system

Abstract

The system according to the embodiment aims to recommend similar works that match the user's preferences and to guide the user to services that allow viewing. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and a guidance unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends similar works based on the information analyzed by the analysis unit. The guidance unit guides users to services on which they can watch works recommended by the recommendation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for users to find similar works that match their preferences, and it has been time-consuming to search for services that allow viewing.

[0005] The system according to the embodiment aims to recommend similar works that match the user's preferences and to guide the user to services that allow viewing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a guidance unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends similar works based on the information analyzed by the analysis unit. The guidance unit guides users to services on which they can watch works recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend similar works that match the user's preferences and guide the user to services that allow viewing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A movie recommendation system according to an embodiment of the present invention recommends similar movies when a user inputs their favorite movie or genre, and also recommends streaming services and rental services where those movies are available. This movie recommendation system uses AI to analyze the user's input of their favorite movies and genres and recommend similar movies. Furthermore, it provides information on streaming services and rental services where the recommended movies are available. This mechanism allows users to easily find movies that match their preferences. For example, a user may input information such as "I like action movies" or "I like Inception." This information is then input into AI. The AI ​​then analyzes the input information and recommends similar movies. The AI ​​identifies movies that match the user's preferences based on a movie database. For example, if a user inputs "I like Inception," the AI ​​recommends similar movies such as "The Matrix" and "Interstellar." Furthermore, it provides information on streaming services and rental services where the recommended movies are available. The AI ​​references the databases of each streaming service and rental service to identify on which services the recommended movies are available. For example, if "The Matrix" is available on a specific streaming service, the AI ​​provides that information to the user. This mechanism allows users to easily find movies that suit their tastes. For example, if a user enters "I like action movies," the AI ​​will recommend similar works such as "Die Hard" and "Mission: Impossible" and guide them to streaming services or home delivery rental services where those works can be viewed. This makes it easy for users to find movies that suit their tastes. This makes it easy for movie recommendation systems to find movies that suit users' tastes.

[0029] A movie recommendation system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and a guidance unit. The reception unit receives input from a user of "a favorite movie" or "a favorite genre." Information input by the user includes, but is not limited to, the movie title, genre, and director's name. The reception unit can receive information in the form of, for example, text input, voice input, or image input. The analysis unit uses AI to analyze the information received by the reception unit. The analysis can be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to, the examples. For example, the analysis unit can analyze the text information input by the user using natural language processing technology to identify the movie title and genre. The analysis unit can also analyze the image information input by the user using image analysis technology to identify movie posters and scenes. The analysis unit can also analyze the voice information input by the user using voice analysis technology to identify the movie title and genre. The recommendation unit uses AI to recommend similar movies based on the information analyzed by the analysis unit. The recommendation can be performed using, for example, collaborative filtering, content-based filtering, or other methods, but is not limited to, the examples. For example, the recommendation unit may use collaborative filtering technology to recommend similar movies based on data on movies watched by other users. The recommendation unit may also use content-based filtering technology to recommend similar movies based on movie genres or themes. The guidance unit may use AI to guide users to streaming services or home delivery rental services where they can watch movies recommended by the recommendation unit. The guidance may be provided, for example, through streaming services, rental services, or purchasing services, but is not limited to these examples. For example, the guidance unit may refer to a database of streaming services to identify services where the recommended movie can be watched. The guidance unit may also refer to a database of rental services to identify services where the recommended movie can be rented. The guidance unit may also refer to a database of purchasing services to identify services where the recommended movie can be purchased. This allows the movie recommendation system according to the embodiment to easily find movies that match a user's preferences.Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit may provide guidance using an AI model that receives information about movies recommended by the recommendation unit and outputs available services.

[0030] The reception unit allows the user to input "my favorite movie" or "my favorite genre." For example, the user inputs "my favorite movie" or "my favorite genre." Information input by the user includes, but is not limited to, the movie title, genre, and director's name. The reception unit can accept information in the form of, for example, text input, voice input, or image input. For example, if the user inputs "I like Inception," the reception unit accepts that information. Similarly, if the user inputs "I like action movies," the reception unit can accept that information. This allows the user to input movies that suit their preferences. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the information input by the user to AI, which can then analyze the information.

[0031] The analysis unit can analyze the information received by the reception unit using AI. The analysis unit analyzes the information received by the reception unit using, for example, AI. The analysis can be performed using, for example, natural language processing, image analysis, or audio analysis, but is not limited to these examples. For example, the analysis unit can analyze text information input by a user using natural language processing technology to identify the title and genre of a movie. The analysis unit can also analyze image information input by a user using image analysis technology to identify movie posters and scenes. Furthermore, the analysis unit can analyze audio information input by a user using audio analysis technology to identify the title and genre of a movie. This improves the accuracy of information analysis by using AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information input by a user into AI, and the AI ​​can analyze the information.

[0032] The recommendation unit can recommend similar works using AI based on the information analyzed by the analysis unit. The recommendation unit recommends similar works based on the information analyzed by the analysis unit, for example, using AI. Recommendations can be made using, for example, collaborative filtering, content-based filtering, or other methods, but are not limited to these examples. For example, the recommendation unit can use collaborative filtering technology to recommend similar movies based on data on movies watched by other users. The recommendation unit can also use content-based filtering technology to recommend similar movies based on movie genres or themes. This improves the accuracy of recommending similar works by using AI. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the information analyzed by the analysis unit into AI, which can then recommend similar works.

