System

Abstract

An object of a system according to an embodiment is to extract an element in content and propose other related content.SOLUTION: A system according to an embodiment includes a content analysis unit, an element extraction unit, and a proposal unit. The content analysis unit analyzes content. The element extraction unit extracts at least one element of quotation of another work, music, and art from the content analyzed by the content analysis unit. The proposal unit proposes another related content based on the element extracted by the element extraction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to efficiently extract elements such as quotes, music, and art from content and suggest other related content.

[0005] The system according to the embodiment aims to extract elements within content and suggest other related content. [Means for solving the problem]

[0006] A system according to an embodiment includes a content analysis unit, an element extraction unit, and a suggestion unit. The content analysis unit analyzes content. The element extraction unit extracts at least one element of quotations from other works or music or art from the content analyzed by the content analysis unit. The suggestion unit suggests other related content based on the elements extracted by the element extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can extract elements within the content and suggest other related content. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The content recommendation system according to the embodiment of the present invention utilizes AI technology to enable users to enjoy discovering, connecting with, and learning from other content through content. As a result, the content recommendation system allows users to naturally encounter other content while enjoying one piece of content.

[0029] A content recommendation system according to an embodiment includes a content analysis unit, an element extraction unit, and a suggestion unit. The content analysis unit analyzes content. For example, the content analysis unit analyzes entertainment content such as novels and movies to detect elements such as quotations from other works, music, and art contained therein. The element extraction unit extracts at least one element of quotations from other works, music, or art from the content analyzed by the content analysis unit. For example, the element extraction unit detects and lists quotations from specific musical works or movies that appear in a novel. The suggestion unit suggests other related content based on the elements extracted by the element extraction unit. For example, the suggestion unit presents movies and musical works quoted in a novel to a user and provides detailed information and viewing links for the movies and music works. In this way, the content recommendation system according to an embodiment allows a user to naturally encounter other content while enjoying one piece of content.

[0030] The element extraction unit can detect quotations from specific musical works or movies and list them. For example, the element extraction unit can detect quotations from specific musical works or movies that appear in a novel and list them. For example, the element extraction unit can analyze the text of a novel and detect quotations from specific musical works or movies. The element extraction unit can also list the detected quotations from musical works or movies and provide it to the user. This allows a list of quotations from related musical works or movies to be provided to the user as they read the novel.

[0031] The suggestion unit can present movies or musical works to the user and provide detailed information and viewing links for them. For example, the suggestion unit can present movies or musical works cited in a novel to the user and provide detailed information and viewing links for them. For example, the suggestion unit can provide the title, director, and cast information of the movie and present a viewing link. The suggestion unit can also provide the artist name and album information of the musical work and present a viewing link. This allows the user to be provided with detailed information and viewing links for related movies and musical works while reading the novel.

[0032] The suggestion unit can provide explanations about historical events or people to help the user understand their background. The suggestion unit can provide explanations about historical events or people cited in a novel, for example, to help the user understand their background. For example, the suggestion unit can provide explanations about the historical background and cultural background of the historical event to the user. The suggestion unit can also provide explanations about the life and achievements of the historical person to the user. This can provide explanations about related historical events and people as the user reads a novel, helping the user understand the background.

[0033] The suggestion unit can automatically display other content and learning information related to a novel when the user inputs the title of the novel they are reading. For example, when the user inputs the title of the novel they are reading, the suggestion unit can automatically display other content and learning information related to the novel. For example, the suggestion unit can automatically display related movies and music works based on the title of the novel. The suggestion unit can also automatically display related academic papers and articles based on the title of the novel. In this way, other related content and learning information can be automatically displayed while the user is reading the novel.

[0034] If a user likes novels of a specific genre, the suggestion unit can preferentially suggest other works and learning information related to that genre. For example, if a user likes novels of a specific genre, the suggestion unit preferentially suggests other works and learning information related to that genre. For example, the suggestion unit analyzes the user's past browsing history and preferentially suggests other novels related to a specific genre. The suggestion unit also preferentially suggests academic papers and articles related to a specific genre. In this way, if a user likes novels of a specific genre, it can preferentially suggest other works and learning information related to that genre.

[0035] The content analysis unit can analyze the relationships between characters or the patterns of their dialogue to clarify the structure of the story. The content analysis unit, for example, analyzes the text of a novel to extract the relationships between characters. For example, it identifies intimate relationships and conflicting relationships based on the frequency and content of dialogue. The content analysis unit also analyzes dialogue patterns to clarify the structure of the story. For example, it identifies the progression and climax of the story based on the flow and content of the dialogue. In this way, it is possible to analyze the relationships between characters and the patterns of dialogue to clarify the structure of the story.

