on the user's interests. It also learns the user's behavioral patterns and makes recommendations at the optimal time. Step 3: The identification unit identifies the content recommended by the recommendation unit. The identification unit identifies content based on metadata, tag information, and relevance to viewing history. For example, it identifies content based on its title and genre, and improves the accuracy of identification by evaluating its relevance to viewing history. It also identifies content based on tags assigned to it. Step 4: The notification unit notifies the user of the content identified by the identification unit. The notification unit adjusts the timing of the notification, the notification method (email, push notification, etc.), and the details of the notification content. For example, it sends a push notification to the user's smartphone at a time when the user is available to view the content. It also customizes the notification content and adjusts it based on the user's interests.

[0066] (Example of form 2) The StreamGuide AI system according to an embodiment of the present invention is a system that notifies users in real time of video content such as sports, dramas, and anime based on their preferences, and enables them to reserve viewing. By registering categories and keywords of interest, the StreamGuide AI system allows users to receive real-time notifications when relevant videos are streamed. It also provides highlight videos and short movies, and centrally manages all video content. Using AI, it learns from the user's viewing history and preferences to provide personalized recommendations. Furthermore, the AI ​​automatically scans video platforms and identifies and notifies users of new content based on their registered interests. This allows users to catch their favorite content without missing anything, significantly improving their satisfaction with video viewing. It also enables time savings and efficient information gathering. Thus, the StreamGuide AI system can improve the user's viewing experience and enable efficient information gathering.

[0067] The StreamGuide AI system according to this embodiment comprises a collection unit, a recommendation unit, an identification unit, and a notification unit. The collection unit collects the user's viewing history. The viewing history includes, but is not limited to, the title of the content viewed, the viewing time, and the viewing frequency. The collection unit also collects metadata of the content viewed by the user and stores it as viewing history. The collection unit can also update the user's viewing history in real time. For example, the collection unit instantly adds newly viewed content to the viewing history. Furthermore, the collection unit can periodically back up the user's viewing history. For example, the collection unit stores the viewing history in cloud storage to ensure data security. The recommendation unit makes personalized recommendations based on the viewing history collected by the collection unit. The recommendation unit analyzes, for example, the user's past viewing history, interests, and behavioral patterns to recommend the most suitable content. For example, the recommendation unit recommends content similar to content the user has viewed in the past. The recommendation unit can also discover and recommend new content based on the user's interests. For example, the recommendation unit recommends new genres of content that the user might be interested in. Furthermore, the recommendation unit can learn the user's behavior patterns and make recommendations at the optimal time. For example, the recommendation unit recommends content according to the time of day the user is watching. The identification unit identifies the content recommended by the recommendation unit. The identification unit identifies content based on, for example, the content's metadata, tag information, and relevance to viewing history. For example, the identification unit identifies content based on its title and genre. The identification unit can also evaluate the relevance of content to viewing history to improve the accuracy of identification. For example, the identification unit prioritizes identifying content that is highly relevant to content the user has watched in the past. Furthermore, the identification unit can utilize the content's tag information to improve the accuracy of identification. For example, the identification unit identifies content based on tags assigned to it. The notification unit notifies the user of the content identified by the identification unit. The notification unit adjusts the timing of the notification, the notification method (email, push notification, etc.), and the details of the notification content before sending it.For example, the notification unit sends notifications according to the time the user is available to watch. The notification unit can also select the most suitable notification method for the user's device. For example, the notification unit sends push notifications to a smartphone. Furthermore, the notification unit can customize the notification content to provide the most relevant information to the user. For example, the notification unit adjusts the notification content based on the user's interests. As a result, the StreamGuide AI system according to this embodiment can improve the user's viewing experience.

[0068] The data collection unit collects users' viewing history. Viewing history includes, but is not limited to, the title of the content viewed, viewing time, and viewing frequency. The data collection unit collects metadata of the content viewed by users and stores it as viewing history. Specifically, the data collection unit collects detailed metadata such as the title, genre, viewing start time, viewing end time, and viewing device of the content viewed by users. This allows for a detailed understanding of the user's viewing behavior. The data collection unit can also update the user's viewing history in real time. For example, the data collection unit instantly adds newly viewed content to the viewing history. This ensures that the user always has the most up-to-date viewing history. Furthermore, the data collection unit can periodically back up the user's viewing history. For example, the data collection unit stores the viewing history in cloud storage to ensure data security. Cloud storage minimizes the risk of data loss by providing data redundancy. In addition, the data collection unit can encrypt or anonymize viewing history to protect user privacy. This reduces the risk of the user's personal information being leaked to third parties. Through these functions, the data collection unit can efficiently and securely collect users' viewing history, thereby improving the overall performance and reliability of the system.