[0033] The guidance unit can use AI to provide guidance on streaming services or home delivery rental services where the works recommended by the recommendation unit can be viewed. The guidance unit, for example, uses AI to provide guidance on streaming services or home delivery rental services where the works recommended by the recommendation unit can be viewed. The guidance can be provided by, for example, streaming services, rental services, purchase services, or the like, but is not limited to these examples. For example, the guidance unit can refer to a database of streaming services to identify services where the recommended movies can be viewed. The guidance unit can also refer to a database of rental services to identify services where the recommended movies can be rented. Furthermore, the guidance unit can refer to a database of purchase services to identify services where the recommended movies can be purchased. In this way, the use of AI improves the accuracy of guidance on services where the movies can be viewed. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can provide guidance using an AI model that inputs information about the movies recommended by the recommendation unit and outputs services where the movies can be viewed.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can, for example, analyze the user's past input history and suggest the optimal input method. The past input history includes, but is not limited to, text input history, voice input history, image input history, etc. For example, the reception unit can automatically display movies and genres that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest movies and genres that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history into AI, which can then analyze the data and suggest the optimal input method.

[0035] The reception unit can automatically complete input candidates based on the user's current viewing history and areas of interest. The reception unit automatically completes input candidates based on, for example, the user's current viewing history and areas of interest. The viewing history includes, for example, titles of movies viewed, viewing dates and times, and viewing durations, but is not limited to these examples. For example, the reception unit displays related movies as input candidates based on the genre of movies recently viewed by the user. The reception unit can also suggest similar movies as input candidates based on data on movies in which the user has shown interest. Furthermore, the reception unit can automatically complete movies that are likely to be viewed next based on the user's viewing history. This allows input candidates to be automatically completed based on the user's viewing history and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's viewing history and areas of interest into AI, which can then analyze the data to automatically complete input candidates.

[0036] The reception unit can prioritize region-specific movies and genres as input candidates based on the user's geographical location information. The reception unit, for example, prioritizes region-specific movies and genres as input candidates, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, when the user is in a specific region, the reception unit can display region-specific movies and genres as input candidates. The reception unit can also suggest movies related to region-specific film festivals and events based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can also present region-specific movies and genres as input candidates that are popular in the region the user is visiting. This allows region-specific movies and genres to be prioritized as input candidates. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's geographical location information to AI, which can then analyze the data and present region-specific movies and genres as input candidates.

[0037] The reception unit can analyze the user's social media activity and present related movies and genres as input candidates. The reception unit, for example, analyzes the user's social media activity and presents related movies and genres as input candidates. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit displays related movies as input candidates based on movies and genres mentioned by the user on social media. The reception unit can also suggest movies and genres that the user has shown interest in based on the user's social media activity. Furthermore, the reception unit can present related movies as input candidates based on movies watched by the user's followers or friends. In this way, related movies and genres can be presented as input candidates based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can analyze the data and present related movies and genres as input candidates.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history during analysis. The past viewing history includes, for example, the titles of movies viewed, the viewing dates and times, and the viewing durations, but is not limited to these examples. For example, the analysis unit can suggest similar movies based on data on movies the user has previously viewed. The analysis unit can also analyze the user's preferences for specific genres from the user's viewing history and suggest related movies. Furthermore, the analysis unit can analyze the user's viewing history and suggest optimal movies based on the user's viewing habits. Thus, the accuracy of the analysis can be improved by referring to the past viewing history. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's past viewing history into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0039] The analysis unit can apply different analysis methods depending on the category or genre of the movie during analysis. For example, the analysis unit can apply different analysis methods depending on the category or genre of the movie during analysis. Movie categories and genres include, but are not limited to, action, comedy, and drama. For example, in the case of an action movie, the analysis unit can analyze the movie by focusing on the number of action scenes and the tempo. In addition, in the case of a romance movie, the analysis unit can analyze the movie by focusing on emotional scenes and character relationships. Furthermore, in the case of a documentary movie, the analysis unit can analyze the movie by focusing on factuality and accuracy of information. This allows different analysis methods to be applied depending on the category or genre of the movie. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the category or genre of the movie into AI, and the AI ​​can analyze the data and apply different analysis methods.

[0040] The analysis unit may prioritize analyzing region-specific movie data by taking into account the user's geographical location information during analysis. For example, the analysis unit may prioritize analyzing region-specific movie data by taking into account the user's geographical location information during analysis. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific region, the analysis unit may prioritize analyzing movie data that is popular in that region. The analysis unit may also analyze movie data related to region-specific film festivals and events based on the user's geographical location information. Furthermore, if the user is traveling, the analysis unit may prioritize analyzing movie data that is popular in the region the user is visiting. This allows region-specific movie data to be prioritized. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's geographical location information into AI, and the AI ​​may analyze the data to prioritize analyzing region-specific movie data.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the analysis unit analyzes related movie data based on movies and genres mentioned by the user on social media. The analysis unit can also analyze movies and genres of interest from the user's social media activity. Furthermore, the analysis unit can analyze related movies based on movie data watched by the user's followers and friends. This improves the accuracy of the analysis based on social media activity. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the user's social media activity into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0042] The recommendation unit can improve the accuracy of recommendations by referring to the user's past viewing history when making recommendations. For example, the recommendation unit can improve the accuracy of recommendations by referring to the user's past viewing history when making recommendations. The past viewing history includes, for example, the titles of movies viewed, the viewing dates and times, and the viewing durations, but is not limited to these examples. For example, the recommendation unit can recommend similar movies based on data on movies the user has previously viewed. The recommendation unit can also analyze the user's preferences for specific genres from the user's viewing history and recommend related movies. Furthermore, the recommendation unit can analyze the user's viewing history and recommend optimal movies based on the user's viewing habits. This improves the accuracy of recommendations by referring to the past viewing history. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's past viewing history into AI, and the AI ​​can analyze the data to improve the accuracy of recommendations.

[0043] The recommendation unit can apply an appropriate recommendation method depending on the category or genre of a movie when making a recommendation. For example, the recommendation unit can apply an appropriate recommendation method depending on the category or genre of a movie when making a recommendation. Movie categories and genres include, but are not limited to, action, comedy, and drama. For example, in the case of action movies, the recommendation unit can recommend movies that emphasize the number of action scenes and tempo. In addition, in the case of romance movies, the recommendation unit can recommend movies that emphasize emotional scenes and character relationships. Furthermore, in the case of documentary movies, the recommendation unit can recommend movies that emphasize factuality and accuracy of information. This allows the application of an optimal recommendation method depending on the category or genre of a movie. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input data on movie categories and genres into AI, which can analyze the data and apply an appropriate recommendation method.

