system

Abstract

The system according to this embodiment aims to enable users to efficiently search for works that contain specific plot developments or scenes. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives user queries. The analysis unit analyzes the queries received by the reception unit and analyzes the content of manga and novels in detail. The identification unit identifies specific plot developments or scenes based on the content analyzed by the analysis unit. The search unit searches for works based on the plot developments or scenes identified by the identification unit. The provision unit provides the search results obtained by the search unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to efficiently search for comics or novels including specific developments or scenes.

[0005] The system according to the embodiment aims to enable a user to efficiently search for works including specific developments or scenes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives user queries. The analysis unit analyzes the queries received by the reception unit and analyzes the content of manga and novels in detail. The identification unit identifies specific plot developments or scenes based on the content analyzed by the analysis unit. The search unit searches for works based on the plot developments or scenes identified by the identification unit. The provision unit provides the search results obtained by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently search for works that contain specific plot developments or scenes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The manga / novel plot search system according to an embodiment of the present invention is a system that uses generative AI to analyze the content of manga and novels and allows users to search for and confirm works based on specific plot developments or scenes. This system allows users to easily find works containing preferred or undesirable plot developments by inputting vague queries. This personalizes the content experience and improves user satisfaction. For example, if a user searches for "a scene where the protagonist awakens to a hidden power," the generative AI identifies works containing that scene and provides them to the user. The user can then confirm the work and enjoy their preferred plot development. Also, if a user wants to avoid "a scene where a dog dies," they can check works containing that scene in advance and avoid them. Thus, the manga / novel plot search system using generative AI is a groundbreaking solution for improving the user's content experience and increasing satisfaction. As a result, the manga / novel plot search system significantly reduces the effort and time users spend searching for their favorite works and provides a means to check works containing undesirable plot developments in advance, thereby improving user satisfaction.

[0029] The manga / novel plot search system according to this embodiment comprises a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives user queries. The reception unit can, for example, receive ambiguous queries entered by the user. The analysis unit analyzes the queries received by the reception unit and analyzes the content of the manga or novel in detail. The analysis unit can, for example, analyze the entire text of the work using a generative AI. The identification unit identifies specific plot developments or scenes based on the content analyzed by the analysis unit. The identification unit can, for example, identify specific plot developments or scenes using a generative AI. The search unit searches for works based on the plot developments or scenes identified by the identification unit. The search unit can, for example, search for works based on specific plot developments or scenes using a generative AI. The provision unit provides the results searched by the search unit. The provision unit can, for example, provide the search results to the user. Thus, the manga / novel plot search system according to this embodiment can search for and provide specific plot developments or scenes of manga or novels based on user queries.

[0030] The reception unit receives user queries. For example, the reception unit can accept vague queries entered by users. Specifically, even if a user enters vague expressions such as "a scene where the protagonist is in danger" or "a moving ending," the reception unit can accept them. The reception unit receives queries through the user interface and sends the query content to the analysis unit. The user interface includes text input fields and voice input functions, designed to allow users to easily enter queries. Furthermore, when receiving user queries, the reception unit can provide auxiliary functions to reduce query ambiguity. For example, it can provide a function that automatically completes relevant keywords and phrases when the user enters a query. It can also suggest appropriate queries to the user by referring to past search history and popular queries. This allows the reception unit to help users enter more specific and appropriate queries, improving the overall search accuracy of the system.

[0031] The analysis unit analyzes queries received by the reception unit and performs a detailed analysis of the content of manga and novels. For example, the analysis unit uses generative AI to analyze the entire text of a work. Specifically, the generative AI utilizes natural language processing technology to analyze the text data of the work and extract information such as characters, story progression, and emotional changes. Based on keywords and phrases included in the query, the generative AI identifies relevant parts of the work and performs a detailed analysis. For example, in response to the query "scene where the protagonist is in danger," the generative AI identifies the scene in the work where the protagonist faces a difficult situation and analyzes the detailed content of that scene. Furthermore, the analysis unit can not only analyze the entire text of a work but also analyze metadata and related information of the work. For example, it analyzes information such as the genre, author, publication date, and rating of the work and uses it as auxiliary information to provide appropriate search results for queries. As a result, the analysis unit can perform highly accurate analysis of user queries and improve the overall search accuracy of the system.

[0032] The identification unit identifies specific plot developments or scenes based on the analysis performed by the analysis unit. For example, the identification unit can use a generative AI to identify specific plot developments or scenes. Specifically, the generative AI executes an algorithm to identify specific plot developments or scenes based on the data provided by the analysis unit. For example, in response to a query such as "emotional ending," the generative AI identifies the ending portion of the work and then identifies scenes within it that contain emotionally moving elements. The identification unit can use the generative AI's algorithm to identify specific plot developments or scenes within the work with high accuracy. Furthermore, the identification unit can add relevant information to the identified plot developments or scenes. For example, it can add information such as the context before and after the identified scene, or changes in the characters' emotions, to help the search unit provide more appropriate search results. This allows the identification unit to perform highly accurate identification of user queries and improve the overall search accuracy of the system.

[0033] The search unit searches for works based on plot developments and scenes identified by the identification unit. The search unit can, for example, use a generative AI to search for works based on specific plot developments and scenes. Specifically, the generative AI executes an algorithm to search for relevant works based on the data provided by the identification unit. For example, in response to a query such as "a scene where the protagonist is in danger," the generative AI searches for works containing the scene identified by the identification unit and provides detailed information about those works. The search unit can perform highly accurate searches for user queries using the generative AI's algorithm. Furthermore, the search unit can add relevant information when providing search results to the user. For example, it can add information such as a summary of the work, its rating, and excerpts of relevant scenes to the search results to make them easier for the user to understand. This allows the search unit to perform highly accurate searches for user queries and improve the overall search accuracy of the system.