[0069] The recommendation department provides personalized recommendations based on viewing history collected by the data collection department. For example, the recommendation department analyzes a user's past viewing history, interests, and behavioral patterns to recommend the most suitable content. Specifically, it uses machine learning algorithms to analyze the user's viewing history and identify content that the user tends to prefer. For example, it recommends content similar to content the user has previously viewed. The recommendation department can also discover and recommend new content based on the user's interests. For example, it recommends content in new genres that the user might be interested in. Furthermore, the recommendation department can learn the user's behavioral patterns and make recommendations at the optimal time. For example, it recommends content based on the time of day the user is likely to be viewing it. This ensures that content is delivered at the time when the user is most likely to view it. Through these functions, the recommendation department can provide highly accurate and personalized content recommendations to users, improving the viewing experience. Additionally, the recommendation department can collect user feedback to continuously improve the accuracy of its recommendation algorithms. For example, it collects whether the user viewed the recommended content and their post-viewing evaluations, using this data as learning data for the algorithm. This allows the recommendation system to continue providing content that is more tailored to the user's preferences.

[0070] The identification unit identifies content recommended by the recommendation unit. The identification unit identifies content based on factors such as metadata, tag information, and relevance to viewing history. Specifically, the identification unit identifies content based on its title and genre. For example, it analyzes keywords and genre tags included in the content title to determine if it matches the user's interests. The identification unit can also evaluate the relevance of content to viewing history to improve identification accuracy. For example, it prioritizes identifying content that is highly relevant to content the user has previously viewed. This allows for efficient identification of content that the user is likely to be interested in. Furthermore, the identification unit can utilize content tag information to improve identification accuracy. For example, it identifies content based on tags assigned to it. Tag information indicates the content and characteristics, making it effective in improving identification accuracy. Through these functions, the identification unit can accurately identify recommended content and provide the user with the most suitable content. In addition, the identification unit can continuously evaluate the identification results and build a feedback loop to improve the accuracy of the identification algorithm. This allows the identification unit to achieve highly accurate content identification based on the latest information at all times, thereby improving the user's viewing experience.

[0071] The notification unit notifies users of content identified by the identification unit. The notification unit adjusts the timing of notifications, the notification method (email, push notification, etc.), and the details of the notification content. Specifically, the notification unit sends notifications at times when the user is available to view the content. For example, it might notify users of content when they are relaxed after work. The notification unit can also select the most suitable notification method for the user's device. For example, it might send push notifications to smartphones. Push notifications are effective in preventing users from missing viewing opportunities because they can be checked immediately. Furthermore, the notification unit can customize notification content to provide the most relevant information to the user. For example, it might adjust notification content based on the user's interests. This allows for effective notification of content that is likely to interest the user. Through these functions, the notification unit can improve the viewing experience by notifying users of the right content at the right time. Additionally, the notification unit can collect user feedback and continuously improve the accuracy of its notification algorithm. For example, it can analyze user behavior after receiving a notification (whether they viewed it, their evaluation, etc.) to evaluate the effectiveness of the notification. This allows the notification unit to provide more effective notifications to users and improve the viewing experience.

[0072] The service provider can provide highlight videos and short movies. For example, the service provider can provide highlight videos of sports matches. For example, the service provider can edit the important scenes of a match and create a highlight video that can be viewed in a short amount of time. The service provider can also provide short movies of drama episodes. For example, the service provider can extract the important scenes of an episode and create a short movie that can be viewed in a short amount of time. Furthermore, the service provider can provide highlight videos of anime. For example, the service provider can edit the important scenes of an anime and create a highlight video that can be viewed in a short amount of time. By providing users with highlight videos and short movies, the viewing experience can be diversified. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generation AI perform the editing of highlight videos and short movies.