[0036] The content analysis unit can expand the content analysis target to include not only text but also multimedia content such as images, audio, and video. The content analysis unit includes not only the text of novels and movies, but also image and audio data as its analysis target. For example, it analyzes movie scene images and audio tracks to extract related elements. The content analysis unit also analyzes video data to extract related elements. For example, it analyzes video scenes and audio to extract related elements. This allows the content analysis target to be expanded to include not only text but also multimedia content such as images, audio, and video.

[0037] The content analysis unit can analyze content in different languages ​​and extract elements that take cultural differences into consideration. For example, the content analysis unit analyzes the text of novels or movies written in different languages ​​and extracts elements that take cultural differences into consideration. For example, it identifies expressions and quotations that are unique to a particular culture. The content analysis unit also analyzes content in different languages ​​and extracts elements that take cultural backgrounds and customs into consideration. For example, it identifies historical events and people in different cultures. This makes it possible to analyze content in different languages ​​and extract elements that take cultural differences into consideration.

[0038] The suggestion unit can classify the extracted elements based on the theme or message of the story and suggest related content. For example, the suggestion unit classifies quotations or musical pieces extracted from a novel based on the theme of the story. For example, elements with themes of love or friendship are collectively suggested. The suggestion unit can also classify the extracted elements based on the message of the story and suggest related content. For example, elements containing lessons or warnings are collectively suggested. In this way, the extracted elements can be classified based on the theme or message of the story and suggest related content.

[0039] The suggestion unit can connect the extracted elements across different genres or media formats. For example, the suggestion unit can connect an extracted quote or piece of music from a novel with content from a different genre. For example, the suggestion unit can suggest movies or music based on a quote from a novel. The suggestion unit can also connect the extracted elements across different media formats. For example, the suggestion unit can suggest artwork or digital content based on a quote from a novel. This allows the extracted elements to be connected across different genres and media formats.

[0040] The suggestion unit can connect the extracted elements with educational content or academic materials to enhance learning depth. For example, the suggestion unit can connect an extracted quote from a novel or a musical piece with educational content, such as providing commentary on a historical event or person based on the quote from the novel. The suggestion unit can also connect the extracted elements with academic materials to enhance learning depth, such as providing a research paper on a particular historical event. This allows the extracted elements to be connected with educational content or academic materials to enhance learning depth.

[0041] The suggestion unit can suggest related academic papers or research materials that may be of interest to the user based on the extracted elements. For example, the suggestion unit can suggest academic papers about historical events or people cited in a novel. For example, the suggestion unit can provide research papers on specific historical events. The suggestion unit can also suggest related research materials that may be of interest to the user based on the extracted elements. For example, the suggestion unit can provide datasets or experimental results on a specific topic. This makes it possible to suggest related academic papers or research materials that may be of interest to the user based on the extracted elements.

[0042] The suggestion unit can provide interactive content that provides detailed explanations of the historical or cultural background related to the extracted elements. For example, the suggestion unit can provide detailed explanations of historical events or people cited in a novel. For example, the suggestion unit can explain the historical background using an interactive timeline or map. The suggestion unit can also provide interactive content that provides detailed explanations of the cultural background related to the extracted elements. For example, the suggestion unit can provide interactive content that explains the customs and values ​​of a particular culture. This makes it possible to provide interactive content that provides detailed explanations of the historical or cultural background related to the extracted elements.

[0043] The suggestion unit can provide information from different academic fields or related information to promote learning. For example, the suggestion unit can suggest related scientific concepts or theories based on literary elements cited in a novel. For example, the suggestion unit can provide explanations of scientific theories that appear in literary works. The suggestion unit can also provide related information across different academic fields. For example, the suggestion unit can provide information related to different academic fields, such as from literature to science or science to history. This makes it possible to provide related information across different academic fields to promote learning.

[0044] The suggestion unit may provide online discussions or forums in which users can participate to promote learning. For example, the suggestion unit may provide online discussions about elements quoted in a novel. For example, the suggestion unit may open a discussion forum about a particular literary work or historical event. The suggestion unit may also provide online discussions or forums in which users can participate. For example, the suggestion unit may provide a forum in which users can exchange opinions through chat rooms or video conferencing. In this way, online discussions or forums in which users can participate to promote learning may be provided.

[0045] The suggestion unit can introduce an interactive map or timeline into the user interface and visually display related content. The suggestion unit, for example, displays elements quoted in a novel using an interactive map or timeline. For example, it visually displays related elements along the progression of the story. The suggestion unit also visually displays related content using an interactive map or timeline. For example, it visually displays elements related to a specific place or time. In this way, it is possible to introduce an interactive map or timeline into the user interface and visually display related content.