[0034] The service provider provides the results searched by the search provider. For example, the service provider can provide search results to the user. Specifically, the service provider displays search results through a user interface, making them easily accessible to the user. The user interface is designed to display search results in a visually clear manner and is designed to allow users to quickly review the results. Furthermore, the service provider can provide relevant information that the user might be interested in in relation to the search results. For example, it can suggest other works related to the search results, other works by the same author, or works in related genres. In addition, the service provider can collect user feedback and continuously improve the accuracy and presentation of search results. This allows the service provider to provide users with high-quality search results and improve the overall user experience of the system.

[0035] The interface unit can provide a user interface. For example, the interface unit can provide a graphical user interface. The interface unit can also provide a voice interface. This makes it easier for the user to operate the system. Some or all of the above-described processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's operation history into the AI ​​and have the AI ​​perform processing to provide the optimal interface.

[0036] The security unit can encrypt and anonymize data. For example, the security unit can encrypt data using encryption algorithms such as AES or RSA. The security unit can also anonymize data using methods such as data masking or pseudo-anonymization. This ensures that user data is securely protected. Some or all of the above processes performed by the security unit may be carried out using AI, for example, or not. For example, the security unit can input methods for data encryption and anonymization into the AI ​​and have the AI ​​perform the process of selecting the optimal method.

[0037] The reception desk can analyze the user's past query history and select the optimal query reception method. For example, the reception desk can automatically display queries that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest queries to be used during specific time periods based on the user's past query history. This allows the reception desk to provide the optimal query reception method based on the user's past query history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past query history into an AI and have the AI ​​perform the processing to select the optimal query reception method.

[0038] The reception desk can filter queries based on the user's current areas of interest when they are received. For example, the reception desk can prioritize receiving relevant queries based on the genres the user has recently searched for. The reception desk can also analyze the user's social media activity and suggest queries related to their current areas of interest. Furthermore, the reception desk can receive relevant queries based on the newsletters the user subscribes to and the authors they follow. This allows the reception desk to prioritize receiving relevant queries based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current areas of interest into the AI ​​and have the AI ​​perform the filtering process.

[0039] The reception desk can prioritize receiving queries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize queries related to works in that region. Furthermore, if the user is traveling, the reception desk can prioritize queries related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize queries related to their home area. This allows the reception desk to prioritize receiving queries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into an AI and have the AI ​​perform the processing necessary to prioritize receiving highly relevant queries.

[0040] The reception desk can analyze a user's social media activity when receiving a query and accept relevant queries. For example, the reception desk can prioritize relevant queries based on posts the user has recently "liked." It can also prioritize relevant queries based on authors and artists the user follows. Furthermore, the reception desk can prioritize relevant queries based on topics in online communities the user participates in. This allows for the priority acceptance of relevant queries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI and have the AI ​​perform the processing to prioritize the acceptance of relevant queries.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the work during the analysis. For example, the analysis unit will perform a detailed analysis of popular works. The analysis unit can also perform a concise analysis of new or unrated works. Furthermore, the analysis unit can perform a special analysis of works that the user is particularly interested in. This allows the level of detail of the analysis to be adjusted based on the importance of the work. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the work into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the work during analysis. For example, in the case of manga, the analysis unit can apply an image analysis algorithm. In the case of novels, the analysis unit can also apply a text analysis algorithm. Furthermore, in the case of light novels, the analysis unit can apply an algorithm that analyzes both images and text. This allows the analysis algorithm to be applied according to the category of the work. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the work into the AI ​​and have the AI ​​perform the process of selecting the analysis algorithm to apply.

[0043] The analysis unit can determine the priority of analysis based on the publication date of the works during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent works. It may also postpone the analysis of classic works. Furthermore, the analysis unit may prioritize the analysis of works that the user is particularly interested in. This allows the analysis priority to be determined based on the publication date of the works. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the publication dates of the works into the AI ​​and have the AI ​​perform the processing to determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the works during the analysis process. For example, the analysis unit may prioritize analyzing works that are most relevant to the user's search query. It can also prioritize analyzing works that are highly relevant based on the user's past search history. Furthermore, it can prioritize analyzing works that are highly relevant based on the user's current areas of interest. This allows the order of analysis to be adjusted based on the relevance of the works. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the works into the AI ​​and have the AI ​​perform the process of adjusting the order of analysis.

[0045] The identification unit can improve the accuracy of identification by considering the relationships between works during the identification process. For example, the identification unit can improve accuracy by considering the relationships when identifying works in the same series. It can also improve accuracy by considering the relationships when identifying works by the same author. Furthermore, it can improve accuracy by considering the relationships when identifying works in the same genre. In this way, the accuracy of identification can be improved by considering the relationships between works. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the relationships between works into AI and have AI perform processing to improve the accuracy of identification.

[0046] The identification unit can perform identification by considering the attribute information of the author of the work. For example, the identification unit can perform identification by considering the trends of the author's past works. The identification unit can also perform identification by considering the author's style and themes. Furthermore, the identification unit can perform identification by considering the author's career and background information. This allows for identification by considering the attribute information of the author of the work. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the author's attribute information into AI and have the AI ​​perform the processing for identification.

[0047] The identification unit can perform identification while considering the geographical distribution of the works. For example, the identification unit can identify works that are popular in a particular region. It can also identify works that were published in a particular region. Furthermore, it can identify works that have themes related to a particular region. This allows for identification while considering the geographical distribution of the works. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the geographical distribution of the works into the AI ​​and have the AI ​​perform the processing for identification.

