system

Abstract

The system according to this embodiment aims to suggest new manga based on the user's preferences. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a dialogue unit, a proposal unit, and a contract unit. The reception unit grasps the user's preferences through preliminary questions. The analysis unit analyzes the information received by the reception unit to grasp the user's preferences. The dialogue unit asks the user in a dialogue format what kind of manga they would like to read, based on the user's preferences grasped by the analysis unit. The proposal unit proposes the most suitable manga from its inventory based on the information obtained by the dialogue unit. The contract unit manages the subscription service contract.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a user to find a new comic that suits their preferences, and there is a problem that the user only reads stereotyped comics.

[0005] The system according to the embodiment aims to propose a new comic based on the user's preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a dialogue unit, a proposal unit, and a contract unit. The reception unit understands the user's preferences through preliminary questions. The analysis unit analyzes the information received by the reception unit to understand the user's preferences. The dialogue unit asks the user, in a dialogue format, what kind of manga they would like to read, based on the user's preferences understood by the analysis unit. The proposal unit proposes the most suitable manga from its inventory based on the information obtained by the dialogue unit. The contract unit manages the subscription service contract. [Effects of the Invention]

[0007] The system according to this embodiment can suggest new manga based on the user's preferences. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied 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 proposed system according to an embodiment of the present invention is a system that grasps the user's preferences through preliminary questions and then proposes recommendations from its inventory through dialogue about what the user wants to read. In this proposal system, the user answers preliminary questions, and a generating AI analyzes that information to grasp the user's preferences. Next, the generating AI asks the user in a dialogue format what kind of manga they want to read based on their preferences. After that, the generating AI proposes the most suitable manga for the user from its inventory, and the user subscribes to a subscription service in units of 10 or 20 books per month. Through this system, the user can enjoy new genres of manga and broaden their world through manga. Furthermore, by utilizing the generating AI, it becomes possible to make optimal suggestions tailored to the user's preferences, and it is expected to gain a dominant market share in the ebook field. For example, the user answers preliminary questions. For example, by answering questions such as "What are your favorite genres?" or "What manga have you read recently?", the system grasps the user's preferences. This information is input into the generating AI and analyzed. Next, the generating AI asks the user in a dialogue format what kind of manga they want to read based on their preferences. For example, it asks questions such as "Do you like adventure stories?" or "Do you want to read romance stories?" to grasp the user's specific desires. The AI ​​then suggests the most suitable manga from its inventory to the user. For example, it might suggest, "How about this adventure manga?" or "I recommend this romance manga." The user selects the suggested manga and subscribes to a monthly subscription service for 10 or 20 titles. This system allows users to enjoy manga in new genres and broaden their horizons through manga. Furthermore, by utilizing the AI, it becomes possible to make optimal suggestions tailored to the user's preferences, and is expected to gain a dominant market share in the ebook sector. In this way, the suggestion system can recommend the most suitable manga based on the user's preferences.

[0029] The proposal system according to this embodiment comprises a reception unit, an analysis unit, a dialogue unit, a proposal unit, and a contract unit. The reception unit understands the user's preferences through pre-questions. For example, the reception unit can understand the user's preferences when the user answers the pre-questions. The analysis unit analyzes the information received by the reception unit to understand the user's preferences. For example, the analysis unit can analyze the user's answers using a generative AI to understand the user's preferences. The dialogue unit asks the user in a dialogue format what kind of manga they would like to read, based on the user's preferences understood by the analysis unit. For example, the dialogue unit can ask the user questions such as "Do you like adventure stories?" or "Do you want to read romance stories?" using a generative AI to understand the user's specific desires. The proposal unit proposes the most suitable manga from its inventory based on the information obtained by the dialogue unit. For example, the proposal unit can make suggestions such as "How about this adventure manga?" or "This romance manga is highly recommended" using a generative AI. The contract unit manages the subscription service contract. The contracts section can manage subscription services where users contract for, for example, 10 or 20 books per month. This allows the proposal system according to the embodiment to suggest the most suitable manga based on the user's preferences.

[0030] The reception department understands user preferences through pre-questions. Specifically, when a user accesses the system, they are first presented with pre-questions. These questions are designed to understand the user's preferences and interests in detail, and include questions such as, "What are your favorite genres?", "What manga have you read recently?", and "What kind of stories do you like?". By answering these questions, users can communicate their preferences and interests to the system. Furthermore, the reception department collects user responses in real time and stores them in a database. This data is used by the subsequent analysis and dialogue departments, so it is important that it is collected accurately and in detail. The reception department processes user responses quickly and prepares them for the next step. In addition, the reception department creates individual profiles based on user responses, building a foundation for providing a customized experience for each user. Thus, the reception department plays a crucial role in accurately understanding user preferences and achieving personalization throughout the entire system.

