System

Abstract

To recommend an optimal content on the basis of viewing history of a user.SOLUTION: A system according to an embodiment includes a collection unit, a determination unit, and a recommendation unit. The collection unit collects viewing history of a user. The determination unit determines a content to be recommended to the user on the basis of the information collected by the collection unit. The recommendation unit recommends the content determined by the determination unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately recommended optimal content based on a user's viewing history, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal content based on a user's viewing history. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, a determination unit, and a recommendation unit. The collection unit collects a viewing history of a user. The determination unit determines content to be recommended to the user based on the information collected by the collection unit. The recommendation unit recommends the content determined by the determination unit to the user. Effect of the Invention

[0007] The system according to the embodiment can recommend optimal content based on the user's viewing history. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The recommendation system AI assistant according to an embodiment of the present invention is a system that analyzes a user's past viewing history and preferences, and makes recommendations from similar genres, artists, movie directors, etc. This system collects the user's viewing history, determines the content to be recommended to the user based on the collected information, and recommends the determined content to the user. Furthermore, it notifies the user of new releases and popular content, and provides playlists and movie lists that match the user's mood and activity. For example, there is a collection unit that collects the user's viewing history, and collects data on music and movies that the user has viewed in the past. There is a decision unit that determines the content to be recommended to the user based on the collected information, and recommends content similar to the content the user has viewed in the past. Furthermore, there is a recommendation unit that recommends the determined content to the user, and has a function of notifying the user of new releases and popular content. This allows the user to enjoy the latest content without missing it. There is also a provision unit that provides playlists and movie lists that match the user's mood and activity, and when the user wants to relax, it provides a playlist of relaxing music, and when the user is exercising, it provides a playlist of music suitable for exercise. This allows the user to enjoy content that matches their mood and activity at the time. Furthermore, there is a function to preferentially recommend regionally limited content based on the user's geographic location information, and when the user is in a specific region, it recommends content that is popular in that region. This allows the user to enjoy content that is tailored to the characteristics of the region. Finally, there is a function to explain the reason for the recommendation based on the user's viewing history, such as "I recommend this movie B because it is similar to movie A that you have viewed in the past." This makes it easier for the user to understand the reason for the recommended content. In this way, the recommendation system AI assistant of the present invention can increase user satisfaction by analyzing the user's viewing history and preferences and recommending the most suitable content.

[0029] The recommendation system AI assistant according to the embodiment includes a collection unit, a determination unit, and a recommendation unit. The collection unit collects the viewing history of the user. The viewing history of the user includes, for example, the type of content viewed, the viewing time, and the viewing frequency, but is not limited to such examples. The collection unit collects information such as the titles and artist names of movies viewed by the user, and the viewing dates and times. The collection unit can analyze the viewing history using AI and grasp the user's preferences and viewing patterns. The determination unit determines the content to be recommended to the user based on the information collected by the collection unit. The determination unit recommends, for example, content similar to the content the user has viewed in the past. Criteria for similar content include, but are not limited to such examples, matching of genres, viewer ratings, and commonalities in content. The determination unit can analyze the viewing history using AI and recommend optimal content based on the user's preferences and viewing patterns. The recommendation unit recommends the content determined by the determination unit to the user. The recommendation unit has a function of notifying the user of, for example, new releases and hot topics. This allows the user to enjoy the latest content without missing it. The recommendation unit can analyze the user's viewing history using AI and recommend content at the optimal timing. This allows the recommendation system AI assistant according to the embodiment to recommend optimal content based on the user's viewing history.

[0030] The collection unit collects the user's viewing history. The user's viewing history includes, for example, the type of content viewed, viewing time, viewing frequency, etc., but is not limited to such examples. Specifically, the collection unit collects information such as the titles and artist names of movies viewed by the user, and the viewing dates and times. These data are automatically acquired from the logs of the user's device and streaming service. The collection unit can analyze the viewing history using AI to understand the user's preferences and viewing patterns. When analyzing the viewing history data, the AI ​​uses machine learning algorithms to model the user's preferences. For example, collaborative filtering and content-based filtering are used to identify the genres and artists that the user prefers. Furthermore, by analyzing the viewing time and frequency data, it is possible to understand what content the user prefers at what time of day. This allows the collection unit to provide basic data for analyzing the user's viewing history in detail and recommending optimal content to each individual user. In order to ensure the privacy and security of the data, the collection unit introduces encryption technology and access control to protect the user's personal information. In addition, the collection unit collects data with the user's consent and transparently explains the purpose of use of the data. This allows the collection unit to effectively collect data while gaining the trust of users, thereby improving the performance of the entire system.

[0031] The determination unit determines the content to be recommended to the user based on the information collected by the collection unit. For example, the determination unit recommends content similar to the content the user has viewed in the past. Criteria for similar content include, but are not limited to, genre agreement, viewer evaluation, and commonalities in content. Specifically, the determination unit can use AI to analyze the viewing history and recommend optimal content based on the user's preferences and viewing patterns. The AI ​​builds a model for predicting the user's preferences based on the viewing history data. For example, a neural network model using deep learning is used to identify content that is likely to be liked by the user. Furthermore, the determination unit can make more accurate recommendations by utilizing not only the user's viewing history but also the viewing data and evaluation data of other users. For example, collaborative filtering is used to recommend content viewed by other users with similar preferences. Furthermore, the determination unit can respond to changes in the user's preferences based on data updated in real time. For example, the recommendation content is dynamically adjusted to reflect the trends of content recently viewed by the user. As a result, the determination unit can always provide optimal content recommendations based on the latest information, thereby improving user satisfaction.

[0032] The recommendation unit recommends the content determined by the determination unit to the user. The recommendation unit has a function of, for example, notifying the user of new releases and hot topics. Specifically, the recommendation unit can analyze the user's viewing history and recommend content at the optimal timing. AI identifies the timing when the user is most likely to be interested based on the user's viewing patterns and behavioral history. For example, a notification is sent taking into account the time of day and day of the week when the user usually watches. In addition, the recommendation unit presents content in a visually attractive form through a user interface. For example, content is effectively recommended to the user using a customized home screen, push notification, email notification, etc. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy of the recommendation algorithm. For example, data such as whether the user has viewed the recommended content and their evaluation after viewing is collected and used as training data for the AI ​​model. This allows the recommendation unit to provide content that is more suitable for the user's preferences and improve the user experience. In addition, the recommendation unit supports multiple devices and platforms, allowing the user to receive consistent recommendations no matter where they are. This allows the recommendation unit to always provide optimal content to the user and enrich the viewing experience.

[0033] The determination unit can determine content similar to content previously viewed by the user as content to be recommended to the user. The determination unit, for example, recommends content similar to content previously viewed by the user. Criteria for similar content include, but are not limited to, matching genres, viewer ratings, and commonalities in content. The determination unit can analyze the viewing history using AI and recommend optimal content based on the user's preferences and viewing patterns. This makes it possible to recommend similar content based on the user's past viewing history.