[0046] The suggestion unit can adapt the user interface to at least one of different devices (smartphone, tablet, PC) to enable seamless use. For example, when a user is reading a novel on a smartphone, the suggestion unit provides a function that allows the same content to be seamlessly displayed on a tablet or PC. For example, the suggestion unit synchronizes data between devices. The suggestion unit also provides a user interface that is adapted to different devices. For example, it provides interfaces optimized for smartphones, tablets, and PCs. This allows the user interface to be adapted to different devices to enable seamless use.

[0047] The suggestion unit can add a social media integration function to the user interface, making it easier for users to share content. For example, the suggestion unit provides a function that allows users to share elements quoted in a novel on social media. For example, a specific quote or piece of music can be shared on an SNS. The suggestion unit also uses the social media integration function to make it easier for users to share content. For example, it provides a function that allows users to post to an SNS with one click. This adds a social media integration function to the user interface, making it easier for users to share content.

[0048] The suggestion unit can analyze the user's browsing history and recommend content related to a specific theme or topic by digging deeper. For example, the suggestion unit can analyze the user's past browsing history and recommend novels related to a specific theme or topic by digging deeper. For example, for a user who likes novels with many historical themes, other novels that also deal with historical themes are recommended. The suggestion unit can also analyze the user's browsing history and recommend content related to a specific theme or topic by digging deeper. For example, content related to a specific technology or culture is recommended. In this way, the suggestion unit can analyze the user's browsing history and recommend content related to a specific theme or topic by digging deeper.

[0049] The suggestion unit can recommend a series or sequel of related content based on the user's interests. For example, the suggestion unit analyzes the user's past browsing history and recommends a series or sequel of related novels based on the user's interests. For example, for a user who likes a particular series of novels, the suggestion unit recommends sequels to that series. The suggestion unit also recommends a series or sequel of related content based on the user's interests. For example, the suggestion unit recommends a series or sequel related to a particular theme or genre. This makes it possible to recommend a series or sequel of related content based on the user's interests.

[0050] The suggestion unit can recommend content of different genres or media formats based on the user's interests. For example, the suggestion unit analyzes the user's past browsing history and recommends novels of different genres based on the user's interests. For example, for a user who likes fantasy novels, novels of other genres that also contain fantasy elements are recommended. The suggestion unit also recommends content of different media formats based on the user's interests. For example, the suggestion unit recommends not only novels but also related content such as movies and music. This makes it possible to recommend content of different genres and media formats based on the user's interests.

[0051] The suggestion unit can provide information on related events or workshops based on the user's interests. The suggestion unit, for example, analyzes the user's past browsing history and provides information on related events or workshops based on the user's interests. For example, it recommends events or workshops related to a specific novel or movie. The suggestion unit also provides information on related events or workshops based on the user's interests. For example, it recommends events or workshops related to a specific theme or genre. This makes it possible to provide information on related events or workshops based on the user's interests.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The suggestion unit can analyze a user's past browsing history and recommend content related to specific themes or topics by digging deeper. For example, if a user has read many historical novels in the past, the suggestion unit can recommend other novels that also deal with historical themes. Also, if a user is interested in a particular technology or culture, the suggestion unit can suggest academic papers and articles related to that topic. This allows for deeper learning and new discoveries based on the user's interests.

[0054] If a user likes novels of a particular genre, the suggestion unit can preferentially suggest other works and learning information related to that genre. For example, if a user likes mystery novels, the suggestion unit can suggest other novels and movies that also contain mystery elements. The suggestion unit can also preferentially suggest academic papers and articles related to a particular genre. This allows a user to naturally experience other related works and learning information while enjoying novels of a particular genre.

[0055] The suggestion unit can automatically display other content and learning information related to a novel when the user enters the title of the novel they are reading. For example, when a user enters the title of a particular novel, movies and music works related to that novel are automatically displayed. Academic papers and articles related to the novel can also be automatically displayed. This allows the user to easily access other related content and learning information while reading a novel.

[0056] The suggestion unit can provide information on related events or workshops based on the user's interests. For example, it can analyze the user's past browsing history and recommend events or workshops related to a specific novel or movie. It can also provide information on events or workshops related to a specific theme or genre. This allows the user to receive information on related events and workshops based on their interests, expanding learning opportunities.

[0057] The suggestion unit can introduce an interactive map or timeline into the user interface to visually display related content. For example, elements quoted in a novel can be displayed on an interactive map or timeline to visually display related elements as the story progresses. Elements related to a specific place or time can also be visually displayed. This allows the suggestion unit to introduce an interactive map or timeline into the user interface to visually display related content.