[0073] The management unit can manage viewing reservations. The management unit can, for example, provide an interface for users to make viewing reservations. For example, the management unit can allow users to select the content they want to watch and make a viewing reservation. The management unit can also provide notifications for viewing reservations. For example, the management unit can send a notification to the user when the time for a viewing reservation is approaching. Furthermore, the management unit can also cancel or change viewing reservations. For example, the management unit can allow users to cancel viewing reservations or change viewing times. This allows users to efficiently plan their viewing by managing their viewing reservations. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can have a generation AI perform the management of viewing reservations.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of viewing history collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect viewing history frequently to obtain detailed data. For example, if the user is stressed, the data collection unit will reduce the frequency of viewing history collection to alleviate the user's burden. For example, if the user is excited, the data collection unit will collect viewing history in real time and reflect it immediately. This reduces the user's burden by adjusting the timing of viewing 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can analyze the user's past viewing history and select the optimal data collection method. For example, the data collection unit can analyze patterns in content the user has previously viewed and determine the optimal timing for collection. For example, the data collection unit can identify viewing tendencies during specific time periods from the user's viewing history and concentrate data collection during those times. For example, the data collection unit can prioritize collecting content that is viewed frequently based on the user's viewing history. In this way, the optimal data collection method can be selected by analyzing the user's past viewing history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's viewing history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter viewing history based on the user's current interests. For example, the data collection unit can prioritize collecting content in genres that the user is currently interested in. For example, the data collection unit can collect relevant viewing history based on keywords the user has recently searched for. For example, the data collection unit can analyze trends in online communities the user participates in and collect relevant viewing history. This allows for the collection of highly relevant data by filtering viewing history based on the user's current interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user interest data into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of viewing history to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed viewing history data. For example, if the user is stressed, the data collection unit will prioritize collecting only important viewing history data. For example, if the user is excited, the data collection unit will prioritize collecting viewing history data in real time. This allows for the priority collection of important data by determining the priority of viewing history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The collection unit can prioritize the collection of highly relevant viewing history by considering the user's geographical location information when collecting viewing history. For example, if the user is in a specific region, the collection unit will prioritize the collection of viewing history of content related to that region. For example, if the user is traveling, the collection unit will prioritize the collection of viewing history of content related to the travel destination. For example, if the user is at home, the collection unit will prioritize the collection of viewing history related to events and news around the user's home. In this way, by considering the user's geographical location information, the collection unit can prioritize the collection of highly relevant viewing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0079] The data collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the data collection unit can collect relevant viewing history based on content shared by the user on social media. For example, the data collection unit can analyze the content posted by accounts that the user follows and collect relevant viewing history. For example, the data collection unit can analyze the trends of groups and communities that the user participates in and collect relevant viewing history. In this way, relevant viewing history can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant history.

[0080] 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 will provide recommendations with detailed descriptions. If the user is stressed, the recommendation system will provide concise and to-the-point recommendations. If the user is excited, the recommendation system will provide recommendations with visually stimulating effects. By adjusting the way recommendations are presented according to the user's emotions, more effective 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.

[0081] The recommendation system can adjust the level of detail of recommendations based on the importance of the content. For example, for important content, the recommendation system will provide recommendations with detailed descriptions. For general content, the recommendation system will provide recommendations with concise descriptions. For content that the user is particularly interested in, the recommendation system will provide recommendations with detailed information. By adjusting the level of detail of recommendations based on the importance of the content, 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 using AI. For example, the recommendation system can input content importance data into a generating AI and have the generating AI perform the adjustment of recommendation detail.

[0082] The recommendation system can apply different recommendation algorithms depending on the content category during the recommendation process. For example, for sports content, the recommendation system might include match highlights and player interviews. For drama content, it might include episode summaries and cast information. For anime content, it might include character introductions and related merchandise information. By applying different recommendation algorithms depending on the content category, more accurate recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input content category data into a generating AI and have the generating AI apply different recommendation algorithms.

[0083] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is relaxed, the recommendation unit will provide longer recommendations with detailed descriptions. If the user is stressed, the recommendation unit will provide concise and short recommendations. If the user is excited, the recommendation unit will provide short 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 unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The recommendation system can determine recommendation priorities based on the content's release date. For example, it might prioritize newly released content. For example, it might prioritize sequels to content the user has previously watched. For example, it might prioritize content related to a specific event or season. By prioritizing recommendations based on the content's release date, it becomes possible to provide recommendations at the optimal time for the user. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input content release date data into a generating AI and have the generating AI determine the recommendation priorities.