[0048] The identification unit can improve the accuracy of its identification by referring to related literature for the work during the identification process. For example, the identification unit can improve the accuracy of its identification by referring to related literature for the work. It can also improve the accuracy of its identification by referring to reviews and ratings of the work. Furthermore, the identification unit can improve the accuracy of its identification by referring to related news articles for the work. In this way, it can improve the accuracy of its identification by referring to related literature for the work. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input related literature for the work into the AI ​​and have the AI ​​perform processing to improve the accuracy of its identification.

[0049] The search unit can improve search accuracy by considering the relationships between works during the search process. For example, when searching for works in the same series, the search unit can improve accuracy by considering the relationships between works. Furthermore, when searching for works by the same author, the search unit can improve accuracy by considering the relationships between works. In this way, the search unit can improve search accuracy by considering the relationships between works. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relationships between works into the AI ​​and have the AI ​​perform processing to improve search accuracy.

[0050] The search unit can perform searches while considering the attribute information of the work's author. For example, the search unit can perform searches while considering the trends of the author's past works. It can also perform searches while considering the author's style and themes. Furthermore, the search unit can perform searches while considering the author's career and background information. This allows the search to be performed while considering the attribute information of the work's author. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the author's attribute information into AI and have the AI ​​perform the processing for performing the search.

[0051] The search unit can perform searches while considering the geographical distribution of works. For example, the search unit can search for works that are popular in a particular region. It can also search for works that were published in a particular region. Furthermore, it can search for works that have themes related to a particular region. This allows the search to be performed while considering the geographical distribution of works. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the geographical distribution of works into AI and have AI perform the processing for performing the search.

[0052] The search unit can improve the accuracy of its search by referring to related literature for the work during the search process. For example, the search unit can improve the accuracy of its search by referring to related literature for the work. It can also improve the accuracy of its search by referring to reviews and ratings of the work. Furthermore, the search unit can improve the accuracy of its search by referring to news articles related to the work. In this way, it can improve the accuracy of its search by referring to related literature for the work. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature for the work into the AI ​​and have the AI ​​perform processing to improve the accuracy of the search.

[0053] The information provider can provide optimal information by referring to the user's past search history at the time of delivery. For example, the information provider can provide relevant information based on queries the user has searched for in the past. The information provider can also predict and provide information that the user will use at a specific time of day based on the user's past search history. Furthermore, the information provider can analyze the user's past search history and provide the most relevant information. This allows the information provider to provide optimal information based on the user's past search history. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's past search history into AI and have the AI ​​perform the processing to provide optimal information.

[0054] The information provider can customize information based on the user's current areas of interest at the time of delivery. For example, the provider can provide relevant information based on genres the user has recently searched for. The provider can also analyze the user's social media activity and provide information related to their current areas of interest. Furthermore, the provider can provide relevant information based on newsletters the user subscribes to and authors the user follows. This allows the information to be customized based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's current areas of interest into AI and have AI perform the processing to customize the information.

[0055] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the provider can provide information related to that region. Furthermore, if the user is traveling, the provider can provide information related to their travel destination. Additionally, if the user is at home, the provider can provide information related to their home area. This allows the provider to provide optimal information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location into AI and have AI perform the processing necessary to provide optimal information.

[0056] The information provider can analyze the user's social media activity and provide information at the time of delivery. For example, the provider can provide relevant information based on posts the user has recently "liked". It can also provide relevant information based on authors and artists the user follows. Furthermore, it can provide relevant information based on topics in online communities the user participates in. This allows the provider to provide relevant information based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity into AI and have the AI ​​perform the processing to provide information.

[0057] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize displaying interface designs that the user has previously preferred. The interface unit can also prioritize displaying specific functions based on the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and provide the most user-friendly interface. This allows the interface unit to provide the optimal interface display method based on the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history into AI and have the AI ​​perform the processing to select the optimal display method.

[0058] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. This allows the interface unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into the AI ​​and have the AI ​​perform the processing to select the optimal display method.

[0059] The security unit can select the optimal encryption method when encrypting data by referring to the user's past security history. For example, the security unit may prioritize applying encryption methods previously used by the user. It can also select a specific encryption method based on the user's past security history. Furthermore, the security unit can analyze the user's past security history and provide the most appropriate encryption method. This allows the security unit to provide the optimal encryption method based on the user's past security history. Some or all of the above processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the user's past security history into an AI and have the AI ​​perform the process of selecting the optimal encryption method.

[0060] The security unit can select the optimal encryption method when encrypting data, taking into account the user's geographical location. For example, if the user is in a specific region, the security unit can select an encryption method based on the security regulations of that region. Furthermore, if the user is traveling, the security unit can select an encryption method based on the security regulations of the travel destination. Additionally, if the user is at home, the security unit can select an encryption method based on the home's security environment. This allows the security unit to provide the optimal encryption method based on the user's geographical location. Some or all of the above processing in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the user's geographical location information into an AI and have the AI ​​perform the process of selecting the optimal encryption method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can analyze a user's past search history and automatically suggest relevant queries based on the queries the user has previously searched for. For example, if a user has frequently searched for works in the "fantasy" genre in the past, the reception desk will prioritize suggesting queries related to the "fantasy" genre. It can also suggest new works or related works by a particular author if the user has frequently searched for their works. Furthermore, if a user is interested in a specific theme (e.g., "adventure" or "romance"), it can suggest queries related to that theme. This allows for more personalized query suggestions based on the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past search history into an AI and have the AI ​​perform the process of selecting the most suitable query suggestions.

[0063] The reception desk can prioritize receiving queries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize queries related to works in that region. If the user is traveling, it can also prioritize queries related to their travel destination. Furthermore, if the user is at home, it can prioritize queries related to their home area. This allows the reception desk to prioritize receiving queries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into the AI ​​and have the AI ​​perform the processing to prioritize receiving queries that are highly relevant.