[0044] The recommendation unit may prioritize region-specific movies based on the user's geographical location information when recommending movies. For example, the recommendation unit may prioritize region-specific movies when recommending movies, taking the user's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific region, the recommendation unit may prioritize recommending movies that are popular in that region. The recommendation unit may also recommend movies related to region-specific film festivals and events based on the user's geographical location information. Furthermore, if the user is traveling, the recommendation unit may prioritize recommending movies that are popular in the region the user is visiting. This allows region-specific movies to be prioritized. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit may input the user's geographical location information into AI, which may analyze the data and prioritize region-specific movies.

[0045] The recommendation unit may improve the accuracy of recommendations by referring to the user's social media activity when making recommendations. For example, the recommendation unit may improve the accuracy of recommendations by referring to the user's social media activity when making recommendations. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the recommendation unit may recommend related movies based on movies and genres mentioned by the user on social media. The recommendation unit may also recommend movies and genres that the user has shown interest in based on the user's social media activity. Furthermore, the recommendation unit may recommend related movies based on movies watched by the user's followers or friends. This improves the accuracy of recommendations based on social media activity. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit may input data on the user's social media activity into AI, which may then analyze the data to improve the accuracy of recommendations.

[0046] The guidance unit can suggest an appropriate streaming service or rental service by referring to the user's past viewing history when providing guidance. For example, the guidance unit can suggest an appropriate streaming service or rental service by referring to the user's past viewing history when providing guidance. The past viewing history may include, but is not limited to, the titles of movies viewed, the viewing dates and times, and the viewing durations of movies viewed. For example, the guidance unit can suggest available services based on streaming services used by the user in the past. The guidance unit can also suggest movies available on specific streaming services based on the user's viewing history. Furthermore, the guidance unit can analyze the user's viewing history and suggest the most frequently used streaming service. This allows the optimal streaming service or rental service to be suggested by referring to the past viewing history. Some or all of the above-described processing by the guidance unit may be performed using, or without, AI. For example, the guidance unit can input the user's past viewing history into AI, which can then analyze the data and suggest appropriate streaming services or rental services.

[0047] The guidance unit can apply an appropriate guidance method depending on the category or genre of the movie when providing guidance. For example, the guidance unit can apply an appropriate guidance method depending on the category or genre of the movie when providing guidance. Movie categories and genres include, but are not limited to, action, comedy, and drama. For example, in the case of an action movie, the guidance unit can provide guidance by emphasizing the number of action scenes and the tempo. In addition, in the case of a romance movie, the guidance unit can provide guidance by emphasizing emotional scenes and character relationships. Furthermore, in the case of a documentary movie, the guidance unit can provide guidance by emphasizing factuality and accuracy of information. This allows the application of an optimal guidance method depending on the category or genre of the movie. Some or all of the above-described processing in the guidance unit can be performed using, for example, AI, or can be performed without using AI. For example, the guidance unit can input data on the category or genre of the movie into AI, and the AI ​​can analyze the data and apply an appropriate guidance method.

[0048] The guidance unit can prioritize region-specific streaming services or rental services based on the user's geographical location information when providing guidance. For example, the guidance unit prioritizes region-specific streaming services or rental services when providing guidance, taking the user's geographical location information into consideration. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, when the user is in a specific region, the guidance unit can recommend streaming services or rental services available in that region. The guidance unit can also recommend streaming services related to region-specific film festivals or events based on the user's geographical location information. Furthermore, when the user is traveling, the guidance unit can also recommend streaming services or rental services available in the region the user is visiting. This allows region-specific streaming services or rental services to be prioritized. Some or all of the above-described processing by the guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the guidance unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to prioritize region-specific streaming services or rental services.

[0049] The guidance unit can improve the accuracy of guidance by referring to the user's social media activity when providing guidance. For example, the guidance unit can improve the accuracy of guidance by referring to the user's social media activity when providing guidance. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the guidance unit can recommend related streaming services and rental services based on movies and genres mentioned by the user on social media. The guidance unit can also recommend movies and genres in which the user has shown interest based on the user's social media activity. Furthermore, the guidance unit can recommend related services based on streaming services used by the user's followers and friends. This improves the accuracy of guidance based on social media activity. Some or all of the above-described processing by the guidance unit can be performed using, for example, AI, or without AI. For example, the guidance unit can input data on the user's social media activity into AI, and the AI ​​can analyze the data to improve the accuracy of guidance.

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

[0051] The reception unit can analyze the user's past viewing history and suggest the optimal input method. For example, movies and genres that the user has frequently input in the past can be automatically displayed as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest movies and genres that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0052] The recommendation unit can prioritize recommending movies and genres specific to a region based on the user's geographic location information. For example, if the user is in a specific region, movies that are popular in that region can be prioritized. Also, based on the user's geographic location information, movies related to film festivals or events specific to the region can be recommended. Furthermore, if the user is traveling, movies that are popular in the region they are visiting can be prioritized.

[0053] The guidance unit can analyze the user's social media activity and present related movies and genres as input candidates. For example, related movies can be displayed as input candidates based on movies and genres mentioned by the user on social media. The guidance unit can also suggest movies and genres that the user has shown interest in based on the user's social media activity. Furthermore, related movies can be presented as input candidates based on movies watched by the user's followers and friends.

[0054] The analysis unit can apply different analysis methods depending on the category or genre of the movie. For example, in the case of an action movie, the analysis can focus on the number of action scenes and the tempo. In the case of a romance movie, the analysis can focus on emotional scenes and character relationships. Furthermore, in the case of a documentary movie, the analysis can focus on factuality and accuracy of information.