[0034] The recommendation system AI assistant includes a notification unit that notifies the user of the latest content. The notification unit notifies the user of, for example, new releases and trending content. Criteria for the latest content include, but are not limited to, release date, trending, and update frequency. The notification unit can use AI to analyze the user's viewing history and notify the user of the latest content at the optimal timing. This allows the user to enjoy the latest content without missing it.

[0035] The recommendation system AI assistant includes a providing unit that provides a playlist or movie list that matches the user's preferences. The providing unit provides, for example, a playlist or movie list that matches the user's mood or activity. Criteria for preferences include, but are not limited to, past viewing history, user ratings, survey results, and the like. The providing unit can analyze the user's viewing history using AI and provide content that is optimal for the user's mood and activity. For example, when the user wants to relax, a playlist of relaxing music is provided, and when the user is exercising, a playlist of music suitable for exercise is provided. This allows the user to enjoy content that matches their mood and activity at the time.

[0036] The determination unit can determine content that is highly similar to the user's preferences as content to be recommended. The determination unit, for example, recommends content that is highly similar to the user's preferences. Criteria for preferences and similarity include, but are not limited to, analysis results of viewing history, user evaluations, and similarities in content. The determination unit can analyze the viewing history using AI and recommend optimal content based on the user's preferences and viewing patterns. This makes it possible to recommend optimal content based on the user's preferences.

[0037] The determination unit can determine that content in a specific region is to be preferentially recommended content based on the geographical location information of the user. Criteria for content in a specific region include, but are not limited to, events that are limited to the region, content related to the culture and customs of the region, and the like. The determination unit can analyze the geographical location information of the user using AI and preferentially recommend content that is limited to the region. This makes it possible to recommend content that is limited to the region based on the geographical location information of the user.

[0038] The recommendation unit can have a function of explaining the reason for recommendation based on the user's viewing history. The criteria for the recommendation reason include, but are not limited to, the analysis result of the viewing history, the user's evaluation, and similarities in content. The recommendation unit can analyze the viewing history using AI and explain to the user, for example, "I recommend this movie B because it is similar to movie A that you have viewed in the past." This makes it easier for the user to understand the reason for the recommended content.

[0039] The determination unit can determine content to be recommended to the user based on the information collected by the collection unit and the user's attributes. The user's attributes include, but are not limited to, age, gender, interests, and occupation. The determination unit can use AI to analyze the viewing history and the user's attributes and recommend optimal content. This makes it possible to recommend optimal content based on the user's attributes.

[0040] The determination unit can determine new genre content as content to be recommended to the user based on the information collected by the collection unit. Criteria for new genre content include, but are not limited to, genre definitions, user ratings, commonalities in content, and the like. The determination unit can use AI to analyze the viewing history and recommend new genre content to the user. This makes it possible to recommend new genre content to the user.

[0041] The collection unit can analyze the user's past viewing history and select the optimal collection method. For example, the collection unit prioritizes collection of content that the user has frequently viewed in the past. If the user has a tendency to view content during a specific time period, collection is concentrated on that time period. If the user has a tendency to view content on a specific device, data from that device is prioritized for collection. The collection unit can analyze the viewing history using AI and select the optimal collection method. This makes it possible to select the optimal collection method based on the user's past viewing history.

[0042] When collecting the viewing history, the collection unit can filter the viewing history based on the user's current living situation and areas of interest. For example, when the user is at work, the collection unit preferentially collects viewing history of work-related content. When the user is on vacation, the collection unit preferentially collects viewing history of relaxing content. When the user is interested in a particular hobby, the collection unit preferentially collects viewing history of content related to that hobby. The collection unit can analyze the viewing history using AI and filter based on the user's living situation and areas of interest. This makes it possible to filter the viewing history based on the user's living situation and areas of interest.

[0043] When collecting the viewing history, the collection unit can preferentially collect a history that is highly relevant based on the geographical location information of the user. For example, when the user is in a specific area, the collection unit preferentially collects a viewing history of content that is popular in that area. When the user is traveling, the collection unit preferentially collects a viewing history of content related to the travel destination. When the user is at home, the collection unit preferentially collects a viewing history of content that the user tends to watch at home. The collection unit can analyze the viewing history using AI and preferentially collect a history that is highly relevant in consideration of the geographical location information of the user. This makes it possible to preferentially collect a viewing history based on the geographical location information of the user.

[0044] When collecting the viewing history, the collection unit can analyze the user's social media activities and collect related histories. For example, the collection unit preferentially collects viewing histories of content shared by the user on social media. The collection unit preferentially collects viewing histories of content that the user "likes" on social media. The collection unit preferentially collects viewing histories of content of artists and film directors that the user follows on social media. The collection unit can analyze social media activities using AI and collect related viewing histories. This makes it possible to collect viewing histories based on the user's social media activities.

[0045] When deciding the content to be recommended, the determination unit can adjust the level of detail of the recommendation based on the importance of the user's past viewing history. For example, the determination unit makes a detailed recommendation related to content frequently viewed by the user. Makes a brief recommendation related to content viewed by the user only once. If the user is interested in a specific genre, makes a detailed recommendation related to the genre. Criteria for the level of importance include, but are not limited to, the number of views, viewing time, and user evaluation. The determination unit can analyze the viewing history using AI and adjust the level of detail of the recommendation based on the importance of the user's past viewing history. This makes it possible to adjust the level of detail of the recommendation based on the importance of the user's past viewing history.

[0046] When determining the content to be recommended, the determination unit can apply different recommendation algorithms according to the category of the user's viewing history. For example, if the user watches a lot of movies, the determination unit applies a recommendation algorithm specialized for movies. If the user listens to a lot of music, the determination unit applies a recommendation algorithm specialized for music. If the user watches a lot of documentaries, the determination unit applies a recommendation algorithm specialized for documentaries. Category criteria include, but are not limited to, genre, theme, target age, and the like. The determination unit can analyze the viewing history using AI and apply the optimal recommendation algorithm according to the category of the user's viewing history. This makes it possible to apply the optimal recommendation algorithm according to the category of the user's viewing history.

[0047] When deciding the content to be recommended, the determination unit can determine the priority of the recommendation based on the time of submission of the user's viewing history. For example, the determination unit prioritizes recommendations related to content recently viewed by the user. Recommendations related to content previously viewed by the user are postponed. Content that the user tends to view in a particular season is preferentially recommended in that season. Criteria for the submission time include, but are not limited to, the recording date and time of the viewing history and the update frequency. The determination unit can analyze the viewing history using AI and determine the priority of the recommendation based on the time of submission of the user's viewing history. This makes it possible to determine the priority of the recommendation based on the time of submission of the user's viewing history.

[0048] When deciding the content to be recommended, the determination unit can adjust the order of recommendations based on the relevance of the user's viewing history. For example, the determination unit first makes recommendations related to content frequently viewed by the user. It postpones recommendations related to content viewed by the user only once. If the user is interested in a specific genre, it first makes recommendations related to that genre. Criteria for relevance include, but are not limited to, commonalities in viewing history, user ratings, and degree of content agreement. The determination unit can analyze the viewing history using AI and adjust the order of recommendations based on the relevance of the user's viewing history. This makes it possible to adjust the order of recommendations based on the relevance of the user's viewing history.