[0085] The recommendation system can adjust the order of recommendations based on the relevance of the content. For example, the recommendation system may prioritize recommending content that is highly relevant to content the user has previously viewed. For example, the recommendation system may prioritize recommending content in genres that the user is interested in. For example, the recommendation system may prioritize recommending content shared by accounts that the user follows. By adjusting the order of recommendations based on the relevance of the content, it becomes possible to recommend content in the order that is most optimal for the user. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system may input content relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0086] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated emotions. For example, if the user is relaxed, the identification unit uses detailed criteria for identification. If the user is stressed, the identification unit uses concise criteria for identification. If the user is excited, the identification unit uses visually stimulating criteria for identification. By adjusting the identification criteria according to the user's emotions, more appropriate identification becomes possible. 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 identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The identification unit can improve the accuracy of its identification by considering the interrelationships of content during the identification process. For example, when identifying episodes of the same series, the identification unit considers the relationship with preceding and succeeding episodes. For example, when identifying content of the same genre, the identification unit considers the relationship with other related content. For example, when identifying content featuring the same actors, the identification unit considers the relationship with other works featuring the same actors. This improves the accuracy of identification by considering the interrelationships of content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input interrelationship data of content into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0088] The identification unit can perform identification by considering the attribute information of the content provider. For example, when identifying content produced by a specific studio, the identification unit considers the studio's attribute information. For example, when identifying content produced by a specific director, the identification unit considers the director's attribute information. For example, when identifying content provided by a specific distribution platform, the identification unit considers the platform's attribute information. This improves the accuracy of identification by considering the attribute information of the content provider. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input provider attribute information data into a generating AI and have the generating AI perform the identification.

[0089] The identification unit can estimate the user's emotions and adjust the order in which the identification results are displayed based on the estimated user emotions. For example, if the user is relaxed, the identification unit displays the identification results in an order that includes detailed information. If the user is stressed, the identification unit displays the identification results in an order that includes concise information. If the user is excited, the identification unit displays the identification results in a visually stimulating order. By adjusting the order in which the identification results are displayed according to the user's emotions, it becomes possible to provide more appropriate information. 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 identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The identification unit can perform identification while considering the geographical distribution of the content. For example, when identifying content popular in a particular region, the identification unit considers the attribute information of that region. For example, when identifying content produced in a particular country, the identification unit considers the attribute information of that country. For example, when identifying content related to an event held in a particular city, the identification unit considers the attribute information of that city. This improves the accuracy of identification by considering the geographical distribution of the content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into a generating AI and have the generating AI perform the identification.

[0091] The identification unit can improve the accuracy of its identification by referring to relevant literature during the identification process. For example, the identification unit can improve the accuracy of its identification by referring to relevant review articles during content identification. For example, the identification unit can improve the accuracy of its identification by referring to relevant academic papers during content identification. For example, the identification unit can improve the accuracy of its identification by referring to relevant news articles during content identification. This improves the accuracy of identification by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0092] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is relaxed, the notification unit will provide a detailed notification. If the user is stressed, the notification unit will provide a concise notification. If the user is excited, the notification unit will provide a visually stimulating notification. By adjusting the notification method according to the user's emotions, more effective notifications can be achieved. 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, for example, or without AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has preferred to receive in the past. For example, the notification unit may avoid selecting notification methods that the user has ignored in the past. For example, the notification unit may select the optimal notification method for a specific time period from the user's past notification history. In this way, the optimal notification method can be selected by referring to the user's past notification 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 may input the user's notification history data into a generating AI and have the generating AI perform the selection of the optimal notification method.

[0094] The notification unit can adjust the timing of notifications based on the user's current situation. For example, if the user is working, the notification unit will send notifications sparingly. If the user is on a break, the notification unit will send notifications more actively. If the user is on the move, the notification unit will send notifications in real time. By adjusting the timing of notifications based on the user's current situation, it becomes possible to send notifications at a more appropriate time. 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 current situation data into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0095] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit will prioritize important notifications. If the user is stressed, the notification unit will prioritize less urgent notifications. If the user is excited, the notification unit will prioritize visually stimulating notifications. In this way, important notifications can be prioritized by determining the priority 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.