[0055] When providing guidance, the guidance unit can suggest appropriate streaming services or rental services by referring to the user's past viewing history. For example, it can suggest available services based on streaming services the user has used in the past. It can also suggest movies available on specific streaming services based on the user's viewing history. It can also analyze the user's viewing history and suggest the most frequently used streaming services.

[0056] The recommendation unit can improve the accuracy of recommendations by referring to the user's social media activity. For example, related movies can be recommended based on movies and genres mentioned by the user on social media. It can also recommend movies and genres that the user has shown interest in based on their social media activity. It can also recommend related movies based on movies watched by the user's followers and friends.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The user inputs "their favorite movie" and "favorite genre" into the reception unit. The information input by the user includes, for example, the movie title, genre, and director's name. The reception unit can accept information in the form of text input, voice input, image input, etc. Step 2: The analysis unit uses AI to analyze the information received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit may use natural language processing technology to analyze the text information entered by the user and identify the movie title and genre. It may also use image analysis technology to analyze the image information entered by the user and identify the movie poster and scenes. It may also use voice analysis technology to analyze the voice information entered by the user and identify the movie title and genre. Step 3: The recommendation unit uses AI to recommend similar movies based on the information analyzed by the analysis unit. Recommendations are made using methods such as collaborative filtering and content-based filtering. For example, the recommendation unit can use collaborative filtering technology to recommend similar movies based on data on movies watched by other users. It can also use content-based filtering technology to recommend similar movies based on movie genres or themes. Step 4: The guidance unit uses AI to provide guidance on streaming services or home delivery rental services where the movies recommended by the recommendation unit can be viewed. Guidance can be provided through streaming services, rental services, purchase services, or other methods. For example, the guidance unit can refer to a database of streaming services to identify services where the recommended movies can be viewed. It can also refer to a database of rental services to identify services where the recommended movies can be rented. It can also refer to a database of purchase services to identify services where the recommended movies can be purchased.

[0059] (Example 2) A movie recommendation system according to an embodiment of the present invention recommends similar movies when a user inputs their favorite movie or genre, and also recommends streaming services and rental services where those movies are available. This movie recommendation system uses AI to analyze the user's input of their favorite movies and genres and recommend similar movies. Furthermore, it provides information on streaming services and rental services where the recommended movies are available. This mechanism allows users to easily find movies that match their preferences. For example, a user may input information such as "I like action movies" or "I like Inception." This information is then input into AI. The AI ​​then analyzes the input information and recommends similar movies. The AI ​​identifies movies that match the user's preferences based on a movie database. For example, if a user inputs "I like Inception," the AI ​​recommends similar movies such as "The Matrix" and "Interstellar." Furthermore, it provides information on streaming services and rental services where the recommended movies are available. The AI ​​references the databases of each streaming service and rental service to identify on which services the recommended movies are available. For example, if "The Matrix" is available on a specific streaming service, the AI ​​provides that information to the user. This mechanism allows users to easily find movies that suit their tastes. For example, if a user enters "I like action movies," the AI ​​will recommend similar works such as "Die Hard" and "Mission: Impossible" and guide them to streaming services or home delivery rental services where those works can be viewed. This makes it easy for users to find movies that suit their tastes. This makes it easy for movie recommendation systems to find movies that suit users' tastes.

[0060] A movie recommendation system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and a guidance unit. The reception unit receives input from a user of "a favorite movie" or "a favorite genre." Information input by the user includes, but is not limited to, the movie title, genre, and director's name. The reception unit can receive information in the form of, for example, text input, voice input, or image input. The analysis unit uses AI to analyze the information received by the reception unit. The analysis can be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to, the examples. For example, the analysis unit can analyze the text information input by the user using natural language processing technology to identify the movie title and genre. The analysis unit can also analyze the image information input by the user using image analysis technology to identify movie posters and scenes. The analysis unit can also analyze the voice information input by the user using voice analysis technology to identify the movie title and genre. The recommendation unit uses AI to recommend similar movies based on the information analyzed by the analysis unit. The recommendation can be performed using, for example, collaborative filtering, content-based filtering, or other methods, but is not limited to, the examples. For example, the recommendation unit may use collaborative filtering technology to recommend similar movies based on data on movies watched by other users. The recommendation unit may also use content-based filtering technology to recommend similar movies based on movie genres or themes. The guidance unit may use AI to guide users to streaming services or home delivery rental services where they can watch movies recommended by the recommendation unit. The guidance may be provided, for example, through streaming services, rental services, or purchasing services, but is not limited to these examples. For example, the guidance unit may refer to a database of streaming services to identify services where the recommended movie can be watched. The guidance unit may also refer to a database of rental services to identify services where the recommended movie can be rented. The guidance unit may also refer to a database of purchasing services to identify services where the recommended movie can be purchased. This allows the movie recommendation system according to the embodiment to easily find movies that match a user's preferences.Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit may provide guidance using an AI model that receives information about movies recommended by the recommendation unit and outputs available services.

[0061] The reception unit allows the user to input "my favorite movie" or "my favorite genre." For example, the user inputs "my favorite movie" or "my favorite genre." Information input by the user includes, but is not limited to, the movie title, genre, and director's name. The reception unit can accept information in the form of, for example, text input, voice input, or image input. For example, if the user inputs "I like Inception," the reception unit accepts that information. Similarly, if the user inputs "I like action movies," the reception unit can accept that information. This allows the user to input movies that suit their preferences. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the information input by the user to AI, which can then analyze the information.

[0062] The analysis unit can analyze the information received by the reception unit using AI. The analysis unit analyzes the information received by the reception unit using, for example, AI. The analysis can be performed using, for example, natural language processing, image analysis, or audio analysis, but is not limited to these examples. For example, the analysis unit can analyze text information input by a user using natural language processing technology to identify the title and genre of a movie. The analysis unit can also analyze image information input by a user using image analysis technology to identify movie posters and scenes. Furthermore, the analysis unit can analyze audio information input by a user using audio analysis technology to identify the title and genre of a movie. This improves the accuracy of information analysis by using AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information input by a user into AI, and the AI ​​can analyze the information.