[0058] The suggestion unit can add a social media integration function to the user interface, making it easier for users to share content. For example, a function can be provided that allows users to share elements quoted in a novel on social media. A specific quote or piece of music can be shared on a social networking site. A function can also be provided that allows users to post to a social networking site with one click. This adds a social media integration function to the user interface, making it easier for users to share content.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The content analysis unit analyzes the content. For example, it analyzes entertainment content such as novels and movies to detect citations from other works, music, art, and other elements contained within. Step 2: The element extraction unit extracts at least one element of quotations from other works, music, or art from the content analyzed by the content analysis unit. For example, it detects quotations from specific musical works or movies that appear in a novel and creates a list of them. Step 3: The suggestion unit suggests other related content based on the elements extracted by the element extraction unit. For example, it may present the user with movies or music pieces cited in the novel, along with detailed information and links to watch them.

[0061] (Example 2) The content recommendation system according to the embodiment of the present invention utilizes AI technology to enable users to enjoy discovering, connecting with, and learning from other content through content. As a result, the content recommendation system allows users to naturally encounter other content while enjoying one piece of content.

[0062] A content recommendation system according to an embodiment includes a content analysis unit, an element extraction unit, and a suggestion unit. The content analysis unit analyzes content. For example, the content analysis unit analyzes entertainment content such as novels and movies to detect elements such as quotations from other works, music, and art contained therein. The element extraction unit extracts at least one element of quotations from other works, music, or art from the content analyzed by the content analysis unit. For example, the element extraction unit detects and lists quotations from specific musical works or movies that appear in a novel. The suggestion unit suggests other related content based on the elements extracted by the element extraction unit. For example, the suggestion unit presents movies and musical works quoted in a novel to a user and provides detailed information and viewing links for the movies and music works. In this way, the content recommendation system according to an embodiment allows a user to naturally encounter other content while enjoying one piece of content.

[0063] The element extraction unit can detect quotations from specific musical works or movies and list them. For example, the element extraction unit can detect quotations from specific musical works or movies that appear in a novel and list them. For example, the element extraction unit can analyze the text of a novel and detect quotations from specific musical works or movies. The element extraction unit can also list the detected quotations from musical works or movies and provide it to the user. This allows a list of quotations from related musical works or movies to be provided to the user as they read the novel.

[0064] The suggestion unit can present movies or musical works to the user and provide detailed information and viewing links for them. For example, the suggestion unit can present movies or musical works cited in a novel to the user and provide detailed information and viewing links for them. For example, the suggestion unit can provide the title, director, and cast information of the movie and present a viewing link. The suggestion unit can also provide the artist name and album information of the musical work and present a viewing link. This allows the user to be provided with detailed information and viewing links for related movies and musical works while reading the novel.

[0065] The suggestion unit can provide explanations about historical events or people to help the user understand their background. The suggestion unit can provide explanations about historical events or people cited in a novel, for example, to help the user understand their background. For example, the suggestion unit can provide explanations about the historical background and cultural background of the historical event to the user. The suggestion unit can also provide explanations about the life and achievements of the historical person to the user. This can provide explanations about related historical events and people as the user reads a novel, helping the user understand the background.

[0066] The suggestion unit can automatically display other content and learning information related to a novel when the user inputs the title of the novel they are reading. For example, when the user inputs the title of the novel they are reading, the suggestion unit can automatically display other content and learning information related to the novel. For example, the suggestion unit can automatically display related movies and music works based on the title of the novel. The suggestion unit can also automatically display related academic papers and articles based on the title of the novel. In this way, other related content and learning information can be automatically displayed while the user is reading the novel.

[0067] If a user likes novels of a specific genre, the suggestion unit can preferentially suggest other works and learning information related to that genre. For example, if a user likes novels of a specific genre, the suggestion unit preferentially suggests other works and learning information related to that genre. For example, the suggestion unit analyzes the user's past browsing history and preferentially suggests other novels related to a specific genre. The suggestion unit also preferentially suggests academic papers and articles related to a specific genre. In this way, if a user likes novels of a specific genre, it can preferentially suggest other works and learning information related to that genre.

[0068] The content analysis unit can analyze emotional tone or mood and extract emotionally significant elements. The content analysis unit analyzes, for example, the text of a novel or a movie and performs emotion analysis. For example, it analyzes the emotional tone of the characters' dialogue or narration and identifies emotions such as joy, sadness, and anger. The content analysis unit also extracts emotionally significant elements and provides them to the user. For example, it extracts moving scenes or emotional climaxes and provides them to the user. In this way, it is possible to analyze the emotional tone or mood in the content and extract emotionally significant elements.

[0069] The content analysis unit can analyze the relationships between characters or the patterns of their dialogue to clarify the structure of the story. The content analysis unit, for example, analyzes the text of a novel to extract the relationships between characters. For example, it identifies intimate relationships and conflicting relationships based on the frequency and content of dialogue. The content analysis unit also analyzes dialogue patterns to clarify the structure of the story. For example, it identifies the progression and climax of the story based on the flow and content of the dialogue. In this way, it is possible to analyze the relationships between characters and the patterns of dialogue to clarify the structure of the story.