[0096] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. For example, if the user is using a tablet, the notification unit will prioritize in-app notifications. For example, if the user is using a smartwatch, the notification unit will prioritize vibration notifications. In this way, the notification unit can select the optimal notification method by considering the user's device 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 device information into a generating AI and have the generating AI select the optimal notification method.

[0097] The notification unit can analyze the user's social media activity and customize the content of notifications when they are sent. For example, the notification unit can send relevant notifications based on content the user has shared on social media. For example, the notification unit can analyze the content of posts from accounts the user follows and send relevant notifications. For example, the notification unit can analyze the trends of groups and communities the user participates in and send relevant notifications. In this way, the content of notifications can be customized 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 without AI. For example, the notification unit can input the user's social media data into a generating AI and have the generating AI customize the content of the notifications.

[0098] The service provider can estimate the user's emotions and adjust how highlight videos and short movies are delivered based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed highlight video. If the user is stressed, the service provider can provide a concise short movie. If the user is excited, the service provider can provide a visually stimulating highlight video. By adjusting the delivery method according to the user's emotions, more effective content delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The service provider can select the optimal service delivery method by referring to the user's past viewing history at the time of delivery. For example, the service provider can analyze patterns of content the user has previously viewed and select the optimal service delivery method. For example, the service provider can identify viewing tendencies at specific time periods from the user's viewing history and concentrate service delivery during those times. For example, the service provider can prioritize providing content that is viewed frequently based on the user's viewing history. In this way, the service provider can select the optimal service delivery method by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's viewing history data into a generating AI and have the generating AI select the optimal service delivery method.

[0100] The service provider can estimate the user's emotions and determine the priority of the content to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize detailed content. For example, if the user is stressed, the service provider will prioritize concise content. For example, if the user is excited, the service provider will prioritize visually stimulating content. In this way, by determining the priority of the content to be provided according to the user's emotions, important content can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider will prioritize providing content related to that region. For example, if the user is traveling, the service provider will prioritize providing content related to the travel destination. For example, if the user is at home, the service provider will prioritize providing content related to events and news around the user's home. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0102] The management unit can estimate the user's emotions and adjust the viewing reservation management method based on the estimated user emotions. For example, if the user is relaxed, the management unit will perform detailed viewing reservation management. For example, if the user is stressed, the management unit will perform simple viewing reservation management. For example, if the user is excited, the management unit will perform visually stimulating viewing reservation management. This allows for more effective viewing reservation management by adjusting the viewing reservation management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The management department can select the optimal management method by referring to the user's past viewing reservation history during management. For example, the management department can analyze patterns of content that users have previously reserved to view and select the optimal management method. For example, the management department can identify a tendency for users to reserve content during specific time slots from their viewing reservation history and concentrate management during those time slots. For example, the management department can prioritize managing content that is frequently viewed based on the user's viewing reservation history. In this way, the optimal management method can be selected by referring to the user's past viewing reservation history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's viewing reservation history data into a generating AI and have the generating AI select the optimal management method.

[0104] The management unit can estimate the user's emotions and determine the priority of viewing reservations based on the estimated emotions. For example, if the user is relaxed, the management unit will prioritize detailed viewing reservations. For example, if the user is stressed, the management unit will prioritize concise viewing reservations. For example, if the user is excited, the management unit will prioritize visually stimulating viewing reservations. This allows for the priority management of important viewing reservations by determining the priority of viewing reservations 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 management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The management unit can select the optimal management method during management, taking into account the user's device information. For example, if the user is using a smartphone, the management unit provides a management method optimized for smartphones. For example, if the user is using a tablet, the management unit provides a management method optimized for tablets. For example, if the user is using a smartwatch, the management unit provides a management method optimized for smartwatches. This allows the management unit to select the optimal management method by taking into account the user's device information. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal management method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can collect not only the user's viewing history but also the user's voice commands. For example, the data collection unit can record the content that the user instructed to watch by voice and add it to the viewing history. For example, the data collection unit can collect keywords that the user searched for by voice and reflect the relevant content in the viewing history. For example, the data collection unit can collect rating information of content that the user rated by voice and integrate it into the viewing history. In this way, by collecting voice commands, the user's viewing history can be understood in more detail.

[0108] The service provider can offer interactive content based on the user's viewing history. For example, the service provider can offer an interactive drama where the user can choose options while watching. The service provider can offer an interactive quiz show where the user can answer quizzes while watching. The service provider can offer an interactive anime where the user can choose how the story unfolds while watching. In this way, the service provider can enrich the user's viewing experience by offering interactive content.