[0049] When explaining the reason for recommendation, the recommendation unit can select the optimal explanation method by referring to the user's past viewing history. For example, the recommendation unit explains a specific reason related to the content the user has viewed in the past. If the user is interested in a specific genre, the recommendation unit explains a reason related to that genre. If the user is interested in a specific artist or film director, the recommendation unit explains a reason related to that person. Criteria for the optimal explanation method include, but are not limited to, text explanation, audio explanation, visual explanation, and the like. The recommendation unit can analyze the viewing history using AI and select the optimal explanation method based on the user's past viewing history. This makes it possible to select the optimal explanation method based on the user's past viewing history.

[0050] The recommendation unit can select the optimal explanation method in consideration of the device information of the user when explaining the reason for recommendation. For example, when the user is using a smartphone, the recommendation unit provides a concise explanation that matches the screen size. When the user is using a tablet, the recommendation unit provides a detailed explanation optimized for a large screen. When the user is using a smartwatch, the recommendation unit provides a concise explanation with high visibility. The criteria for the device information include, but are not limited to, the type of device, OS, screen size, and the like. The recommendation unit can analyze the device information using AI and select the optimal explanation method based on the device information of the user. This makes it possible to select the optimal explanation method based on the device information of the user.

[0051] The notification unit can select optimal notification content by referring to the user's past viewing history at the time of notification. The notification unit, for example, notifies the user of new releases related to content previously viewed by the user. If the user is interested in a particular genre, the notification unit notifies the user of topical content related to that genre. If the user is interested in a particular artist or film director, the notification unit notifies the user of new releases related to that person. Criteria for optimal notification content include, but are not limited to, the type of notification, the timing of notification, and the frequency of notification. The notification unit can use AI to analyze the viewing history and select optimal notification content based on the user's past viewing history. This allows optimal notification content to be selected based on the user's past viewing history.

[0052] The notification unit can select the optimal notification method in consideration of the user's device information at the time of notification. For example, when the user is using a smartphone, the notification unit uses a push notification. When the user is using a tablet, the notification unit uses an in-app notification. When the user is using a smartwatch, the notification unit uses a vibration notification. The criteria for the device information include, but are not limited to, the type of device, OS, screen size, and the like. The notification unit can analyze the device information using AI and select the optimal notification method based on the user's device information. This makes it possible to select the optimal notification method based on the user's device information.

[0053] The notification unit can select the optimal notification timing by referring to the calendar information of the user at the time of notification. The notification unit, for example, refers to the schedule registered in the user's calendar and adjusts the timing of the notification. When the user participates in a specific event, the notification unit notifies the user of content related to the event. The optimal notification timing according to the schedule is selected based on the user's calendar information. The criteria for the calendar information include, but are not limited to, the type of schedule, the importance of the schedule, and the frequency of the schedule. The notification unit can analyze the calendar information using AI and select the optimal notification timing based on the user's calendar information. This makes it possible to select the optimal notification timing based on the user's calendar information.

[0054] The providing unit can select optimal content to be provided by referring to the user's past viewing history when providing a playlist or a movie list. For example, the providing unit provides a playlist related to content that the user has viewed in the past. If the user is interested in a specific genre, the providing unit provides a playlist related to the genre. If the user is interested in a specific artist or film director, the providing unit provides a playlist related to that person. Criteria for optimal content to be provided include, but are not limited to, the type of content, the timing of the content to be provided, and the frequency of the content to be provided. The providing unit can analyze the viewing history using AI and select optimal content to be provided based on the user's past viewing history. This makes it possible to select optimal content to be provided based on the user's past viewing history.

[0055] The providing unit can perform filtering based on the user's current life situation and field of interest when providing a playlist or a movie list. For example, when the user is at work, the providing unit provides a playlist of work-related content. When the user is on vacation, the providing unit provides a playlist of relaxing content. When the user is interested in a particular hobby, the providing unit provides a playlist of content related to the hobby. Criteria for life situations include, but are not limited to, work situations, home situations, health conditions, and the like. Criteria for fields of interest include, but are not limited to, hobbies, topics of interest, fields of expertise, and the like. The providing unit can analyze the viewing history using AI and perform filtering based on the user's life situation and field of interest. This makes it possible to filter playlists and movie lists based on the user's life situation and field of interest.

[0056] The providing unit can select optimal content to be provided in consideration of the geographical location information of the user when providing a playlist or a movie list. For example, when the user is in a specific area, the providing unit provides a playlist of content that is popular in that area. When the user is traveling, the providing unit provides a playlist of content related to the travel destination. When the user is at home, the providing unit provides a playlist of content that the user tends to watch at home. Criteria for the geographical location information include, but are not limited to, GPS data, IP address, location information services, and the like. The providing unit can analyze the geographical location information using AI and select optimal content to be provided based on the geographical location information of the user. This makes it possible to select optimal content to be provided based on the geographical location information of the user.

[0057] The providing unit can analyze the user's social media activity and provide a related list when providing a playlist or a movie list. For example, the providing unit provides a playlist related to content shared by the user on social media. Provides a playlist related to content that the user has "liked" on social media. Provides a playlist related to artists or film directors that the user follows on social media. Criteria for social media activity include, but are not limited to, the content of posts, the number of likes, the number of followers, and the like. The providing unit can analyze social media activity using AI and provide a related list based on the user's social media activity. This makes it possible to provide a related list based on the user's social media activity.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The recommendation system AI assistant can collect ratings of content that the user has previously viewed based on the user's viewing history, and determine the content to recommend based on those ratings. For example, it can preferentially recommend content similar to movies or music that the user has given a high rating. It can also avoid genres or artists related to content that the user has given a low rating. In addition, it can analyze the user's rating history, detect changes in the user's preferences, and adjust the content to be recommended accordingly. This allows for more accurate recommendations based on the user's ratings.

[0060] The recommendation system AI assistant can analyze the viewing time of the content viewed by the user based on the user's viewing history and determine the content to recommend based on that viewing time. For example, it can preferentially recommend content similar to the content the user viewed for a long time. It can also avoid genres and artists related to content that the user stopped viewing in a short time. In addition, it can analyze the user's viewing time pattern and preferentially recommend content that the user tends to view at a specific time period. This allows for more appropriate recommendations based on the user's viewing time.

[0061] The recommendation system AI assistant can analyze the viewing device of the content viewed by the user based on the user's viewing history and determine the content to recommend based on that viewing device. For example, it can recommend content for smartphones that is similar to the content the user viewed on a smartphone. It can also recommend genres and artists for televisions that are related to the content the user viewed on a television. In addition, it can analyze the usage patterns of the user's viewing device and preferentially recommend content that the user tends to view on a specific device. This allows for more appropriate recommendations to be made based on the user's viewing device.

[0062] The recommendation system AI assistant can analyze the viewing frequency of the content viewed by the user based on the user's viewing history, and determine the content to recommend based on that viewing frequency. For example, it can preferentially recommend content similar to the content the user frequently viewed. It can also avoid genres and artists related to content the user viewed only once. In addition, it can analyze the user's viewing frequency pattern and preferentially recommend content that the user tends to view with a certain frequency. This allows for more appropriate recommendations based on the user's viewing frequency.