[0064] The search unit can improve search accuracy by considering the relationships between works during the search process. For example, it can improve accuracy by considering the relationships between works when searching for works in the same series. It can also improve accuracy by considering the relationships between works when searching for works by the same author. Furthermore, it can improve accuracy by considering the relationships between works when searching for works in the same genre. In this way, the search unit can improve search accuracy by considering the relationships between works. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relationships between works into the AI ​​and have the AI ​​perform processing to improve search accuracy.

[0065] The information provider can provide optimal information by referring to the user's past search history at the time of delivery. For example, it can provide relevant information based on queries the user has searched for in the past. It can also predict and provide information that the user will use at a specific time of day based on the user's past search history. Furthermore, it can analyze the user's past search history and provide the most relevant information. This allows the information provider to provide optimal information based on the user's past search history. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's past search history into AI and have the AI ​​perform the processing to provide optimal information.

[0066] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the interface unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into the AI ​​and have the AI ​​perform the processing to select the optimal display method.

[0067] The security unit can select the optimal encryption method when encrypting data by referring to the user's past security history. For example, it can prioritize applying encryption methods previously used by the user. It can also select a specific encryption method based on the user's past security history. Furthermore, it can analyze the user's past security history and provide the most appropriate encryption method. This allows the security unit to provide the optimal encryption method based on the user's past security history. Some or all of the above processes in the security unit may be performed using AI, for example, or not. For example, the security unit can input the user's past security history into an AI and have the AI ​​perform the process of selecting the optimal encryption method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives user queries. The reception desk can, for example, receive ambiguous queries entered by users. Step 2: The analysis unit analyzes the queries received by the reception unit and performs a detailed analysis of the content of the manga or novel. For example, the analysis unit uses a generative AI to analyze the entire text of the work. Step 3: The identification unit identifies specific developments or scenes based on the content analyzed by the analysis unit. The identification unit can, for example, use a generative AI to identify specific developments or scenes. Step 4: The search unit searches for works based on the plot developments and scenes identified by the identification unit. The search unit can, for example, use a generative AI to search for works based on specific plot developments and scenes. Step 5: The providing unit provides the results found by the searching unit. For example, the providing unit can provide the search results to the user.

[0070] (Example of form 2) The manga / novel plot search system according to an embodiment of the present invention is a system that uses generative AI to analyze the content of manga and novels and allows users to search for and confirm works based on specific plot developments or scenes. This system allows users to easily find works containing preferred or undesirable plot developments by inputting vague queries. This personalizes the content experience and improves user satisfaction. For example, if a user searches for "a scene where the protagonist awakens to a hidden power," the generative AI identifies works containing that scene and provides them to the user. The user can then confirm the work and enjoy their preferred plot development. Also, if a user wants to avoid "a scene where a dog dies," they can check works containing that scene in advance and avoid them. Thus, the manga / novel plot search system using generative AI is a groundbreaking solution for improving the user's content experience and increasing satisfaction. As a result, the manga / novel plot search system significantly reduces the effort and time users spend searching for their favorite works and provides a means to check works containing undesirable plot developments in advance, thereby improving user satisfaction.

[0071] The manga / novel plot search system according to this embodiment comprises a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives user queries. The reception unit can, for example, receive ambiguous queries entered by the user. The analysis unit analyzes the queries received by the reception unit and analyzes the content of the manga or novel in detail. The analysis unit can, for example, analyze the entire text of the work using a generative AI. The identification unit identifies specific plot developments or scenes based on the content analyzed by the analysis unit. The identification unit can, for example, identify specific plot developments or scenes using a generative AI. The search unit searches for works based on the plot developments or scenes identified by the identification unit. The search unit can, for example, search for works based on specific plot developments or scenes using a generative AI. The provision unit provides the results searched by the search unit. The provision unit can, for example, provide the search results to the user. Thus, the manga / novel plot search system according to this embodiment can search for and provide specific plot developments or scenes of manga or novels based on user queries.

[0072] The reception unit receives user queries. For example, the reception unit can accept vague queries entered by users. Specifically, even if a user enters vague expressions such as "a scene where the protagonist is in danger" or "a moving ending," the reception unit can accept them. The reception unit receives queries through the user interface and sends the query content to the analysis unit. The user interface includes text input fields and voice input functions, designed to allow users to easily enter queries. Furthermore, when receiving user queries, the reception unit can provide auxiliary functions to reduce query ambiguity. For example, it can provide a function that automatically completes relevant keywords and phrases when the user enters a query. It can also suggest appropriate queries to the user by referring to past search history and popular queries. This allows the reception unit to help users enter more specific and appropriate queries, improving the overall search accuracy of the system.

[0073] The analysis unit analyzes queries received by the reception unit and performs a detailed analysis of the content of manga and novels. For example, the analysis unit uses generative AI to analyze the entire text of a work. Specifically, the generative AI utilizes natural language processing technology to analyze the text data of the work and extract information such as characters, story progression, and emotional changes. Based on keywords and phrases included in the query, the generative AI identifies relevant parts of the work and performs a detailed analysis. For example, in response to the query "scene where the protagonist is in danger," the generative AI identifies the scene in the work where the protagonist faces a difficult situation and analyzes the detailed content of that scene. Furthermore, the analysis unit can not only analyze the entire text of a work but also analyze metadata and related information of the work. For example, it analyzes information such as the genre, author, publication date, and rating of the work and uses it as auxiliary information to provide appropriate search results for queries. As a result, the analysis unit can perform highly accurate analysis of user queries and improve the overall search accuracy of the system.