[0109] The management department can set viewing reminders in addition to user viewing reservations. For example, the management department can set reminders for content that users have reserved for viewing and send notifications before the viewing time. The management department can set reminders for content that users want to watch and send notifications when it becomes available. The management department can set reminders for the next episode of a series that a user is currently watching and send notifications when it becomes available. This ensures that users don't miss out on content they want to watch by setting viewing reminders.

[0110] The data collection unit can estimate the user's emotions and adjust the method of collecting viewing history based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect viewing history in detail. If the user is stressed, the data collection unit will simplify the method of collecting viewing history. If the user is excited, the data collection unit will collect viewing history in real time. By adjusting the method of collecting viewing history according to the user's emotions, the burden on the user can be reduced.

[0111] The data collection unit can collect information about the user's viewing environment in addition to their past viewing history. For example, the data collection unit can collect information about the type of device the user is using to view content. For example, the data collection unit can collect information about the location where the user is viewing content. For example, the data collection unit can collect information about the time of day the user is viewing content. By collecting information about the viewing environment, it becomes possible to understand the user's viewing history in more detail.

[0112] The data collection unit can filter viewing history based on the user's current interests. For example, it can prioritize collecting content in genres the user is currently interested in. For example, it can collect relevant viewing history based on keywords the user has recently searched for. For example, it can analyze trends in online communities the user participates in and collect relevant viewing history. By filtering viewing history based on the user's current interests, it is possible to collect highly relevant data.

[0113] The data collection unit can estimate the user's emotions and determine the priority of viewing history to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed viewing history data. If the user is stressed, the data collection unit will prioritize collecting only important viewing history data. If the user is excited, the data collection unit will prioritize collecting viewing history data in real time. This allows for the priority collection of important data by determining the priority of viewing history according to the user's emotions.

[0114] The data collection unit can prioritize the collection of highly relevant viewing history by considering the user's geographical location when collecting viewing history. For example, if the user is in a specific region, the data collection unit will prioritize the collection of viewing history of content related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of viewing history of content related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of viewing history related to events and news around the user's home. In this way, by considering the user's geographical location, the data collection unit can prioritize the collection of highly relevant viewing history.

[0115] The data collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the data collection unit can collect relevant viewing history based on content shared by the user on social media. For example, the data collection unit can analyze the content posted by accounts that the user follows and collect relevant viewing history. For example, the data collection unit can analyze the trends of groups and communities that the user participates in and collect relevant viewing history. In this way, relevant viewing history can be collected by analyzing the user's social media activity.

[0116] 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 will provide recommendations with detailed explanations. If the user is stressed, the recommendation system will provide concise and to-the-point recommendations. If the user is excited, the recommendation system will provide recommendations with visually stimulating effects. By adjusting the way recommendations are presented according to the user's emotions, more effective recommendations become possible.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects the user's viewing history. This viewing history includes the title of the content viewed, viewing time, and viewing frequency. The data collection unit collects metadata of the content viewed by the user and stores it as viewing history. The data collection unit also updates the user's viewing history in real time, instantly adding newly viewed content to the viewing history. Furthermore, the data collection unit ensures data security by regularly backing up the viewing history and saving it to cloud storage. Step 2: The recommendation unit provides personalized recommendations based on the viewing history collected by the data collection unit. The recommendation unit analyzes the user's past viewing history, interests, and behavioral patterns to recommend the most suitable content. For example, it recommends content similar to what the user has watched in the past, or new genres of content based on the user's interests. It also learns the user's behavioral patterns and makes recommendations at the optimal time. Step 3: The identification unit identifies the content recommended by the recommendation unit. The identification unit identifies content based on metadata, tag information, and relevance to viewing history. For example, it identifies content based on its title and genre, and improves the accuracy of identification by evaluating its relevance to viewing history. It also identifies content based on tags assigned to it. Step 4: The notification unit notifies the user of the content identified by the identification unit. The notification unit adjusts the timing of the notification, the notification method (email, push notification, etc.), and the details of the notification content. For example, it sends a push notification to the user's smartphone at a time when the user is available to view the content. It also customizes the notification content and adjusts it based on the user's interests.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, recommendation unit, identification unit, notification unit, provision unit, and management unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's viewing history using the control unit 46A of the smart device 14 and stores and backs up the viewing history using the identification processing unit 290 of the data processing unit 12. The recommendation unit makes personalized recommendations using the identification processing unit 290 of the data processing unit 12, and the identification unit identifies content using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user using the control unit 46A of the smart device 14, and the provision unit provides highlight videos and short movies using the control unit 46A of the smart device 14. The management unit manages viewing reservations using the control unit 46A of the smart device 14 and backs up viewing reservations using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, recommendation unit, identification unit, notification unit, provision unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's viewing history using the control unit 46A of the smart glasses 214 and stores and backs up the viewing history using the identification processing unit 290 of the data processing unit 12. The recommendation unit makes personalized recommendations using the identification processing unit 290 of the data processing unit 12, and the identification unit identifies content using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user using the control unit 46A of the smart glasses 214, and the provision unit provides highlight videos and short movies using the control unit 46A of the smart glasses 214. The management unit manages viewing reservations using the control unit 46A of the smart glasses 214 and backs up viewing reservations using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, recommendation unit, identification unit, notification unit, provision unit, and management unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's viewing history by the control unit 46A of the headset terminal 314 and stores and backs up the viewing history by the identification processing unit 290 of the data processing unit 12. The recommendation unit makes personalized recommendations by the identification processing unit 290 of the data processing unit 12, and the identification unit identifies content by the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user by the control unit 46A of the headset terminal 314, and the provision unit provides highlight videos and short movies by the control unit 46A of the headset terminal 314. The management unit manages viewing reservations by the control unit 46A of the headset terminal 314 and backs up viewing reservations by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In 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.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 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.