[0063] The recommendation system AI assistant can analyze the viewing location of the content viewed by the user based on the user's viewing history and determine the content to recommend based on the viewing location. For example, it can recommend content similar to the content the user viewed at home for home use. It can also recommend genres and artists related to the content the user viewed while on the go for away-of-home use. Furthermore, it can analyze the user's viewing location patterns and preferentially recommend content that the user tends to view in a specific location. This allows for more appropriate recommendations based on the user's viewing location.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The collection unit collects the user's viewing history. The viewing history includes the type of content viewed, viewing time, viewing frequency, movie titles and artist names, viewing date and time, etc. The collection unit uses AI to analyze the viewing history and understand the user's preferences and viewing patterns. Step 2: The decision unit decides which content to recommend to the user based on the information collected by the collection unit. The decision unit recommends content similar to content the user has previously viewed, based on criteria such as matching genres, viewer ratings, and commonalities in content. The decision unit uses AI to analyze the viewing history and recommend the most suitable content based on the user's preferences and viewing patterns. Step 3: The recommendation unit recommends the content determined by the determination unit to the user. The recommendation unit has the function of notifying the user of new releases and trending content, and uses AI to analyze the user's viewing history and recommend content at the optimal time.

[0066] (Example 2) The recommendation system AI assistant according to an embodiment of the present invention is a system that analyzes a user's past viewing history and preferences, and makes recommendations from similar genres, artists, movie directors, etc. This system collects the user's viewing history, determines the content to be recommended to the user based on the collected information, and recommends the determined content to the user. Furthermore, it notifies the user of new releases and popular content, and provides playlists and movie lists that match the user's mood and activity. For example, there is a collection unit that collects the user's viewing history, and collects data on music and movies that the user has viewed in the past. There is a decision unit that determines the content to be recommended to the user based on the collected information, and recommends content similar to the content the user has viewed in the past. Furthermore, there is a recommendation unit that recommends the determined content to the user, and has a function of notifying the user of new releases and popular content. This allows the user to enjoy the latest content without missing it. There is also a provision unit that provides playlists and movie lists that match the user's mood and activity, and when the user wants to relax, it provides a playlist of relaxing music, and when the user is exercising, it provides a playlist of music suitable for exercise. This allows the user to enjoy content that matches their mood and activity at the time. Furthermore, there is a function to preferentially recommend regionally limited content based on the user's geographic location information, and when the user is in a specific region, it recommends content that is popular in that region. This allows the user to enjoy content that is tailored to the characteristics of the region. Finally, there is a function to explain the reason for the recommendation based on the user's viewing history, such as "I recommend this movie B because it is similar to movie A that you have viewed in the past." This makes it easier for the user to understand the reason for the recommended content. In this way, the recommendation system AI assistant of the present invention can increase user satisfaction by analyzing the user's viewing history and preferences and recommending the most suitable content.

[0067] The recommendation system AI assistant according to the embodiment includes a collection unit, a determination unit, and a recommendation unit. The collection unit collects the viewing history of the user. The viewing history of the user includes, for example, the type of content viewed, the viewing time, and the viewing frequency, but is not limited to such examples. The collection unit collects information such as the titles and artist names of movies viewed by the user, and the viewing dates and times. The collection unit can analyze the viewing history using AI and grasp the user's preferences and viewing patterns. The determination unit determines the content to be recommended to the user based on the information collected by the collection unit. The determination unit recommends, for example, content similar to the content the user has viewed in the past. Criteria for similar content include, but are not limited to such examples, matching of genres, viewer ratings, and commonalities in content. The determination unit can analyze the viewing history using AI and recommend optimal content based on the user's preferences and viewing patterns. The recommendation unit recommends the content determined by the determination unit to the user. The recommendation unit has a function of notifying the user of, for example, new releases and hot topics. This allows the user to enjoy the latest content without missing it. The recommendation unit can analyze the user's viewing history using AI and recommend content at the optimal timing. This allows the recommendation system AI assistant according to the embodiment to recommend optimal content based on the user's viewing history.

[0068] The collection unit collects the user's viewing history. The user's viewing history includes, for example, the type of content viewed, viewing time, viewing frequency, etc., but is not limited to such examples. Specifically, the collection unit collects information such as the titles and artist names of movies viewed by the user, and the viewing dates and times. These data are automatically acquired from the logs of the user's device and streaming service. The collection unit can analyze the viewing history using AI to understand the user's preferences and viewing patterns. When analyzing the viewing history data, the AI ​​uses machine learning algorithms to model the user's preferences. For example, collaborative filtering and content-based filtering are used to identify the genres and artists that the user prefers. Furthermore, by analyzing the viewing time and frequency data, it is possible to understand what content the user prefers at what time of day. This allows the collection unit to provide basic data for analyzing the user's viewing history in detail and recommending optimal content to each individual user. In order to ensure the privacy and security of the data, the collection unit introduces encryption technology and access control to protect the user's personal information. In addition, the collection unit collects data with the user's consent and transparently explains the purpose of use of the data. This allows the collection unit to effectively collect data while gaining the trust of users, thereby improving the performance of the entire system.

[0069] The determination unit determines the content to be recommended to the user based on the information collected by the collection unit. For example, the determination unit recommends content similar to the content the user has viewed in the past. Criteria for similar content include, but are not limited to, genre agreement, viewer evaluation, and commonalities in content. Specifically, the determination unit can use AI to analyze the viewing history and recommend optimal content based on the user's preferences and viewing patterns. The AI ​​builds a model for predicting the user's preferences based on the viewing history data. For example, a neural network model using deep learning is used to identify content that is likely to be liked by the user. Furthermore, the determination unit can make more accurate recommendations by utilizing not only the user's viewing history but also the viewing data and evaluation data of other users. For example, collaborative filtering is used to recommend content viewed by other users with similar preferences. Furthermore, the determination unit can respond to changes in the user's preferences based on data updated in real time. For example, the recommendation content is dynamically adjusted to reflect the trends of content recently viewed by the user. As a result, the determination unit can always provide optimal content recommendations based on the latest information, thereby improving user satisfaction.

[0070] The recommendation unit recommends the content determined by the determination unit to the user. The recommendation unit has a function of, for example, notifying the user of new releases and hot topics. Specifically, the recommendation unit can analyze the user's viewing history and recommend content at the optimal timing. AI identifies the timing when the user is most likely to be interested based on the user's viewing patterns and behavioral history. For example, a notification is sent taking into account the time of day and day of the week when the user usually watches. In addition, the recommendation unit presents content in a visually attractive form through a user interface. For example, content is effectively recommended to the user using a customized home screen, push notification, email notification, etc. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy of the recommendation algorithm. For example, data such as whether the user has viewed the recommended content and their evaluation after viewing is collected and used as training data for the AI ​​model. This allows the recommendation unit to provide content that is more suitable for the user's preferences and improve the user experience. In addition, the recommendation unit supports multiple devices and platforms, allowing the user to receive consistent recommendations no matter where they are. This allows the recommendation unit to always provide optimal content to the user and enrich the viewing experience.