[0074] The identification unit identifies specific plot developments or scenes based on the analysis performed by the analysis unit. For example, the identification unit can use a generative AI to identify specific plot developments or scenes. Specifically, the generative AI executes an algorithm to identify specific plot developments or scenes based on the data provided by the analysis unit. For example, in response to a query such as "emotional ending," the generative AI identifies the ending portion of the work and then identifies scenes within it that contain emotionally moving elements. The identification unit can use the generative AI's algorithm to identify specific plot developments or scenes within the work with high accuracy. Furthermore, the identification unit can add relevant information to the identified plot developments or scenes. For example, it can add information such as the context before and after the identified scene, or changes in the characters' emotions, to help the search unit provide more appropriate search results. This allows the identification unit to perform highly accurate identification of user queries and improve the overall search accuracy of the system.

[0075] The search unit searches for works based on plot developments and scenes identified by the identification unit. The search unit can, for example, use a generative AI to search for works based on specific plot developments and scenes. Specifically, the generative AI executes an algorithm to search for relevant works based on the data provided by the identification unit. For example, in response to a query such as "a scene where the protagonist is in danger," the generative AI searches for works containing the scene identified by the identification unit and provides detailed information about those works. The search unit can perform highly accurate searches for user queries using the generative AI's algorithm. Furthermore, the search unit can add relevant information when providing search results to the user. For example, it can add information such as a summary of the work, its rating, and excerpts of relevant scenes to the search results to make them easier for the user to understand. This allows the search unit to perform highly accurate searches for user queries and improve the overall search accuracy of the system.

[0076] The service provider provides the results searched by the search provider. For example, the service provider can provide search results to the user. Specifically, the service provider displays search results through a user interface, making them easily accessible to the user. The user interface is designed to display search results in a visually clear manner and is designed to allow users to quickly review the results. Furthermore, the service provider can provide relevant information that the user might be interested in in relation to the search results. For example, it can suggest other works related to the search results, other works by the same author, or works in related genres. In addition, the service provider can collect user feedback and continuously improve the accuracy and presentation of search results. This allows the service provider to provide users with high-quality search results and improve the overall user experience of the system.

[0077] The interface unit can provide a user interface. For example, the interface unit can provide a graphical user interface. The interface unit can also provide a voice interface. This makes it easier for the user to operate the system. Some or all of the above-described processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's operation history into the AI ​​and have the AI ​​perform processing to provide the optimal interface.

[0078] The security unit can encrypt and anonymize data. For example, the security unit can encrypt data using encryption algorithms such as AES or RSA. The security unit can also anonymize data using methods such as data masking or pseudo-anonymization. This ensures that user data is securely protected. Some or all of the above processes performed by the security unit may be carried out using AI, for example, or not. For example, the security unit can input methods for data encryption and anonymization into the AI ​​and have the AI ​​perform the process of selecting the optimal method.

[0079] The reception desk can estimate the user's emotions and adjust how queries are processed based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick query entry. This provides a query processing method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk can analyze the user's past query history and select the optimal query reception method. For example, the reception desk can automatically display queries that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest queries to be used during specific time periods based on the user's past query history. This allows the reception desk to provide the optimal query reception method based on the user's past query history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past query history into an AI and have the AI ​​perform the processing to select the optimal query reception method.

[0081] The reception desk can filter queries based on the user's current areas of interest when they are received. For example, the reception desk can prioritize receiving relevant queries based on the genres the user has recently searched for. The reception desk can also analyze the user's social media activity and suggest queries related to their current areas of interest. Furthermore, the reception desk can receive relevant queries based on the newsletters the user subscribes to and the authors they follow. This allows the reception desk to prioritize receiving relevant queries based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current areas of interest into the AI ​​and have the AI ​​perform the filtering process.

[0082] The reception desk can estimate the user's emotions and determine the priority of queries to accept based on the estimated emotions. For example, if the user is excited, the reception desk may prioritize entertaining queries. If the user is calm, the reception desk may also prioritize academic queries. Furthermore, if the user is tired, the reception desk may prioritize relaxing queries. This provides query prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception desk can prioritize receiving queries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize queries related to works in that region. Furthermore, if the user is traveling, the reception desk can prioritize queries related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize queries related to their home area. This allows the reception desk to prioritize receiving queries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into an AI and have the AI ​​perform the processing necessary to prioritize receiving highly relevant queries.

[0084] The reception desk can analyze a user's social media activity when receiving a query and accept relevant queries. For example, the reception desk can prioritize relevant queries based on posts the user has recently "liked." It can also prioritize relevant queries based on authors and artists the user follows. Furthermore, the reception desk can prioritize relevant queries based on topics in online communities the user participates in. This allows for the priority acceptance of relevant queries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI and have the AI ​​perform the processing to prioritize the acceptance of relevant queries.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows for the presentation of the analysis to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the work during the analysis. For example, the analysis unit will perform a detailed analysis of popular works. The analysis unit can also perform a concise analysis of new or unrated works. Furthermore, the analysis unit can perform a special analysis of works that the user is particularly interested in. This allows the level of detail of the analysis to be adjusted based on the importance of the work. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the work into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of the work during analysis. For example, in the case of manga, the analysis unit can apply an image analysis algorithm. In the case of novels, the analysis unit can also apply a text analysis algorithm. Furthermore, in the case of light novels, the analysis unit can apply an algorithm that analyzes both images and text. This allows the analysis algorithm to be applied according to the category of the work. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the work into the AI ​​and have the AI ​​perform the process of selecting the analysis algorithm to apply.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. This allows for analysis lengths tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit can determine the priority of analysis based on the publication date of the works during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent works. It may also postpone the analysis of classic works. Furthermore, the analysis unit may prioritize the analysis of works that the user is particularly interested in. This allows the analysis priority to be determined based on the publication date of the works. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the publication dates of the works into the AI ​​and have the AI ​​perform the processing to determine the analysis priority.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the works during the analysis process. For example, the analysis unit may prioritize analyzing works that are most relevant to the user's search query. It can also prioritize analyzing works that are highly relevant based on the user's past search history. Furthermore, it can prioritize analyzing works that are highly relevant based on the user's current areas of interest. This allows the order of analysis to be adjusted based on the relevance of the works. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the works into the AI ​​and have the AI ​​perform the process of adjusting the order of analysis.