[0070] The content analysis unit can use the emotion estimation function to analyze the emotion a user has toward a specific scene or quote and extract related elements based on that emotion. For example, the content analysis unit can analyze the user's emotional reaction to a specific scene in a novel and suggest other related scenes or works based on that emotion. For example, based on the emotional reaction to a moving scene, the content analysis unit can suggest other similarly moving works. The content analysis unit can also use the emotion estimation function to analyze the emotion a user has toward a specific quote and extract related elements based on that emotion. For example, based on the emotional reaction to the quoted passage, other related quotes or works can be suggested. This makes it possible to analyze the emotion a user has toward a specific scene or quote and extract related elements based on that emotion.

[0071] The content analysis unit can expand the content analysis target to include not only text but also multimedia content such as images, audio, and video. The content analysis unit includes not only the text of novels and movies, but also image and audio data as its analysis target. For example, it analyzes movie scene images and audio tracks to extract related elements. The content analysis unit also analyzes video data to extract related elements. For example, it analyzes video scenes and audio to extract related elements. This allows the content analysis target to be expanded to include not only text but also multimedia content such as images, audio, and video.

[0072] The content analysis unit can analyze content in different languages ​​and extract elements that take cultural differences into consideration. For example, the content analysis unit analyzes the text of novels or movies written in different languages ​​and extracts elements that take cultural differences into consideration. For example, it identifies expressions and quotations that are unique to a particular culture. The content analysis unit also analyzes content in different languages ​​and extracts elements that take cultural backgrounds and customs into consideration. For example, it identifies historical events and people in different cultures. This makes it possible to analyze content in different languages ​​and extract elements that take cultural differences into consideration.

[0073] The content analysis unit uses the emotion estimation function to analyze the user's real-time emotional response to the content they are watching and extract related elements based on the emotion. The content analysis unit analyzes the user's emotional response in real time while they are watching, for example, a movie or a drama, and suggests other related scenes or works based on the emotion. For example, based on the emotional response to a moving scene, it suggests other similarly moving works. The content analysis unit also uses the emotion estimation function to analyze the user's real-time emotional response to the content they are watching and extract related elements based on the emotion. For example, based on the emotional response to a scene they are watching, it suggests other related scenes or works. This makes it possible to analyze the user's real-time emotional response to the content they are watching and extract related elements based on the emotion.

[0074] The suggestion unit can classify the extracted elements based on the theme or message of the story and suggest related content. For example, the suggestion unit classifies quotations or musical pieces extracted from a novel based on the theme of the story. For example, elements with themes of love or friendship are collectively suggested. The suggestion unit can also classify the extracted elements based on the message of the story and suggest related content. For example, elements containing lessons or warnings are collectively suggested. In this way, the extracted elements can be classified based on the theme or message of the story and suggest related content.

[0075] The suggestion unit can use the emotion estimation function to suggest other content that emotionally resonates with the user based on the emotions the user has toward a specific element. For example, the suggestion unit analyzes the user's emotional reaction to a specific scene in a novel and suggests other related scenes or works based on the emotions. For example, based on the emotional reaction to a moving scene, the suggestion unit suggests other similarly moving works. The suggestion unit also uses the emotion estimation function to suggest other content that emotionally resonates with the user based on the emotions the user has toward a specific element. For example, based on the emotional reaction to a specific quote or piece of music, the suggestion unit suggests other related quotes or works. In this way, other content that emotionally resonates with the user can be suggested based on the emotions the user has toward a specific element.

[0076] The suggestion unit can connect the extracted elements across different genres or media formats. For example, the suggestion unit can connect an extracted quote or piece of music from a novel with content from a different genre. For example, the suggestion unit can suggest movies or music based on a quote from a novel. The suggestion unit can also connect the extracted elements across different media formats. For example, the suggestion unit can suggest artwork or digital content based on a quote from a novel. This allows the extracted elements to be connected across different genres and media formats.

[0077] The suggestion unit can connect the extracted elements with educational content or academic materials to enhance learning depth. For example, the suggestion unit can connect an extracted quote from a novel or a musical piece with educational content, such as providing commentary on a historical event or person based on the quote from the novel. The suggestion unit can also connect the extracted elements with academic materials to enhance learning depth, such as providing a research paper on a particular historical event. This allows the extracted elements to be connected with educational content or academic materials to enhance learning depth.

[0078] The suggestion unit uses the emotion estimation function to analyze the user's emotional response to the content being viewed in real time and connect related elements based on the emotion. For example, the suggestion unit analyzes the user's emotional response in real time while watching a movie or a drama and suggests other related scenes or works based on the emotion. For example, based on the emotional response to a moving scene, the suggestion unit suggests other similarly moving works. The suggestion unit also uses the emotion estimation function to analyze the user's emotional response to the content being viewed in real time and connect related elements based on the emotion. For example, based on the emotional response to the scene being viewed, the suggestion unit suggests other related scenes or works. This makes it possible to analyze the user's emotional response to the content being viewed in real time and connect related elements based on the emotion.