[0063] The recommendation unit can recommend similar works using AI based on the information analyzed by the analysis unit. The recommendation unit recommends similar works based on the information analyzed by the analysis unit, for example, using AI. Recommendations can be made using, for example, collaborative filtering, content-based filtering, or other methods, but are not limited to these examples. For example, the recommendation unit can use collaborative filtering technology to recommend similar movies based on data on movies watched by other users. The recommendation unit can also use content-based filtering technology to recommend similar movies based on movie genres or themes. This improves the accuracy of recommending similar works by using AI. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the information analyzed by the analysis unit into AI, which can then recommend similar works.

[0064] The guidance unit can use AI to provide guidance on streaming services or home delivery rental services where the works recommended by the recommendation unit can be viewed. The guidance unit, for example, uses AI to provide guidance on streaming services or home delivery rental services where the works recommended by the recommendation unit can be viewed. The guidance can be provided by, for example, streaming services, rental services, purchase services, or the like, but is not limited to these examples. For example, the guidance unit can refer to a database of streaming services to identify services where the recommended movies can be viewed. The guidance unit can also refer to a database of rental services to identify services where the recommended movies can be rented. Furthermore, the guidance unit can refer to a database of purchase services to identify services where the recommended movies can be purchased. In this way, the use of AI improves the accuracy of guidance on services where the movies can be viewed. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can provide guidance using an AI model that inputs information about the movies recommended by the recommendation unit and outputs services where the movies can be viewed.

[0065] The reception unit can estimate a user's emotion and dynamically change the design of the input interface based on the estimated user emotion. The reception unit, for example, estimates a user's emotion and dynamically changes the design of the input interface based on the estimated user emotion. Emotion estimation can be performed using, but is not limited to, methods such as facial expression recognition, voice analysis, and text analysis. For example, the reception unit can use facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The reception unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the reception unit can use text analysis technology to analyze text information entered by the user and estimate the emotion. This allows an interface to be provided that corresponds to the user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is excited, the reception unit can adopt a visually stimulating design to make inputting more enjoyable. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, an AI. For example, the reception unit can input the user's emotion data into the AI, which can then analyze the data and estimate the emotion.

[0066] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can, for example, analyze the user's past input history and suggest the optimal input method. The past input history includes, but is not limited to, text input history, voice input history, image input history, etc. For example, the reception unit can automatically display movies and genres that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest movies and genres that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history into AI, which can then analyze the data and suggest the optimal input method.

[0067] The reception unit can automatically complete input candidates based on the user's current viewing history and areas of interest. The reception unit automatically completes input candidates based on, for example, the user's current viewing history and areas of interest. The viewing history includes, for example, titles of movies viewed, viewing dates and times, and viewing durations, but is not limited to these examples. For example, the reception unit displays related movies as input candidates based on the genre of movies recently viewed by the user. The reception unit can also suggest similar movies as input candidates based on data on movies in which the user has shown interest. Furthermore, the reception unit can automatically complete movies that are likely to be viewed next based on the user's viewing history. This allows input candidates to be automatically completed based on the user's viewing history and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's viewing history and areas of interest into AI, which can then analyze the data to automatically complete input candidates.

[0068] The reception unit can estimate the user's emotion and prioritize inputs based on the estimated user's emotion. The reception unit can, for example, estimate the user's emotion and prioritize inputs based on the estimated user's emotion. Emotion estimation can be performed using, but is not limited to, methods such as facial expression recognition, voice analysis, and text analysis. For example, the reception unit can analyze the user's facial expression and estimate the emotion using facial expression recognition technology. The reception unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the reception unit can analyze text information entered by the user using text analysis technology to estimate the emotion. This allows input prioritization to be determined based on the user's emotion. For example, if the user is in a hurry, the reception unit can prioritize voice input to quickly input movies and genres. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. If the user is stressed, the reception unit can provide a simple interface to minimize input steps. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's emotion data into an AI, which may analyze the data to estimate the emotion and determine the priority of the input.

[0069] The reception unit can prioritize region-specific movies and genres as input candidates based on the user's geographical location information. The reception unit, for example, prioritizes region-specific movies and genres as input candidates, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, when the user is in a specific region, the reception unit can display region-specific movies and genres as input candidates. The reception unit can also suggest movies related to region-specific film festivals and events based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can also present region-specific movies and genres as input candidates that are popular in the region the user is visiting. This allows region-specific movies and genres to be prioritized as input candidates. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's geographical location information to AI, which can then analyze the data and present region-specific movies and genres as input candidates.

[0070] The reception unit can analyze the user's social media activity and present related movies and genres as input candidates. The reception unit, for example, analyzes the user's social media activity and presents related movies and genres as input candidates. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit displays related movies as input candidates based on movies and genres mentioned by the user on social media. The reception unit can also suggest movies and genres that the user has shown interest in based on the user's social media activity. Furthermore, the reception unit can present related movies as input candidates based on movies watched by the user's followers or friends. In this way, related movies and genres can be presented as input candidates based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can analyze the data and present related movies and genres as input candidates.

[0071] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and dynamically adjusts the analysis algorithm based on the estimated user emotions. Emotion estimation can be performed using, but is not limited to, methods such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can use facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the analysis unit can use text analysis technology to analyze text information entered by the user and estimate the emotions. This allows the analysis algorithm to be dynamically adjusted according to the user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest movies from a wide range of genres. If the user is in a hurry, the analysis unit can perform a quick analysis and prioritize suggesting the most relevant movies. Furthermore, if the user is excited, the analysis unit can prioritize suggesting visually stimulating movies. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input user emotion data into AI, which analyzes the data to estimate emotions and dynamically adjust the analysis algorithm.