[0091] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated emotions. For example, if the user is relaxed, the identification unit can apply detailed identification criteria. If the user is in a hurry, the identification unit can also apply concise identification criteria. Furthermore, if the user is excited, the identification unit can apply visually appealing identification criteria. This allows for the provision of identification criteria that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The identification unit can improve the accuracy of identification by considering the relationships between works during the identification process. For example, the identification unit can improve accuracy by considering the relationships when identifying works in the same series. It can also improve accuracy by considering the relationships when identifying works by the same author. Furthermore, it can improve accuracy by considering the relationships when identifying works in the same genre. In this way, the accuracy of identification can be improved by considering the relationships between works. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the relationships between works into AI and have AI perform processing to improve the accuracy of identification.

[0093] The identification unit can perform identification by considering the attribute information of the author of the work. For example, the identification unit can perform identification by considering the trends of the author's past works. The identification unit can also perform identification by considering the author's style and themes. Furthermore, the identification unit can perform identification by considering the author's career and background information. This allows for identification by considering the attribute information of the author of the work. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the author's attribute information into AI and have the AI ​​perform the processing for identification.

[0094] The identification unit can estimate the user's emotions and adjust the order in which the identification results are displayed based on the estimated emotions. For example, if the user is relaxed, the identification unit may prioritize displaying detailed identification results. It may also prioritize displaying concise identification results if the user is in a hurry. Furthermore, if the user is excited, the identification unit may prioritize displaying visually appealing identification results. This allows for the provision of identification results tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The identification unit can perform identification while considering the geographical distribution of the works. For example, the identification unit can identify works that are popular in a particular region. It can also identify works that were published in a particular region. Furthermore, it can identify works that have themes related to a particular region. This allows for identification while considering the geographical distribution of the works. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the geographical distribution of the works into the AI ​​and have the AI ​​perform the processing for identification.

[0096] The identification unit can improve the accuracy of its identification by referring to related literature for the work during the identification process. For example, the identification unit can improve the accuracy of its identification by referring to related literature for the work. It can also improve the accuracy of its identification by referring to reviews and ratings of the work. Furthermore, the identification unit can improve the accuracy of its identification by referring to related news articles for the work. In this way, it can improve the accuracy of its identification by referring to related literature for the work. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input related literature for the work into the AI ​​and have the AI ​​perform processing to improve the accuracy of its identification.

[0097] The search unit can estimate the user's emotions and adjust the search method based on the estimated emotions. For example, if the user is relaxed, the search unit can provide detailed search results. If the user is in a hurry, the search unit can also provide concise search results that get straight to the point. Furthermore, if the user is excited, the search unit can provide visually appealing search results. This allows for a search method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The search unit can improve search accuracy by considering the relationships between works during the search process. For example, when searching for works in the same series, the search unit can improve accuracy by considering the relationships between works. Furthermore, when searching for works by the same author, the search unit can improve accuracy by considering the relationships between works. In this way, the search unit can improve search accuracy by considering the relationships between works. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relationships between works into the AI ​​and have the AI ​​perform processing to improve search accuracy.

[0099] The search unit can perform searches while considering the attribute information of the work's author. For example, the search unit can perform searches while considering the trends of the author's past works. It can also perform searches while considering the author's style and themes. Furthermore, the search unit can perform searches while considering the author's career and background information. This allows the search to be performed while considering the attribute information of the work's author. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the author's attribute information into AI and have the AI ​​perform the processing for performing the search.

[0100] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit may prioritize displaying detailed search results. If the user is in a hurry, the search unit may also prioritize displaying concise search results. Furthermore, if the user is excited, the search unit may also prioritize displaying visually appealing search results. This allows the search unit to provide search results that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The search unit can perform searches while considering the geographical distribution of works. For example, the search unit can search for works that are popular in a particular region. It can also search for works that were published in a particular region. Furthermore, it can search for works that have themes related to a particular region. This allows the search to be performed while considering the geographical distribution of works. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the geographical distribution of works into AI and have AI perform the processing for performing the search.

[0102] The search unit can improve the accuracy of its search by referring to related literature for the work during the search process. For example, the search unit can improve the accuracy of its search by referring to related literature for the work. It can also improve the accuracy of its search by referring to reviews and ratings of the work. Furthermore, the search unit can improve the accuracy of its search by referring to news articles related to the work. In this way, it can improve the accuracy of its search by referring to related literature for the work. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature for the work into the AI ​​and have the AI ​​perform processing to improve the accuracy of the search.

[0103] The service provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can also provide concise information that gets straight to the point. Furthermore, if the user is excited, the service provider can provide visually appealing information. This allows the service provider to provide information presentation methods that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The information provider can provide optimal information by referring to the user's past search history at the time of delivery. For example, the information provider can provide relevant information based on queries the user has searched for in the past. The information provider can also predict and provide information that the user will use at a specific time of day based on the user's past search history. Furthermore, the information provider can analyze the user's past search history and provide the most relevant information. This allows the information provider to provide optimal information based on the user's past search history. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's past search history into AI and have the AI ​​perform the processing to provide optimal information.