[0079] The suggestion unit can suggest related academic papers or research materials that may be of interest to the user based on the extracted elements. For example, the suggestion unit can suggest academic papers about historical events or people cited in a novel. For example, the suggestion unit can provide research papers on specific historical events. The suggestion unit can also suggest related research materials that may be of interest to the user based on the extracted elements. For example, the suggestion unit can provide datasets or experimental results on a specific topic. This makes it possible to suggest related academic papers or research materials that may be of interest to the user based on the extracted elements.

[0080] The suggestion unit can provide interactive content that provides detailed explanations of the historical or cultural background related to the extracted elements. For example, the suggestion unit can provide detailed explanations of historical events or people cited in a novel. For example, the suggestion unit can explain the historical background using an interactive timeline or map. The suggestion unit can also provide interactive content that provides detailed explanations of the cultural background related to the extracted elements. For example, the suggestion unit can provide interactive content that explains the customs and values ​​of a particular culture. This makes it possible to provide interactive content that provides detailed explanations of the historical or cultural background related to the extracted elements.

[0081] The suggestion unit can use the emotion estimation function to suggest emotionally interesting learning content based on the emotions the user feels toward specific elements. For example, the suggestion unit analyzes the user's emotional reaction to a specific scene in a novel and suggests related academic papers or research materials based on the emotions. For example, based on the emotional reaction to a moving scene, the suggestion unit can suggest similarly moving academic papers. The suggestion unit can also use the emotion estimation function to suggest emotionally interesting learning content based on the emotions the user feels toward specific elements. For example, based on the emotional reaction to a specific quote or piece of music, the suggestion unit can suggest related learning content. This makes it possible to suggest emotionally interesting learning content based on the emotions the user feels toward specific elements.

[0082] The suggestion unit can provide information from different academic fields or related information to promote learning. For example, the suggestion unit can suggest related scientific concepts or theories based on literary elements cited in a novel. For example, the suggestion unit can provide explanations of scientific theories that appear in literary works. The suggestion unit can also provide related information across different academic fields. For example, the suggestion unit can provide information related to different academic fields, such as from literature to science or science to history. This makes it possible to provide related information across different academic fields to promote learning.

[0083] The suggestion unit may provide online discussions or forums in which users can participate to promote learning. For example, the suggestion unit may provide online discussions about elements quoted in a novel. For example, the suggestion unit may open a discussion forum about a particular literary work or historical event. The suggestion unit may also provide online discussions or forums in which users can participate. For example, the suggestion unit may provide a forum in which users can exchange opinions through chat rooms or video conferencing. In this way, online discussions or forums in which users can participate to promote learning may be provided.

[0084] The suggestion unit can use the emotion estimation function to analyze the emotions felt by the user during the learning process and adjust the depth of learning based on those emotions. For example, the suggestion unit can analyze the user's emotional response during the learning process to an element quoted in a novel and adjust the depth of learning based on those emotions. For example, the content of learning can be adjusted based on the emotional response to a moving scene. The suggestion unit can also use the emotion estimation function to analyze the emotions felt by the user during the learning process and adjust the depth of learning based on those emotions. For example, the speed and content of learning can be adjusted based on the user's emotional response. This makes it possible to analyze the emotions felt by the user during the learning process and adjust the depth of learning based on those emotions.

[0085] The suggestion unit incorporates an emotion estimation function into the user interface and can customize the interface according to the user's emotion. The suggestion unit, for example, analyzes the user's emotional response in real time and dynamically changes the design and color scheme of the interface according to the emotion. For example, if the user has a strong positive emotion, it provides an interface with a bright color tone. The suggestion unit also uses the emotion estimation function to customize the interface according to the user's emotion. For example, it adjusts the layout and functions of the interface based on the user's emotion. In this way, the emotion estimation function can be incorporated into the user interface and the interface can be customized according to the user's emotion.

[0086] The suggestion unit can introduce an interactive map or timeline into the user interface and visually display related content. The suggestion unit, for example, displays elements quoted in a novel using an interactive map or timeline. For example, it visually displays related elements along the progression of the story. The suggestion unit also visually displays related content using an interactive map or timeline. For example, it visually displays elements related to a specific place or time. In this way, it is possible to introduce an interactive map or timeline into the user interface and visually display related content.

[0087] The suggestion unit can adapt the user interface to at least one of different devices (smartphone, tablet, PC) to enable seamless use. For example, when a user is reading a novel on a smartphone, the suggestion unit provides a function that allows the same content to be seamlessly displayed on a tablet or PC. For example, the suggestion unit synchronizes data between devices. The suggestion unit also provides a user interface that is adapted to different devices. For example, it provides interfaces optimized for smartphones, tablets, and PCs. This allows the user interface to be adapted to different devices to enable seamless use.