[0071] The determination unit can determine content similar to content previously viewed by the user as content to be recommended to the user. The determination unit, for example, recommends content similar to content previously viewed by the user. Criteria for similar content include, but are not limited to, matching genres, viewer ratings, and commonalities in content. The determination unit can analyze the viewing history using AI and recommend optimal content based on the user's preferences and viewing patterns. This makes it possible to recommend similar content based on the user's past viewing history.

[0072] The recommendation system AI assistant includes a notification unit that notifies the user of the latest content. The notification unit notifies the user of, for example, new releases and trending content. Criteria for the latest content include, but are not limited to, release date, trending, and update frequency. The notification unit can use AI to analyze the user's viewing history and notify the user of the latest content at the optimal timing. This allows the user to enjoy the latest content without missing it.

[0073] The recommendation system AI assistant includes a providing unit that provides a playlist or movie list that matches the user's preferences. The providing unit provides, for example, a playlist or movie list that matches the user's mood or activity. Criteria for preferences include, but are not limited to, past viewing history, user ratings, survey results, and the like. The providing unit can analyze the user's viewing history using AI and provide content that is optimal for the user's mood and activity. For example, when the user wants to relax, a playlist of relaxing music is provided, and when the user is exercising, a playlist of music suitable for exercise is provided. This allows the user to enjoy content that matches their mood and activity at the time.

[0074] The determination unit can determine content that is highly similar to the user's preferences as content to be recommended. The determination unit, for example, recommends content that is highly similar to the user's preferences. Criteria for preferences and similarity include, but are not limited to, analysis results of viewing history, user evaluations, and similarities in content. The determination unit can analyze the viewing history using AI and recommend optimal content based on the user's preferences and viewing patterns. This makes it possible to recommend optimal content based on the user's preferences.

[0075] The determination unit can determine that content in a specific region is to be preferentially recommended content based on the geographical location information of the user. Criteria for content in a specific region include, but are not limited to, events that are limited to the region, content related to the culture and customs of the region, and the like. The determination unit can analyze the geographical location information of the user using AI and preferentially recommend content that is limited to the region. This makes it possible to recommend content that is limited to the region based on the geographical location information of the user.

[0076] The recommendation unit can have a function of explaining the reason for recommendation based on the user's viewing history. The criteria for the recommendation reason include, but are not limited to, the analysis result of the viewing history, the user's evaluation, and similarities in content. The recommendation unit can analyze the viewing history using AI and explain to the user, for example, "I recommend this movie B because it is similar to movie A that you have viewed in the past." This makes it easier for the user to understand the reason for the recommended content.

[0077] The determination unit can determine content to be recommended to the user based on the information collected by the collection unit and the user's attributes. The user's attributes include, but are not limited to, age, gender, interests, and occupation. The determination unit can use AI to analyze the viewing history and the user's attributes and recommend optimal content. This makes it possible to recommend optimal content based on the user's attributes.

[0078] The determination unit can determine new genre content as content to be recommended to the user based on the information collected by the collection unit. Criteria for new genre content include, but are not limited to, genre definitions, user ratings, commonalities in content, and the like. The determination unit can use AI to analyze the viewing history and recommend new genre content to the user. This makes it possible to recommend new genre content to the user.

[0079] The collection unit can estimate the user's emotion and adjust the timing of collecting the viewing history based on the estimated user's emotion. For example, when the user is relaxed, the collection unit frequently collects the viewing history. When the user is stressed, the collection unit sparingly collects the viewing history. When the user is excited, the collection unit immediately collects the viewing history. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the timing of collecting the viewing history according to the user's emotion.

[0080] The collection unit can analyze the user's past viewing history and select the optimal collection method. For example, the collection unit prioritizes collection of content that the user has frequently viewed in the past. If the user has a tendency to view content during a specific time period, collection is concentrated on that time period. If the user has a tendency to view content on a specific device, data from that device is prioritized for collection. The collection unit can analyze the viewing history using AI and select the optimal collection method. This makes it possible to select the optimal collection method based on the user's past viewing history.

[0081] When collecting the viewing history, the collection unit can filter the viewing history based on the user's current living situation and areas of interest. For example, when the user is at work, the collection unit preferentially collects viewing history of work-related content. When the user is on vacation, the collection unit preferentially collects viewing history of relaxing content. When the user is interested in a particular hobby, the collection unit preferentially collects viewing history of content related to that hobby. The collection unit can analyze the viewing history using AI and filter based on the user's living situation and areas of interest. This makes it possible to filter the viewing history based on the user's living situation and areas of interest.

[0082] The collection unit can estimate the user's emotion and determine the priority of the viewing history to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit preferentially collects the viewing history of relaxing content. When the user is stressed, the collection unit preferentially collects the viewing history of content that helps relieve stress. When the user is excited, the collection unit preferentially collects the viewing history of content that maintains excitement. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to determine the priority of the viewing history according to the user's emotion.

[0083] When collecting the viewing history, the collection unit can preferentially collect a history that is highly relevant based on the geographical location information of the user. For example, when the user is in a specific area, the collection unit preferentially collects a viewing history of content that is popular in that area. When the user is traveling, the collection unit preferentially collects a viewing history of content related to the travel destination. When the user is at home, the collection unit preferentially collects a viewing history of content that the user tends to watch at home. The collection unit can analyze the viewing history using AI and preferentially collect a history that is highly relevant in consideration of the geographical location information of the user. This makes it possible to preferentially collect a viewing history based on the geographical location information of the user.

[0084] When collecting the viewing history, the collection unit can analyze the user's social media activities and collect related histories. For example, the collection unit preferentially collects viewing histories of content shared by the user on social media. The collection unit preferentially collects viewing histories of content that the user "likes" on social media. The collection unit preferentially collects viewing histories of content of artists and film directors that the user follows on social media. The collection unit can analyze social media activities using AI and collect related viewing histories. This makes it possible to collect viewing histories based on the user's social media activities.

[0085] The determination unit can estimate the user's emotion and adjust the expression method of the recommended content based on the estimated user's emotion. For example, when the user is relaxed, the determination unit recommends content with a gentle expression. When the user is excited, the determination unit recommends content with an energetic expression. When the user is stressed, the determination unit recommends content that is useful for relieving stress with a gentle expression. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the expression method of the recommended content according to the user's emotion.

[0086] When deciding the content to be recommended, the determination unit can adjust the level of detail of the recommendation based on the importance of the user's past viewing history. For example, the determination unit makes a detailed recommendation related to content frequently viewed by the user. Makes a brief recommendation related to content viewed by the user only once. If the user is interested in a specific genre, makes a detailed recommendation related to the genre. Criteria for the level of importance include, but are not limited to, the number of views, viewing time, and user evaluation. The determination unit can analyze the viewing history using AI and adjust the level of detail of the recommendation based on the importance of the user's past viewing history. This makes it possible to adjust the level of detail of the recommendation based on the importance of the user's past viewing history.