[0072] The analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history during analysis. The past viewing history includes, for example, the titles of movies viewed, the viewing dates and times, and the viewing durations, but is not limited to these examples. For example, the analysis unit can suggest similar movies based on data on movies the user has previously viewed. The analysis unit can also analyze the user's preferences for specific genres from the user's viewing history and suggest related movies. Furthermore, the analysis unit can analyze the user's viewing history and suggest optimal movies based on the user's viewing habits. Thus, the accuracy of the analysis can be improved by referring to the past viewing history. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's past viewing history into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0073] The analysis unit can apply different analysis methods depending on the category or genre of the movie during analysis. For example, the analysis unit can apply different analysis methods depending on the category or genre of the movie during analysis. Movie categories and genres include, but are not limited to, action, comedy, and drama. For example, in the case of an action movie, the analysis unit can analyze the movie by focusing on the number of action scenes and the tempo. In addition, in the case of a romance movie, the analysis unit can analyze the movie by focusing on emotional scenes and character relationships. Furthermore, in the case of a documentary movie, the analysis unit can analyze the movie by focusing on factuality and accuracy of information. This allows different analysis methods to be applied depending on the category or genre of the movie. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the category or genre of the movie into AI, and the AI ​​can analyze the data and apply different analysis methods.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can, for example, estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but is not limited to these examples. For example, the analysis unit can use facial expression recognition technology to analyze the user's facial expressions and estimate emotions. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate emotions. Furthermore, the analysis unit can use text analysis technology to analyze text information entered by the user and estimate emotions. This allows the display method of the analysis results to be adjusted according to the user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the AI, which may analyze the data to estimate the emotion and adjust the display method of the analysis results.

[0075] The analysis unit may prioritize analyzing region-specific movie data by taking into account the user's geographical location information during analysis. For example, the analysis unit may prioritize analyzing region-specific movie data by taking into account the user's geographical location information during analysis. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific region, the analysis unit may prioritize analyzing movie data that is popular in that region. The analysis unit may also analyze movie data related to region-specific film festivals and events based on the user's geographical location information. Furthermore, if the user is traveling, the analysis unit may prioritize analyzing movie data that is popular in the region the user is visiting. This allows region-specific movie data to be prioritized. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's geographical location information into AI, and the AI ​​may analyze the data to prioritize analyzing region-specific movie data.

[0076] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the analysis unit analyzes related movie data based on movies and genres mentioned by the user on social media. The analysis unit can also analyze movies and genres of interest from the user's social media activity. Furthermore, the analysis unit can analyze related movies based on movie data watched by the user's followers and friends. This improves the accuracy of the analysis based on social media activity. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the user's social media activity into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0077] The recommendation unit can estimate a user's emotions and dynamically adjust the recommendation algorithm based on the estimated user emotions. The recommendation unit can, for example, estimate a user's emotions and dynamically adjust the recommendation algorithm based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but is not limited to these examples. For example, the recommendation unit can use facial expression recognition technology to analyze a user's facial expressions and estimate emotions. The recommendation unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate emotions. Furthermore, the recommendation unit can use text analysis technology to analyze text information entered by the user and estimate emotions. This allows the recommendation algorithm to be dynamically adjusted according to the user's emotions. For example, if the user is relaxed, the recommendation unit can recommend movies from a wide range of genres. If the user is in a hurry, the recommendation unit can prioritize recommending the most relevant movies. Furthermore, if the user is excited, the recommendation unit can prioritize recommending visually stimulating movies. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, or without, an AI. For example, the recommendation unit may input user emotion data into an AI, which may analyze the data to estimate the emotion and dynamically adjust the recommendation algorithm.

[0078] The recommendation unit can improve the accuracy of recommendations by referring to the user's past viewing history when making recommendations. For example, the recommendation unit can improve the accuracy of recommendations by referring to the user's past viewing history when making recommendations. The past viewing history includes, for example, the titles of movies viewed, the viewing dates and times, and the viewing durations, but is not limited to these examples. For example, the recommendation unit can recommend similar movies based on data on movies the user has previously viewed. The recommendation unit can also analyze the user's preferences for specific genres from the user's viewing history and recommend related movies. Furthermore, the recommendation unit can analyze the user's viewing history and recommend optimal movies based on the user's viewing habits. This improves the accuracy of recommendations by referring to the past viewing history. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's past viewing history into AI, and the AI ​​can analyze the data to improve the accuracy of recommendations.

[0079] The recommendation unit can apply an appropriate recommendation method depending on the category or genre of a movie when making a recommendation. For example, the recommendation unit can apply an appropriate recommendation method depending on the category or genre of a movie when making a recommendation. Movie categories and genres include, but are not limited to, action, comedy, and drama. For example, in the case of action movies, the recommendation unit can recommend movies that emphasize the number of action scenes and tempo. In addition, in the case of romance movies, the recommendation unit can recommend movies that emphasize emotional scenes and character relationships. Furthermore, in the case of documentary movies, the recommendation unit can recommend movies that emphasize factuality and accuracy of information. This allows the application of an optimal recommendation method depending on the category or genre of a movie. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input data on movie categories and genres into AI, which can analyze the data and apply an appropriate recommendation method.

[0080] The recommendation unit can estimate the user's emotions and adjust the display method of the recommended results based on the estimated user emotions. The recommendation unit can, for example, estimate the user's emotions and adjust the display method of the recommended results based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but is not limited to these examples. For example, the recommendation unit can use facial expression recognition technology to analyze the user's facial expressions and estimate their emotions. The recommendation unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, the recommendation unit can use text analysis technology to analyze text information entered by the user and estimate their emotions. This allows the display method of the recommended results to be adjusted according to the user's emotions. For example, if the user is nervous, the recommendation unit can provide a simple, highly visible display method. If the user is relaxed, the recommendation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the recommendation unit can provide a display method that focuses on the main points. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, or without, an AI. For example, the recommendation unit may input user emotion data into an AI, which may analyze the data to estimate the emotion and adjust the display method of the recommendation results.