[0105] The information provider can customize information based on the user's current areas of interest at the time of delivery. For example, the provider can provide relevant information based on genres the user has recently searched for. The provider can also analyze the user's social media activity and provide information related to their current areas of interest. Furthermore, the provider can provide relevant information based on newsletters the user subscribes to and authors the user follows. This allows the information to be customized based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's current areas of interest into AI and have AI perform the processing to customize the information.

[0106] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is excited, the service provider may prioritize providing highly entertaining information. If the user is calm, the service provider may also prioritize providing academic information. Furthermore, if the user is tired, the service provider may also prioritize providing relaxing information. This allows for the provision of information prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the provider can provide information related to that region. Furthermore, if the user is traveling, the provider can provide information related to their travel destination. Additionally, if the user is at home, the provider can provide information related to their home area. This allows the provider to provide optimal information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location into AI and have AI perform the processing necessary to provide optimal information.

[0108] The information provider can analyze the user's social media activity and provide information at the time of delivery. For example, the provider can provide relevant information based on posts the user has recently "liked". It can also provide relevant information based on authors and artists the user follows. Furthermore, it can provide relevant information based on topics in online communities the user participates in. This allows the provider to provide relevant information based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity into AI and have the AI ​​perform the processing to provide information.

[0109] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the interface unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the interface unit can provide a simple and highly visible interface to facilitate the input process. This allows for the provision of an interface display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using AI, or not. For example, the interface unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize displaying interface designs that the user has previously preferred. The interface unit can also prioritize displaying specific functions based on the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and provide the most user-friendly interface. This allows the interface unit to provide the optimal interface display method based on the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history into AI and have the AI ​​perform the processing to select the optimal display method.

[0111] The interface unit can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, if the user is tense, the interface unit can provide simple and intuitive operation procedures. If the user is enjoying themselves, the interface unit can also provide customizable operation procedures. Furthermore, if the user is tired, the interface unit can provide procedures that require minimal operation. This allows for the provision of interface operation procedures tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using AI, or not. For example, the interface unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. This allows the interface unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into the AI ​​and have the AI ​​perform the processing to select the optimal display method.

[0113] The security unit can estimate the user's emotions and adjust the data encryption method based on the estimated emotions. For example, if the user is stressed, the security unit can apply a strong encryption method. Alternatively, if the user is relaxed, it can apply a standard encryption method. Furthermore, if the user is in a hurry, the security unit can apply a rapid encryption method. This allows for data encryption methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI, or not. For example, the security unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The security unit can select the optimal encryption method when encrypting data by referring to the user's past security history. For example, the security unit may prioritize applying encryption methods previously used by the user. It can also select a specific encryption method based on the user's past security history. Furthermore, the security unit can analyze the user's past security history and provide the most appropriate encryption method. This allows the security unit to provide the optimal encryption method based on the user's past security history. Some or all of the above processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the user's past security history into an AI and have the AI ​​perform the process of selecting the optimal encryption method.

[0115] The security unit can estimate the user's emotions and adjust the data anonymization method based on the estimated emotions. For example, if the user is stressed, the security unit can apply a strong anonymization method. If the user is relaxed, the security unit can also apply a standard anonymization method. Furthermore, if the user is in a hurry, the security unit can apply a rapid anonymization method. This allows for data anonymization methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not. For example, the security unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The security unit can select the optimal encryption method when encrypting data, taking into account the user's geographical location. For example, if the user is in a specific region, the security unit can select an encryption method based on the security regulations of that region. Furthermore, if the user is traveling, the security unit can select an encryption method based on the security regulations of the travel destination. Additionally, if the user is at home, the security unit can select an encryption method based on the home's security environment. This allows the security unit to provide the optimal encryption method based on the user's geographical location. Some or all of the above processing in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the user's geographical location information into an AI and have the AI ​​perform the process of selecting the optimal encryption method.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The reception desk can analyze a user's past search history and automatically suggest relevant queries based on the queries the user has previously searched for. For example, if a user has frequently searched for works in the "fantasy" genre in the past, the reception desk will prioritize suggesting queries related to the "fantasy" genre. It can also suggest new works or related works by a particular author if the user has frequently searched for their works. Furthermore, if a user is interested in a specific theme (e.g., "adventure" or "romance"), it can suggest queries related to that theme. This allows for more personalized query suggestions based on the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past search history into an AI and have the AI ​​perform the process of selecting the most suitable query suggestions.

[0119] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing analysis results. This allows for providing an level of detail in the analysis that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated emotions. For example, if the user is relaxed, detailed identification criteria can be applied. If the user is in a hurry, concise identification criteria can be applied. Furthermore, if the user is excited, visually appealing identification criteria can be applied. This allows for the provision of identification criteria that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The search unit can estimate the user's emotions and adjust the search method based on the estimated emotions. For example, if the user is relaxed, it can provide detailed search results. If the user is in a hurry, it can provide concise search results that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing search results. This allows for a search method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, concise information can be provided. Furthermore, if the user is excited, visually appealing information can be provided. This allows for the presentation of information in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0123] The reception desk can prioritize receiving queries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize queries related to works in that region. If the user is traveling, it can also prioritize queries related to their travel destination. Furthermore, if the user is at home, it can prioritize queries related to their home area. This allows the reception desk to prioritize receiving queries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into the AI ​​and have the AI ​​perform the processing to prioritize receiving queries that are highly relevant.

[0124] The search unit can improve search accuracy by considering the relationships between works during the search process. For example, it can improve accuracy by considering the relationships between works when searching for works in the same series. It can also improve accuracy by considering the relationships between works when searching for works by the same author. Furthermore, it can improve accuracy by considering the relationships between works when searching for works in the same genre. In this way, the search unit can improve search accuracy by considering the relationships between works. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relationships between works into the AI ​​and have the AI ​​perform processing to improve search accuracy.