[0087] When determining the content to be recommended, the determination unit can apply different recommendation algorithms according to the category of the user's viewing history. For example, if the user watches a lot of movies, the determination unit applies a recommendation algorithm specialized for movies. If the user listens to a lot of music, the determination unit applies a recommendation algorithm specialized for music. If the user watches a lot of documentaries, the determination unit applies a recommendation algorithm specialized for documentaries. Category criteria include, but are not limited to, genre, theme, target age, and the like. The determination unit can analyze the viewing history using AI and apply the optimal recommendation algorithm according to the category of the user's viewing history. This makes it possible to apply the optimal recommendation algorithm according to the category of the user's viewing history.

[0088] The determination unit can estimate the user's emotion and adjust the length of the recommended content based on the estimated user's emotion. For example, the determination unit recommends longer content when the user is relaxed. For example, the determination unit recommends shorter content when the user is in a hurry. For example, the determination unit recommends energetic short content when the user is excited. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the length of the recommended content according to the user's emotion.

[0089] When deciding the content to be recommended, the determination unit can determine the priority of the recommendation based on the time of submission of the user's viewing history. For example, the determination unit prioritizes recommendations related to content recently viewed by the user. Recommendations related to content previously viewed by the user are postponed. Content that the user tends to view in a particular season is preferentially recommended in that season. Criteria for the submission time include, but are not limited to, the recording date and time of the viewing history and the update frequency. The determination unit can analyze the viewing history using AI and determine the priority of the recommendation based on the time of submission of the user's viewing history. This makes it possible to determine the priority of the recommendation based on the time of submission of the user's viewing history.

[0090] When deciding the content to be recommended, the determination unit can adjust the order of recommendations based on the relevance of the user's viewing history. For example, the determination unit first makes recommendations related to content frequently viewed by the user. It postpones recommendations related to content viewed by the user only once. If the user is interested in a specific genre, it first makes recommendations related to that genre. Criteria for relevance include, but are not limited to, commonalities in viewing history, user ratings, and degree of content agreement. The determination unit can analyze the viewing history using AI and adjust the order of recommendations based on the relevance of the user's viewing history. This makes it possible to adjust the order of recommendations based on the relevance of the user's viewing history.

[0091] The recommendation unit can estimate the user's emotions and adjust the method of explaining the reason for recommendation based on the estimated user's emotions. For example, when the user is relaxed, the recommendation unit explains the reason for recommendation in a gentle manner. When the user is excited, the recommendation unit explains the reason for recommendation in an energetic manner. When the user is stressed, the recommendation unit explains the reason for recommending content that is useful for relieving stress in a gentle manner. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the method of explaining the reason for recommendation according to the user's emotions.

[0092] When explaining the reason for recommendation, the recommendation unit can select the optimal explanation method by referring to the user's past viewing history. For example, the recommendation unit explains a specific reason related to the content the user has viewed in the past. If the user is interested in a specific genre, the recommendation unit explains a reason related to that genre. If the user is interested in a specific artist or film director, the recommendation unit explains a reason related to that person. Criteria for the optimal explanation method include, but are not limited to, text explanation, audio explanation, visual explanation, and the like. The recommendation unit can analyze the viewing history using AI and select the optimal explanation method based on the user's past viewing history. This makes it possible to select the optimal explanation method based on the user's past viewing history.

[0093] The recommendation unit can select the optimal explanation method in consideration of the device information of the user when explaining the reason for recommendation. For example, when the user is using a smartphone, the recommendation unit provides a concise explanation that matches the screen size. When the user is using a tablet, the recommendation unit provides a detailed explanation optimized for a large screen. When the user is using a smartwatch, the recommendation unit provides a concise explanation with high visibility. The criteria for the device information include, but are not limited to, the type of device, OS, screen size, and the like. The recommendation unit can analyze the device information using AI and select the optimal explanation method based on the device information of the user. This makes it possible to select the optimal explanation method based on the device information of the user.

[0094] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, the notification unit notifies frequently when the user is relaxed. When the user is stressed, the notification unit notifies sparingly. When the user is excited, the notification unit notifies immediately. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the timing of the notification according to the user's emotion.

[0095] The notification unit can select optimal notification content by referring to the user's past viewing history at the time of notification. The notification unit, for example, notifies the user of new releases related to content previously viewed by the user. If the user is interested in a particular genre, the notification unit notifies the user of topical content related to that genre. If the user is interested in a particular artist or film director, the notification unit notifies the user of new releases related to that person. Criteria for optimal notification content include, but are not limited to, the type of notification, the timing of notification, and the frequency of notification. The notification unit can use AI to analyze the viewing history and select optimal notification content based on the user's past viewing history. This allows optimal notification content to be selected based on the user's past viewing history.

[0096] The notification unit can select the optimal notification method in consideration of the user's device information at the time of notification. For example, when the user is using a smartphone, the notification unit uses a push notification. When the user is using a tablet, the notification unit uses an in-app notification. When the user is using a smartwatch, the notification unit uses a vibration notification. The criteria for the device information include, but are not limited to, the type of device, OS, screen size, and the like. The notification unit can analyze the device information using AI and select the optimal notification method based on the user's device information. This makes it possible to select the optimal notification method based on the user's device information.

[0097] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, when the user is relaxed, the notification unit prioritizes notification of relaxing content. When the user is stressed, the notification unit prioritizes notification of content that helps relieve stress. When the user is excited, the notification unit prioritizes notification of content that maintains excitement. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to determine the priority of notifications according to the user's emotion.

[0098] The notification unit can select the optimal notification timing by referring to the calendar information of the user at the time of notification. The notification unit, for example, refers to the schedule registered in the user's calendar and adjusts the timing of the notification. When the user participates in a specific event, the notification unit notifies the user of content related to the event. The optimal notification timing according to the schedule is selected based on the user's calendar information. The criteria for the calendar information include, but are not limited to, the type of schedule, the importance of the schedule, and the frequency of the schedule. The notification unit can analyze the calendar information using AI and select the optimal notification timing based on the user's calendar information. This makes it possible to select the optimal notification timing based on the user's calendar information.

[0099] The providing unit can estimate the user's emotion and adjust the method of providing the playlist or movie list based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides a relaxing playlist. When the user is excited, the providing unit provides an energetic playlist. When the user is stressed, the providing unit provides a playlist that helps relieve stress. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the method of providing the playlist or movie list according to the user's emotion.

[0100] The providing unit can select optimal content to be provided by referring to the user's past viewing history when providing a playlist or a movie list. For example, the providing unit provides a playlist related to content that the user has viewed in the past. If the user is interested in a specific genre, the providing unit provides a playlist related to the genre. If the user is interested in a specific artist or film director, the providing unit provides a playlist related to that person. Criteria for optimal content to be provided include, but are not limited to, the type of content, the timing of the content to be provided, and the frequency of the content to be provided. The providing unit can analyze the viewing history using AI and select optimal content to be provided based on the user's past viewing history. This makes it possible to select optimal content to be provided based on the user's past viewing history.