[0081] The recommendation unit may prioritize region-specific movies based on the user's geographical location information when recommending movies. For example, the recommendation unit may prioritize region-specific movies when recommending movies, taking the user's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific region, the recommendation unit may prioritize recommending movies that are popular in that region. The recommendation unit may also recommend movies related to region-specific film festivals and events based on the user's geographical location information. Furthermore, if the user is traveling, the recommendation unit may prioritize recommending movies that are popular in the region the user is visiting. This allows region-specific movies to be prioritized. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit may input the user's geographical location information into AI, which may analyze the data and prioritize region-specific movies.

[0082] The recommendation unit may improve the accuracy of recommendations by referring to the user's social media activity when making recommendations. For example, the recommendation unit may improve the accuracy of recommendations by referring to the user's social media activity when making recommendations. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the recommendation unit may recommend related movies based on movies and genres mentioned by the user on social media. The recommendation unit may also recommend movies and genres that the user has shown interest in based on the user's social media activity. Furthermore, the recommendation unit may recommend related movies based on movies watched by the user's followers or friends. This improves the accuracy of recommendations based on social media activity. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit may input data on the user's social media activity into AI, which may then analyze the data to improve the accuracy of recommendations.

[0083] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user emotions. The guidance unit can, for example, estimate the user's emotions and adjust the way the guidance is presented based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but is not limited to these examples. For example, the guidance unit can use facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The guidance unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the guidance unit can use text analysis technology to analyze text information entered by the user and estimate the emotions. This allows the way the guidance is presented to be adjusted depending on the user's emotions. For example, if the user is nervous, the guidance unit can provide guidance in a calm tone. If the user is relaxed, the guidance unit can provide guidance in a bright tone. Furthermore, if the user is in a hurry, the guidance unit can provide quick and concise guidance. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using, or without, AI. For example, the guidance unit may input the user's emotional data into AI, which may analyze the data to estimate the emotion and adjust the way the guidance is presented.

[0084] The guidance unit can suggest an appropriate streaming service or rental service by referring to the user's past viewing history when providing guidance. For example, the guidance unit can suggest an appropriate streaming service or rental service by referring to the user's past viewing history when providing guidance. The past viewing history may include, but is not limited to, the titles of movies viewed, the viewing dates and times, and the viewing durations of movies viewed. For example, the guidance unit can suggest available services based on streaming services used by the user in the past. The guidance unit can also suggest movies available on specific streaming services based on the user's viewing history. Furthermore, the guidance unit can analyze the user's viewing history and suggest the most frequently used streaming service. This allows the optimal streaming service or rental service to be suggested by referring to the past viewing history. Some or all of the above-described processing by the guidance unit may be performed using, or without, AI. For example, the guidance unit can input the user's past viewing history into AI, which can then analyze the data and suggest appropriate streaming services or rental services.

[0085] The guidance unit can apply an appropriate guidance method depending on the category or genre of the movie when providing guidance. For example, the guidance unit can apply an appropriate guidance method depending on the category or genre of the movie when providing guidance. Movie categories and genres include, but are not limited to, action, comedy, and drama. For example, in the case of an action movie, the guidance unit can provide guidance by emphasizing the number of action scenes and the tempo. In addition, in the case of a romance movie, the guidance unit can provide guidance by emphasizing emotional scenes and character relationships. Furthermore, in the case of a documentary movie, the guidance unit can provide guidance by emphasizing factuality and accuracy of information. This allows the application of an optimal guidance method depending on the category or genre of the movie. Some or all of the above-described processing in the guidance unit can be performed using, for example, AI, or can be performed without using AI. For example, the guidance unit can input data on the category or genre of the movie into AI, and the AI ​​can analyze the data and apply an appropriate guidance method.

[0086] The guidance unit can estimate the user's emotions and prioritize guidance based on the estimated user emotions. The guidance unit can estimate the user's emotions and prioritize guidance based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but is not limited to these examples. For example, the guidance unit can use facial expression recognition technology to analyze the user's facial expressions and estimate the user's emotions. The guidance unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the user's emotions. Furthermore, the guidance unit can use text analysis technology to analyze text information entered by the user and estimate the user's emotions. This allows guidance prioritization to be determined based on the user's emotions. For example, if the user is in a hurry, the guidance unit can prioritize the most relevant streaming services or rental services. If the user is relaxed, the guidance unit can provide guidance with detailed information. Furthermore, if the user is stressed, the guidance unit can provide simple and intuitive guidance. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using, or without, an AI. For example, the guidance unit may input the user's emotional data into an AI, which may analyze the data to estimate the emotion and determine the priority of guidance.

[0087] The guidance unit can prioritize region-specific streaming services or rental services based on the user's geographical location information when providing guidance. For example, the guidance unit prioritizes region-specific streaming services or rental services when providing guidance, taking the user's geographical location information into consideration. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, when the user is in a specific region, the guidance unit can recommend streaming services or rental services available in that region. The guidance unit can also recommend streaming services related to region-specific film festivals or events based on the user's geographical location information. Furthermore, when the user is traveling, the guidance unit can also recommend streaming services or rental services available in the region the user is visiting. This allows region-specific streaming services or rental services to be prioritized. Some or all of the above-described processing by the guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the guidance unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to prioritize region-specific streaming services or rental services.