[0031] The analysis unit analyzes the information received by the reception unit to understand user preferences. Specifically, it uses generative AI to analyze user responses. The generative AI utilizes natural language processing technology to analyze user responses as text data and extract user preferences and interests. For example, if a user responds, "I like adventure stories," the generative AI extracts the keyword "adventure," understanding that the user is interested in the adventure genre. It also analyzes the emotions and nuances contained in the user's responses to understand more detailed preferences. Based on this information, the analysis unit updates the user profile and provides data for use in the next step. Furthermore, the analysis unit can also predict user preferences by referring to past data and the trends of other users. For example, it can list works that the user might be interested in by referring to manga that other users who gave similar answers have liked. In this way, the analysis unit can accurately understand user preferences and improve the accuracy and effectiveness of the entire system.

[0032] The dialogue unit, based on the user's preferences identified by the analysis unit, asks the user in a conversational format what kind of manga they would like to read. Specifically, it uses a generative AI to ask the user questions such as, "Do you like adventure stories?" or "Do you want to read romance stories?" The generative AI dynamically generates the next question in response to the user's answer, and the conversation continues. For example, if the user answers, "I like adventure stories," the generative AI will continue with more specific questions such as, "What kind of adventure stories do you like?" or "Do you like adventure stories that feature a specific character?" This allows the dialogue unit to understand the user's specific wishes and interests in detail. Furthermore, the dialogue unit also analyzes the user's reactions, response speed, and emotional changes to improve the quality of the conversation. For example, it asks more in-depth questions about themes the user shows interest in, and switches to different questions about themes the user shows no interest in. In this way, the dialogue unit can understand more detailed preferences through conversation with the user and improve the accuracy of the suggestions made by the subsequent suggestion unit.

[0033] The suggestion department, based on information obtained by the dialogue department, proposes the most suitable manga from its inventory. Specifically, it uses generative AI to make suggestions such as, "How about this adventure manga?" or "This romance manga is highly recommended." The generative AI selects and proposes the most suitable manga based on the user's preferences and interests. For example, if the user likes adventure stories, the generative AI searches for adventure manga in its inventory and proposes them to the user. The suggestion department also considers the user's past browsing history and ratings to provide more personalized suggestions. For example, it can increase user satisfaction by suggesting works similar to manga that the user has previously given high ratings to. Furthermore, the suggestion department can update its suggestions in real time and adjust them according to the user's response. For example, if the user is not interested in a suggested manga, it will suggest a different work. The suggestion department also collects user feedback and continuously improves the accuracy of its suggestion algorithm. As a result, the suggestion department can propose the most suitable manga to the user and increase their satisfaction.

[0034] The Contracts Department manages subscription service contracts. Specifically, it manages subscription services where users subscribe to services in units of 10 or 20 books per month. The Contracts Department stores user contract information in a database and updates contract details in real time. For example, if a user applies for a new contract, the Contracts Department immediately reflects that information and updates the services available to the user. The Contracts Department also monitors users' contract status and supports contract renewal and cancellation procedures. For example, it sends renewal notices to users whose contracts are nearing expiration to ensure a smooth continuation of their contracts. Furthermore, the Contracts Department can analyze user usage patterns and propose the most suitable plan. For example, if a user subscribed to a plan of 10 books per month frequently makes additional purchases, the Contracts Department will propose a plan that includes a larger number of books. In this way, the Contracts Department can provide flexible services tailored to user needs and improve user satisfaction.

[0035] The suggestion unit can use generative AI to suggest the most suitable manga based on the user's preferences. For example, the suggestion unit can use generative AI to make suggestions such as "How about this adventure manga?" or "This romance manga is highly recommended" based on the user's preferences. This makes it possible to make optimal suggestions based on the user's preferences by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the suggestion unit may be performed using generative AI, or it may be performed without using generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's preferences as input and outputs the most suitable manga.

[0036] The dialogue unit can interact with the user using a generative AI and ask what kind of manga the user wants to read. For example, the dialogue unit can use a generative AI to ask the user questions such as, "Do you like adventure stories?" or "Do you want to read romance stories?" to understand the user's specific preferences. Thus, by using a generative AI, the user's specific preferences can be understood. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can conduct a dialogue using a generative AI model that takes the user's preferences as input and outputs the dialogue content.