[0088] The suggestion unit can add a social media integration function to the user interface, making it easier for users to share content. For example, the suggestion unit provides a function that allows users to share elements quoted in a novel on social media. For example, a specific quote or piece of music can be shared on an SNS. The suggestion unit also uses the social media integration function to make it easier for users to share content. For example, it provides a function that allows users to post to an SNS with one click. This adds a social media integration function to the user interface, making it easier for users to share content.

[0089] The suggestion unit can use the emotion estimation function to dynamically change the design or color scheme of the interface based on the user's emotion. For example, the suggestion unit analyzes the user's emotional response in real time and dynamically changes the design or color scheme of the interface according to the emotion. For example, if the user has a strong positive emotion, the suggestion unit provides an interface with a bright color tone. The suggestion unit also uses the emotion estimation function to dynamically change the design or color scheme of the interface based on the user's emotion. For example, the layout or color of the interface is adjusted based on the user's emotion. In this way, the emotion estimation function can be used to dynamically change the design or color scheme of the interface based on the user's emotion.

[0090] The suggestion unit can use the emotion estimation function to preferentially recommend content that resonates with the user's emotions based on the user's past browsing history or interests. The suggestion unit, for example, analyzes the user's past browsing history and uses the emotion estimation function to preferentially recommend content that resonates with the user's emotions. For example, for a user who likes novels with many moving scenes, other similarly moving novels are recommended. The suggestion unit also preferentially recommends content that resonates with the user's emotions based on the user's interests. For example, content related to a specific genre or theme is preferentially recommended. This allows the suggestion unit to preferentially recommend content that resonates with the user's emotions based on the user's past browsing history and interests using the emotion estimation function.

[0091] The suggestion unit can analyze the user's browsing history and recommend content related to a specific theme or topic by digging deeper. For example, the suggestion unit can analyze the user's past browsing history and recommend novels related to a specific theme or topic by digging deeper. For example, for a user who likes novels with many historical themes, other novels that also deal with historical themes are recommended. The suggestion unit can also analyze the user's browsing history and recommend content related to a specific theme or topic by digging deeper. For example, content related to a specific technology or culture is recommended. In this way, the suggestion unit can analyze the user's browsing history and recommend content related to a specific theme or topic by digging deeper.

[0092] The suggestion unit can recommend a series or sequel of related content based on the user's interests. For example, the suggestion unit analyzes the user's past browsing history and recommends a series or sequel of related novels based on the user's interests. For example, for a user who likes a particular series of novels, the suggestion unit recommends sequels to that series. The suggestion unit also recommends a series or sequel of related content based on the user's interests. For example, the suggestion unit recommends a series or sequel related to a particular theme or genre. This makes it possible to recommend a series or sequel of related content based on the user's interests.

[0093] The suggestion unit can recommend content of different genres or media formats based on the user's interests. For example, the suggestion unit analyzes the user's past browsing history and recommends novels of different genres based on the user's interests. For example, for a user who likes fantasy novels, novels of other genres that also contain fantasy elements are recommended. The suggestion unit also recommends content of different media formats based on the user's interests. For example, the suggestion unit recommends not only novels but also related content such as movies and music. This makes it possible to recommend content of different genres and media formats based on the user's interests.

[0094] The suggestion unit can provide information on related events or workshops based on the user's interests. The suggestion unit, for example, analyzes the user's past browsing history and provides information on related events or workshops based on the user's interests. For example, it recommends events or workshops related to a specific novel or movie. The suggestion unit also provides information on related events or workshops based on the user's interests. For example, it recommends events or workshops related to a specific theme or genre. This makes it possible to provide information on related events or workshops based on the user's interests.

[0095] The suggestion unit uses the emotion estimation function to make personalized content recommendations based on the user's emotions, thereby providing an emotionally positive experience. The suggestion unit, for example, analyzes the user's emotional response in real time and makes personalized content recommendations based on the emotions. For example, if the user has a strong positive emotion, the suggestion unit recommends content that similarly elicits positive emotions. The suggestion unit also uses the emotion estimation function to make personalized content recommendations based on the user's emotions, thereby providing an emotionally positive experience. For example, the suggestion unit recommends content that provides a positive experience based on the user's emotions. In this way, the emotion estimation function can be used to make personalized content recommendations based on the user's emotions, thereby providing an emotionally positive experience.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can analyze a user's past browsing history and recommend content related to specific themes or topics by digging deeper. For example, if a user has read many historical novels in the past, the suggestion unit can recommend other novels that also deal with historical themes. Also, if a user is interested in a particular technology or culture, the suggestion unit can suggest academic papers and articles related to that topic. This allows for deeper learning and new discoveries based on the user's interests.