[0101] The providing unit can perform filtering based on the user's current life situation and field of interest when providing a playlist or a movie list. For example, when the user is at work, the providing unit provides a playlist of work-related content. When the user is on vacation, the providing unit provides a playlist of relaxing content. When the user is interested in a particular hobby, the providing unit provides a playlist of content related to the hobby. Criteria for life situations include, but are not limited to, work situations, home situations, health conditions, and the like. Criteria for fields of interest include, but are not limited to, hobbies, topics of interest, fields of expertise, and the like. The providing unit can analyze the viewing history using AI and perform filtering based on the user's life situation and field of interest. This makes it possible to filter playlists and movie lists based on the user's life situation and field of interest.

[0102] The providing unit can estimate the user's emotion and determine the priority order of playlists and movie lists based on the estimated user's emotion. For example, when the user is relaxed, the providing unit preferentially provides a relaxing playlist. When the user is excited, the providing unit preferentially provides an energetic playlist. When the user is stressed, the providing unit preferentially provides a playlist that is useful for relieving stress. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to determine the priority order of playlists and movie lists according to the user's emotion.

[0103] The providing unit can select optimal content to be provided in consideration of the geographical location information of the user when providing a playlist or a movie list. For example, when the user is in a specific area, the providing unit provides a playlist of content that is popular in that area. When the user is traveling, the providing unit provides a playlist of content related to the travel destination. When the user is at home, the providing unit provides a playlist of content that the user tends to watch at home. Criteria for the geographical location information include, but are not limited to, GPS data, IP address, location information services, and the like. The providing unit can analyze the geographical location information using AI and select optimal content to be provided based on the geographical location information of the user. This makes it possible to select optimal content to be provided based on the geographical location information of the user.

[0104] The providing unit can analyze the user's social media activity and provide a related list when providing a playlist or a movie list. For example, the providing unit provides a playlist related to content shared by the user on social media. Provides a playlist related to content that the user has "liked" on social media. Provides a playlist related to artists or film directors that the user follows on social media. Criteria for social media activity include, but are not limited to, the content of posts, the number of likes, the number of followers, and the like. The providing unit can analyze social media activity using AI and provide a related list based on the user's social media activity. This makes it possible to provide a related list based on the user's social media activity.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The recommendation system AI assistant can collect ratings of content that the user has previously viewed based on the user's viewing history, and determine the content to recommend based on those ratings. For example, it can preferentially recommend content similar to movies or music that the user has given a high rating. It can also avoid genres or artists related to content that the user has given a low rating. In addition, it can analyze the user's rating history, detect changes in the user's preferences, and adjust the content to be recommended accordingly. This allows for more accurate recommendations based on the user's ratings.

[0107] The recommendation system AI assistant can analyze the viewing time of the content viewed by the user based on the user's viewing history and determine the content to recommend based on that viewing time. For example, it can preferentially recommend content similar to the content the user viewed for a long time. It can also avoid genres and artists related to content that the user stopped viewing in a short time. In addition, it can analyze the user's viewing time pattern and preferentially recommend content that the user tends to view at a specific time period. This allows for more appropriate recommendations based on the user's viewing time.

[0108] The recommendation system AI assistant can analyze the viewing device of the content viewed by the user based on the user's viewing history and determine the content to recommend based on that viewing device. For example, it can recommend content for smartphones that is similar to the content the user viewed on a smartphone. It can also recommend genres and artists for televisions that are related to the content the user viewed on a television. In addition, it can analyze the usage patterns of the user's viewing device and preferentially recommend content that the user tends to view on a specific device. This allows for more appropriate recommendations to be made based on the user's viewing device.

[0109] The recommendation system AI assistant can analyze the viewing frequency of the content viewed by the user based on the user's viewing history, and determine the content to recommend based on that viewing frequency. For example, it can preferentially recommend content similar to the content the user frequently viewed. It can also avoid genres and artists related to content the user viewed only once. In addition, it can analyze the user's viewing frequency pattern and preferentially recommend content that the user tends to view with a certain frequency. This allows for more appropriate recommendations based on the user's viewing frequency.

[0110] The recommendation system AI assistant can analyze the viewing location of the content viewed by the user based on the user's viewing history and determine the content to recommend based on the viewing location. For example, it can recommend content similar to the content the user viewed at home for home use. It can also recommend genres and artists related to the content the user viewed while on the go for away-of-home use. Furthermore, it can analyze the user's viewing location patterns and preferentially recommend content that the user tends to view in a specific location. This allows for more appropriate recommendations based on the user's viewing location.

[0111] The recommendation system AI assistant can estimate the user's emotions, analyze the emotional impact of the content the user has viewed based on the estimated user's emotions, and determine the content to recommend based on that impact. For example, if the user is relaxed, it can preferentially recommend relaxing content. Also, if the user is stressed, it can preferentially recommend content that helps relieve stress. Furthermore, it is also possible to analyze changes in the user's emotions and adjust the content to be recommended according to the user's emotions. This allows for more appropriate recommendations based on the user's emotions.

[0112] The recommendation system AI assistant can estimate the user's emotions, analyze the emotional response of the content the user has viewed based on the estimated user's emotions, and determine the content to recommend based on the response. For example, it can preferentially recommend content similar to the content that moved the user. It can also avoid genres and artists related to content that the user found unpleasant. Furthermore, it can analyze the user's emotional response patterns and adjust the content to be recommended according to the user's emotions. This allows for more appropriate recommendations based on the user's emotional response.

[0113] The recommendation system AI assistant can estimate the user's emotions, analyze the emotional value of the content the user has viewed based on the estimated user's emotions, and determine the content to recommend based on that value. For example, it can preferentially recommend content similar to the content that the user found emotionally satisfying. It can also avoid genres and artists related to content that the user found emotionally dissatisfying. Furthermore, it can analyze changes in the user's emotional value and adjust the content to be recommended according to the user's emotions. This allows for more appropriate recommendations based on the user's emotional value.

[0114] The recommendation system AI assistant can estimate the user's emotions, predict the emotional impact of the content the user has viewed based on the estimated user's emotions, and determine the content to recommend based on the prediction. For example, if the user is relaxed, it can preferentially recommend relaxing content. Also, if the user is stressed, it can preferentially recommend content that helps relieve stress. Furthermore, it can predict changes in the user's emotions and adjust the content to be recommended according to the user's emotions. This allows for more appropriate recommendations based on the user's emotions.

[0115] The recommendation system AI assistant can estimate the user's emotions, evaluate the emotional impact of the content the user has viewed based on the estimated user's emotions, and determine the content to recommend based on the evaluation. For example, it can preferentially recommend content similar to the content that the user found emotionally satisfying. It can also avoid genres and artists related to content that the user found emotionally dissatisfying. Furthermore, it can analyze the evaluation of the user's emotional impact and adjust the content to be recommended according to the user's emotions. This allows for more appropriate recommendations based on the user's emotional impact.

[0116] The process flow of the second embodiment will be briefly described below.