[0088] The guidance unit can improve the accuracy of guidance by referring to the user's social media activity when providing guidance. For example, the guidance unit can improve the accuracy of guidance by referring to the user's social media activity when providing guidance. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the guidance unit can recommend related streaming services and rental services based on movies and genres mentioned by the user on social media. The guidance unit can also recommend movies and genres in which the user has shown interest based on the user's social media activity. Furthermore, the guidance unit can recommend related services based on streaming services used by the user's followers and friends. This improves the accuracy of guidance based on social media activity. Some or all of the above-described processing by the guidance unit can be performed using, for example, AI, or without AI. For example, the guidance unit can input data on the user's social media activity into AI, and the AI ​​can analyze the data to improve the accuracy of guidance. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and guidance unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows the user to input text, voice, or image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends similar works based on the analyzed information. The guidance unit is realized, for example, by the output device 40 of the smart device 14, and introduces streaming services or home delivery rental services where the recommended works can be viewed. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input voice information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends similar works based on the analyzed information. The guidance unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides information on streaming services or home delivery rental services where the recommended works can be viewed. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and guidance unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input voice information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends similar works based on the analyzed information. The guidance unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides information about streaming services and home delivery rental services where the recommended works can be viewed. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and guidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input voice information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends similar works based on the analyzed information. The guidance unit is realized, for example, by the speaker 240 of the robot 414, and introduces streaming services or home delivery rental services where the recommended works can be viewed.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The reception unit can analyze the user's past viewing history and suggest the optimal input method. For example, movies and genres that the user has frequently input in the past can be automatically displayed as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest movies and genres that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0091] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest movies from a wide range of genres. If the user is in a hurry, the analysis unit can quickly perform an analysis and prioritize suggesting the most relevant movies. Furthermore, if the user is excited, the analysis unit can prioritize suggesting visually stimulating movies.

[0092] The recommendation unit can prioritize recommending movies and genres specific to a region based on the user's geographic location information. For example, if the user is in a specific region, movies that are popular in that region can be prioritized. Also, based on the user's geographic location information, movies related to film festivals or events specific to the region can be recommended. Furthermore, if the user is traveling, movies that are popular in the region they are visiting can be prioritized.

[0093] The guidance unit can analyze the user's social media activity and present related movies and genres as input candidates. For example, related movies can be displayed as input candidates based on movies and genres mentioned by the user on social media. The guidance unit can also suggest movies and genres that the user has shown interest in based on the user's social media activity. Furthermore, related movies can be presented as input candidates based on movies watched by the user's followers and friends.

[0094] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface to minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is excited, the reception unit can adopt a visually stimulating design to make input tasks more enjoyable.

[0095] The analysis unit can apply different analysis methods depending on the category or genre of the movie. For example, in the case of an action movie, the analysis can focus on the number of action scenes and the tempo. In the case of a romance movie, the analysis can focus on emotional scenes and character relationships. Furthermore, in the case of a documentary movie, the analysis can focus on factuality and accuracy of information.

[0096] The recommendation unit can estimate the user's emotions and dynamically adjust the recommendation algorithm based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can recommend movies across a wide range of genres. If the user is in a hurry, the recommendation unit can prioritize recommending the most relevant movies. Furthermore, if the user is excited, the recommendation unit can prioritize recommending visually stimulating movies.

[0097] When providing guidance, the guidance unit can suggest appropriate streaming services or rental services by referring to the user's past viewing history. For example, it can suggest available services based on streaming services the user has used in the past. It can also suggest movies available on specific streaming services based on the user's viewing history. It can also analyze the user's viewing history and suggest the most frequently used streaming services.

[0098] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, if the user is nervous, the guidance unit can provide guidance in a calm tone. If the user is relaxed, the guidance unit can provide guidance in a bright tone. Furthermore, if the user is in a hurry, the guidance unit can provide quick and concise guidance.

[0099] The recommendation unit can improve the accuracy of recommendations by referring to the user's social media activity. For example, related movies can be recommended based on movies and genres mentioned by the user on social media. It can also recommend movies and genres that the user has shown interest in based on their social media activity. It can also recommend related movies based on movies watched by the user's followers and friends.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The user inputs "their favorite movie" and "favorite genre" into the reception unit. The information input by the user includes, for example, the movie title, genre, and director's name. The reception unit can accept information in the form of text input, voice input, image input, etc. Step 2: The analysis unit uses AI to analyze the information received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit may use natural language processing technology to analyze the text information entered by the user and identify the movie title and genre. It may also use image analysis technology to analyze the image information entered by the user and identify the movie poster and scenes. It may also use voice analysis technology to analyze the voice information entered by the user and identify the movie title and genre. Step 3: The recommendation unit uses AI to recommend similar movies based on the information analyzed by the analysis unit. Recommendations are made using methods such as collaborative filtering and content-based filtering. For example, the recommendation unit can use collaborative filtering technology to recommend similar movies based on data on movies watched by other users. It can also use content-based filtering technology to recommend similar movies based on movie genres or themes. Step 4: The guidance unit uses AI to provide guidance on streaming services or home delivery rental services where the movies recommended by the recommendation unit can be viewed. Guidance can be provided through streaming services, rental services, purchase services, or other methods. For example, the guidance unit can refer to a database of streaming services to identify services where the recommended movies can be viewed. It can also refer to a database of rental services to identify services where the recommended movies can be rented. It can also refer to a database of purchase services to identify services where the recommended movies can be purchased.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[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 audio according to instructions from the processor 46.

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

[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 carried out in a secure state.

[0129] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[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 audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] 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.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 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 reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a recommendation unit that recommends similar works based on the information analyzed by the analysis unit; a guide unit that guides users to services where the works recommended by the recommendation unit can be viewed; A system characterized by:

2. The reception unit The user inputs their favorite movie or favorite genre.

2. The system of claim 1.

3. The analysis unit The information received by the reception unit is analyzed by AI.

2. The system of claim 1.

4. The recommendation unit Using AI, similar works are recommended based on the information analyzed by the analysis unit.

2. The system of claim 1.

5. The guide unit is Using AI, the system guides users to streaming services or home delivery rental services where they can watch the works recommended by the recommendation unit.

2. The system of claim 1.

6. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

8. The reception unit Auto-complete suggestions based on the user's current viewing history and interests 2. The system of claim 1.

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