[0098] If a user likes novels of a particular genre, the suggestion unit can preferentially suggest other works and learning information related to that genre. For example, if a user likes mystery novels, the suggestion unit can suggest other novels and movies that also contain mystery elements. The suggestion unit can also preferentially suggest academic papers and articles related to a particular genre. This allows a user to naturally experience other related works and learning information while enjoying novels of a particular genre.

[0099] The suggestion unit can automatically display other content and learning information related to a novel when the user enters the title of the novel they are reading. For example, when a user enters the title of a particular novel, movies and music works related to that novel are automatically displayed. Academic papers and articles related to the novel can also be automatically displayed. This allows the user to easily access other related content and learning information while reading a novel.

[0100] The suggestion unit can analyze the user's emotional response to the content being viewed in real time and connect related elements based on the emotion. For example, the suggestion unit can analyze the user's emotional response while watching a movie or drama, and suggest other similarly moving works based on the user's emotional response to a moving scene. It can also suggest other related scenes or works based on the user's emotional response to the scene being viewed. This allows the user to analyze the user's emotional response to the content being viewed in real time and connect related elements based on the emotion.

[0101] The suggestion unit can dynamically change the design or color scheme of the interface based on the user's emotions. For example, it can analyze the user's emotional response in real time and provide an interface with bright colors if the user has a strong positive emotion. It can also provide an interface with subdued colors if the user has a strong negative emotion. This allows the interface to be customized according to the user's emotions, providing a more comfortable user experience.

[0102] The suggestion unit can make personalized content recommendations based on the user's emotions and provide an emotionally positive experience. For example, the suggestion unit analyzes the user's emotional response in real time, and if the user's positive emotions are strong, recommends content that similarly elicits positive emotions. It can also recommend content that provides a positive experience based on the user's emotions. This allows the emotion estimation function to make personalized content recommendations based on the user's emotions and provide an emotionally positive experience.

[0103] The suggestion unit can use the emotion estimation function to preferentially recommend content that resonates with the user's emotions based on the user's past browsing history or interests. For example, by analyzing the user's past browsing history, the suggestion unit can recommend other similarly moving novels to a user who likes novels with many moving scenes. The suggestion unit can also preferentially recommend content related to a specific genre or theme based on the user's interests. This allows the suggestion unit to preferentially recommend content that resonates with the user's emotions based on the user's past browsing history and interests using the emotion estimation function.

[0104] The suggestion unit can provide information on related events or workshops based on the user's interests. For example, it can analyze the user's past browsing history and recommend events or workshops related to a specific novel or movie. It can also provide information on events or workshops related to a specific theme or genre. This allows the user to receive information on related events and workshops based on their interests, expanding learning opportunities.

[0105] The suggestion unit can introduce an interactive map or timeline into the user interface to visually display related content. For example, elements quoted in a novel can be displayed on an interactive map or timeline to visually display related elements as the story progresses. Elements related to a specific place or time can also be visually displayed. This allows the suggestion unit to introduce an interactive map or timeline into the user interface to visually display related content.

[0106] The suggestion unit can add a social media integration function to the user interface, making it easier for users to share content. For example, a function can be provided that allows users to share elements quoted in a novel on social media. A specific quote or piece of music can be shared on a social networking site. A function can also be provided that allows users to post to a social networking site with one click. This adds a social media integration function to the user interface, making it easier for users to share content.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The content analysis unit analyzes the content. For example, it analyzes entertainment content such as novels and movies to detect citations from other works, music, art, and other elements contained within. Step 2: The element extraction unit extracts at least one element of quotations from other works, music, or art from the content analyzed by the content analysis unit. For example, it detects quotations from specific musical works or movies that appear in a novel and creates a list of them. Step 3: The suggestion unit suggests other related content based on the elements extracted by the element extraction unit. For example, it may present the user with movies or music pieces cited in the novel, along with detailed information and links to watch them.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 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 content analysis unit that analyzes content; an element extraction unit that extracts at least one element of a quotation from another work, music, or art from the content analyzed by the content analysis unit; a suggestion unit that suggests other related content based on the elements extracted by the element extraction unit. A system characterized by:

2. The element extraction unit Identify and list specific musical or film quotations that appear in a novel 2. The system of claim 1.

3. The proposal unit When a user enters the title of a novel they are reading, other content or learning information related to that novel is automatically displayed.

2. The system of claim 1.

4. The proposal unit Categorizing the extracted elements based on the theme or message of the story and suggesting related content.

2. The system of claim 1.

5. The proposal unit Based on the extracted elements, suggest relevant academic papers or research materials that may be of interest to the user.

2. The system of claim 1.

6. The content analysis unit Analyzing the emotional tone or mood within said content and extracting said emotionally significant elements.

2. The system of claim 1.

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