[0171] Each of the multiple elements described above, including the collection unit, recommendation unit, identification unit, notification unit, provision unit, and management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's viewing history by the control unit 46A of the robot 414 and stores and backs up the viewing history by the identification processing unit 290 of the data processing unit 12. The recommendation unit makes personalized recommendations by the identification processing unit 290 of the data processing unit 12, and the identification unit identifies content by the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user by the control unit 46A of the robot 414, and the provision unit provides highlight videos and short movies by the control unit 46A of the robot 414. The management unit manages viewing reservations by the control unit 46A of the robot 414 and backs up viewing reservations by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A collection department that collects viewing history, A recommendation unit provides personalized recommendations based on viewing history collected by the aforementioned collection unit, An identification unit that identifies content recommended by the aforementioned recommendation unit, The system includes a notification unit that notifies the content identified by the identification unit. A system characterized by the following features. (Note 2) It includes a section that provides highlight videos and short movies. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a management department that manages viewing reservations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of viewing history collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past viewing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting viewing history, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of viewing history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting viewing history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting viewing 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 10) 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 11) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned recommendation department, When making recommendations, we prioritize recommendations based on the timing of content delivery. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned identification unit is It estimates the user's sentiment and adjusts the identification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned identification unit is During identification, the accuracy of identification is improved by considering the interrelationships between content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned identification unit is During identification, the attribute information of the content provider is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned identification unit is It estimates the user's sentiment and adjusts the order in which the identification results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned identification unit is During identification, the geographical distribution of the content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned identification unit is During identification, we refer to relevant literature related to the content to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the timing of the notifications will be adjusted based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The aforementioned supply unit is, We estimate the user's emotions and adjust how highlight videos and short films are delivered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, the system will refer to the user's past viewing history to select the most suitable delivery method. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned management department, It estimates the user's emotions and adjusts how viewing reservations are managed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned management department, During management, the system selects the optimal management method by referring to the user's past viewing reservation history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned management department, It estimates the user's emotions and determines the priority of viewing reservations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned management department, During management, the optimal management method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0191] 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 collection department that collects viewing history, A recommendation unit provides personalized recommendations based on viewing history collected by the aforementioned collection unit, An identification unit that identifies content recommended by the aforementioned recommendation unit, The system includes a notification unit that notifies the content identified by the identification unit. A system characterized by the following features.

2. It includes a section that provides highlight videos and short movies. The system according to feature 1.

3. It has a management department that manages viewing reservations. The system according to feature 1.

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

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

6. The aforementioned collection unit is When collecting viewing history, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of viewing history to collect based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting viewing history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system according to feature 1.

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

10. 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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