[0117] Step 1: The collection unit collects the user's viewing history. The viewing history includes the type of content viewed, viewing time, viewing frequency, movie titles and artist names, viewing date and time, etc. The collection unit uses AI to analyze the viewing history and understand the user's preferences and viewing patterns. Step 2: The decision unit decides which content to recommend to the user based on the information collected by the collection unit. The decision unit recommends content similar to content the user has previously viewed, based on criteria such as matching genres, viewer ratings, and commonalities in content. The decision unit uses AI to analyze the viewing history and recommend the most suitable content based on the user's preferences and viewing patterns. Step 3: The recommendation unit recommends the content determined by the determination unit to the user. The recommendation unit has the function of notifying the user of new releases and trending content, and uses AI to analyze the user's viewing history and recommend content at the optimal time.

[0118] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0120] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0121] For example, the collection unit collects the user's viewing history using the camera 42 and microphone 38B of the smart device 14, and the history is analyzed by the control unit 46A. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the content to be recommended to the user based on the collected information. The recommendation unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of the determined content. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of new releases and hot topics. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides a playlist or movie list that matches the user's mood or activity. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0129] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0132] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0134] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0137] For example, the collection unit collects the user's viewing history using the camera 42 and microphone 238 of the smart glasses 214, and the history is analyzed by the control unit 46A. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the content to be recommended to the user based on the collected information. The recommendation unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user of the determined content. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of new releases and hot topics. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides a playlist or movie list that matches the user's mood or activity. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] For example, the collecting unit collects the user's viewing history using the camera 42 and microphone 238 of the headset type terminal 314, and the history is analyzed by the control unit 46A. The determining unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the content to be recommended to the user based on the collected information. The recommending unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user of the determined content. The notifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of new releases and hot topics. The providing unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides a playlist or movie list that matches the user's mood or activity. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a 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.

[0156] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] For example, the collecting unit collects the user's viewing history using the camera 42 and microphone 238 of the robot 414, and the history is analyzed by the control unit 46A. The determining unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the content to be recommended to the user based on the collected information. The recommending unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of the determined content. The notifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of new releases and hot topics. The providing unit is realized, for example, by the control unit 46A of the robot 414, and provides a playlist or movie list that matches the user's mood or activity. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0172] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0175] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0176] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] In addition, 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 upon request from the data processing device 12.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0183] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0187] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0189] (Appendix 1) A collection unit that collects a user's viewing history; a determination unit that determines content to be recommended to the user based on the information collected by the collection unit; A recommendation unit that recommends the content determined by the determination unit to the user. A system characterized by: (Appendix 2) The determination unit is determining content similar to content previously viewed by the user as content to be recommended to the user; 2. The system of claim 1, (Appendix 3) A notification unit that notifies the user of the latest content 2. The system of claim 1, (Appendix 4) A providing unit for providing a playlist or movie list according to the user's preferences. 2. The system of claim 1, (Appendix 5) The determination unit is Content that is highly similar to the user's preferences is determined as the content to be recommended. 2. The system of claim 1, (Appendix 6) The determination unit is Based on the geographical location information of the user, content in a specific region is determined as content to be preferentially recommended. 2. The system of claim 1, (Appendix 7) The recommendation unit, A function for explaining the reason for recommendation based on the user's viewing history is provided. 2. The system of claim 1, (Appendix 8) The determination unit is determining content to be recommended to the user based on the information collected by the collection unit and the attributes of the user; 2. The system of claim 1, (Appendix 9) The determination unit is Based on the information collected by the collection unit, content in a new genre is determined as content to be recommended to the user. 2. The system of claim 1, (Appendix 10) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting a viewing history based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Analyze users' past viewing history and select the optimal collection method 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting viewing history, it will be filtered based on the user's current life situation and interests. 2. The system of claim 1, (Appendix 13) The collecting unit includes: A user's emotion is estimated, and a priority order of viewing histories to be collected is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting viewing history, the system prioritizes collection of history that is more relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting viewing history, analyze the user's social media activity and collect related history. 2. The system of claim 1, (Appendix 16) The determination unit is Estimate user emotions and adjust the presentation of recommended content based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The determination unit is When deciding which content to recommend, adjust the level of detail of the recommendation based on the importance of the user's past viewing history. 2. The system of claim 1, (Appendix 18) The determination unit is When deciding which content to recommend, different recommendation algorithms are applied depending on the category of the user's viewing history. 2. The system of claim 1, (Appendix 19) The determination unit is Estimate user emotions and adjust the length of recommended content based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The determination unit is When deciding which content to recommend, the system prioritizes recommendations based on when the user's viewing history was submitted. 2. The system of claim 1, (Appendix 21) The determination unit is When deciding which content to recommend, adjust the order of recommendations based on the relevance of the user's viewing history 2. The system of claim 1, (Appendix 22) The recommendation unit, Inferring user emotions and adjusting the explanation method for recommendation reasons based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The recommendation unit, When explaining the reason for a recommendation, the system selects the most appropriate explanation method by referring to the user's past viewing history. 2. The system of claim 1, (Appendix 24) The recommendation unit, When explaining the reason for a recommendation, the system selects the most appropriate explanation method taking into account the user's device information. 2. The system of claim 1, (Appendix 25) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The notification unit is When sending a notification, the system selects the most appropriate notification content by referring to the user's past viewing history. 2. The system of claim 1, (Appendix 27) The notification unit is When notifying users, the optimal notification method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 28) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The notification unit is When sending a notification, the system refers to the user's calendar information to select the optimal notification timing. 2. The system of claim 1, (Appendix 30) The providing unit is Inferring user emotions and adjusting playlist and movie list serving methods based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing playlists or movie lists, the system selects the most suitable content based on the user's past viewing history. 2. The system of claim 1, (Appendix 32) The providing unit is Filter playlists and movie listings based on the user's current life situation and interests 2. The system of claim 1, (Appendix 33) The providing unit is Estimating user emotions and prioritizing playlists and movie lists based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The providing unit is When serving playlists or movie lists, the app takes into account the user's geographic location to select the best content to serve. 2. The system of claim 1, (Appendix 35) The providing unit is When providing playlists or movie lists, analyze users' social media activity to provide relevant lists 2. The system of claim 1, [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects a user's viewing history; a determination unit that determines content to be recommended to the user based on the information collected by the collection unit; A recommendation unit that recommends the content determined by the determination unit to the user. A system characterized by:

2. The determination unit is determining content similar to content previously viewed by the user as content to be recommended to the user; 2. The system of claim 1 .

3. A notification unit that notifies the user of the latest content 2. The system of claim 1 .

4. A providing unit for providing a playlist or movie list according to the user's preferences.

2. The system of claim 1 .

5. The determination unit is Content that is highly similar to the user's preferences is determined as the content to be recommended.

2. The system of claim 1 .

6. The determination unit is Based on the geographical location information of the user, content in a specific region is determined as content to be preferentially recommended.

2. The system of claim 1 .

7. The recommendation unit, A function for explaining the reason for recommendation based on the user's viewing history is provided.

2. The system of claim 1 .

8. The determination unit is determining content to be recommended to the user based on the information collected by the collection unit and the attributes of the user; 2. The system of claim 1 .

9. The determination unit is Based on the information collected by the collection unit, content in a new genre is determined as content to be recommended to the user.

2. The system of claim 1 .

10. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting a viewing history based on the estimated user's emotion.

2. The system of claim 1 .

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