[0125] The information provider can provide optimal information by referring to the user's past search history at the time of delivery. For example, it can provide relevant information based on queries the user has searched for in the past. It can also predict and provide information that the user will use at a specific time of day based on the user's past search history. Furthermore, it can analyze the user's past search history and provide the most relevant information. This allows the information provider to provide optimal information based on the user's past search history. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's past search history into AI and have the AI ​​perform the processing to provide optimal information.

[0126] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the interface unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into the AI ​​and have the AI ​​perform the processing to select the optimal display method.

[0127] The security unit can select the optimal encryption method when encrypting data by referring to the user's past security history. For example, it can prioritize applying encryption methods previously used by the user. It can also select a specific encryption method based on the user's past security history. Furthermore, it can analyze the user's past security history and provide the most appropriate encryption method. This allows the security unit to provide the optimal encryption method based on the user's past security history. Some or all of the above processes in the security unit may be performed using AI, for example, or not. For example, the security unit can input the user's past security history into an AI and have the AI ​​perform the process of selecting the optimal encryption method.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk receives user queries. The reception desk can, for example, receive ambiguous queries entered by users. Step 2: The analysis unit analyzes the queries received by the reception unit and performs a detailed analysis of the content of the manga or novel. For example, the analysis unit uses a generative AI to analyze the entire text of the work. Step 3: The identification unit identifies specific developments or scenes based on the content analyzed by the analysis unit. The identification unit can, for example, use a generative AI to identify specific developments or scenes. Step 4: The search unit searches for works based on the plot developments and scenes identified by the identification unit. The search unit can, for example, use a generative AI to search for works based on specific plot developments and scenes. Step 5: The providing unit provides the results found by the searching unit. For example, the providing unit can provide the search results to the user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, search unit, provision unit, interface unit, and security unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user queries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes queries using generation AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies specific developments or scenes. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for works based on the identified developments or scenes. The provision unit is implemented by the output device 40 of the smart device 14 and provides search results to the user. The interface unit is implemented by the control unit 46A of the smart device 14 and provides a user interface. The security unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs data encryption and anonymization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, search unit, provision unit, interface unit, and security unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user queries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes queries using generation AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies specific developments or scenes. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for works based on the identified developments or scenes. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides search results to the user. The interface unit is implemented by the control unit 46A of the smart glasses 214 and provides a user interface. The security unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs data encryption and anonymization. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, search unit, provision unit, interface unit, and security unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user queries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes queries using generation AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies specific developments or scenes. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for works based on the identified developments or scenes. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides search results to the user. The interface unit is implemented by the control unit 46A of the headset terminal 314 and provides a user interface. The security unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs data encryption and anonymization. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, search unit, provision unit, interface unit, and security unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user queries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes queries using generation AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies specific developments or scenes. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for works based on the identified developments or scenes. The provision unit is implemented by the speaker 240 of the robot 414 and provides search results to the user. The interface unit is implemented by the control unit 46A of the robot 414 and provides a user interface. The security unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs data encryption and anonymization. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) A reception desk that accepts user queries, The analysis unit analyzes the queries received by the reception unit and performs a detailed analysis of the content of the manga and novels. An identification unit identifies a specific development or scene based on the content analyzed by the aforementioned analysis unit, A search unit searches for works based on the plot development and scenes identified by the aforementioned identification unit, The system comprises a providing unit that provides the results retrieved by the search unit. A system characterized by the following features. (Note 2) It includes an interface unit that provides a user interface. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a security unit that encrypts and anonymizes data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's sentiment and adjusts how queries are accepted based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past query history and select the optimal query acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When a query is received, it is filtered based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's sentiment and determines the priority of queries to accept based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving queries, the system prioritizes accepting queries that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a query is received, the system analyzes the user's social media activity and accepts relevant queries. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the publication date of the works. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the works. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned identification unit is It estimates the user's sentiment and adjusts the identification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned identification unit is When identifying artworks, the interrelationships between them are taken into consideration to improve the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned identification unit is During identification, the author's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned identification unit is It estimates the user's sentiment and adjusts the order in which the identification results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned identification unit is During identification, the geographical distribution of the artworks is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned identification unit is During identification, we refer to related literature to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's sentiment and adjusts the search method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When searching, consider the relationships between works to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, the search will take into account the author's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned search unit, When searching, the search will take into account the geographical distribution of the works. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned search unit, When searching, referencing related literature for the work improves the accuracy of the search. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we refer to the user's past search history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, customize it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 2, characterized by the features described herein. (Note 36) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned security unit is It estimates the user's emotions and adjusts the data encryption method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned security unit is When encrypting data, the system selects the optimal encryption method by referring to the user's past security history. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned security unit is We estimate the user's sentiment and adjust the data anonymization method based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned security unit is When encrypting data, the optimal encryption method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user queries, The analysis unit analyzes the queries received by the reception unit and performs a detailed analysis of the content of the manga and novels. An identification unit identifies a specific development or scene based on the content analyzed by the aforementioned analysis unit, A search unit searches for works based on the plot development and scenes identified by the aforementioned identification unit, The system comprises a providing unit that provides the results retrieved by the search unit. A system characterized by the following features.

2. It includes an interface unit that provides a user interface. The system according to feature 1.

3. It is equipped with a security unit that encrypts and anonymizes data. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's sentiment and adjusts how queries are accepted based on the estimated sentiment. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past query history and select the optimal query acceptance method. The system according to feature 1.

6. The aforementioned reception unit is When a query is received, it is filtered based on the user's current areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's sentiment and determines the priority of queries to accept based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned reception unit is When receiving queries, the system prioritizes accepting queries that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When a query is received, the system analyzes the user's social media activity and accepts relevant queries. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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