[0037] The reception desk can analyze the user's past response history and select the optimal question order. For example, the reception desk can analyze patterns in questions the user has answered in the past and present questions in the most effective order. The reception desk can also prioritize questions related to specific genres based on the user's past response history. Furthermore, the reception desk can automatically select highly relevant questions based on the user's response history. This allows for efficient information collection by selecting the optimal question order based on past response history. 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 response history data into AI and have the AI ​​select the optimal question order.

[0038] The reception desk can filter questions based on the user's current interests when they answer pre-submitted questions. For example, the reception desk can prioritize relevant questions based on the genre of manga the user has recently read. It can also select questions related to a specific theme if the user has shown interest in that theme. Furthermore, the reception desk can dynamically change the content of questions based on the user's current interests. This allows for the collection of more relevant information by filtering questions based on the user's current interests. 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 user interest data into an AI and have the AI ​​perform the question filtering.

[0039] The reception desk can prioritize presenting highly relevant questions by considering the user's geographical location when they answer pre-submitted questions. For example, if the user lives in a specific region, the reception desk can prioritize presenting questions about manga related to that region. Similarly, if the user is traveling, the reception desk can prioritize presenting questions about manga related to their travel destination. Furthermore, the reception desk can select region-specific questions based on the user's geographical location. This allows for the presentation of more relevant questions by considering 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 data into an AI and have the AI ​​select highly relevant questions.

[0040] The reception desk can analyze the user's social media activity when they answer pre-submitted questions and suggest relevant questions. For example, the reception desk can suggest relevant questions based on comics the user has shared on social media. It can also suggest relevant questions based on the activity of accounts the user follows. Furthermore, the reception desk can analyze the user's interests on social media and select the most relevant questions. This allows for the suggestion of more relevant questions by analyzing 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 data into an AI and have the AI ​​select relevant questions.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the user's past subscription history. For example, the analysis unit can analyze the user's preferences based on data of manga the user has read in the past. The analysis unit can also analyze the user's interest in specific genres from their subscription history. Furthermore, the analysis unit can analyze the user's past subscription history and provide data to make optimal suggestions. This improves the accuracy of the analysis by referring to past subscription history. 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 user's subscription history data into AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0042] The analysis unit can apply different analysis methods depending on the user's area of ​​interest during analysis. For example, if the user is interested in adventure manga, the analysis unit can apply an analysis method specialized for adventure manga. Similarly, if the user is interested in romance manga, the analysis unit can apply an analysis method specialized for romance manga. Furthermore, if the user is interested in mystery manga, the analysis unit can apply an analysis method specialized for mystery manga. By applying different analysis methods to each user's area of ​​interest, more accurate analysis becomes possible. 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 user's area of ​​interest data into the AI ​​and have the AI ​​execute the application of different analysis methods.

[0043] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user lives in a specific region, the analysis unit will prioritize analyzing manga data related to that region. Also, if the user is traveling, the analysis unit can prioritize analyzing manga data related to the travel destination. Furthermore, the analysis unit can apply region-specific analysis methods based on the user's geographical location information. This makes it possible to perform more relevant analysis by considering the user's geographical location information. 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 user's geographical location information data into AI and have the AI ​​perform highly relevant analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can analyze the user's preferences based on relevant literature the user has read in the past. The analysis unit can also analyze the user's interests in specific genres from the user's relevant literature. Furthermore, the analysis unit can analyze the user's relevant literature and provide data to make optimal suggestions. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's relevant literature data into AI and have the AI ​​perform the analysis to improve accuracy.

[0045] The dialogue unit can select the optimal dialogue scenario during a conversation by referring to the user's past dialogue history. For example, the dialogue unit can analyze patterns of past conversations with the user and select the most effective dialogue scenario. Furthermore, the dialogue unit can prioritize conversations related to specific genres based on the user's past dialogue history. The dialogue unit can also automatically select highly relevant dialogue scenarios based on the user's dialogue history. This allows the optimal dialogue scenario to be selected by referring to past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's dialogue history data into an AI and have the AI ​​select the optimal dialogue scenario.

[0046] The dialogue unit can apply different dialogue algorithms to the user's areas of interest during a conversation. For example, if the user is interested in adventure manga, the dialogue unit can apply a dialogue algorithm specifically for adventure manga. If the user is interested in romance manga, the dialogue unit can apply a dialogue algorithm specifically for romance manga. If the user is interested in mystery manga, the dialogue unit can apply a dialogue algorithm specifically for mystery manga. By applying different dialogue algorithms to each user's areas of interest, more appropriate conversations become possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's areas of interest data into the AI ​​and have the AI ​​apply different dialogue algorithms.

[0047] The dialogue unit can conduct conversations while considering the user's geographical location. For example, if the user lives in a specific region, the dialogue unit will prioritize conversations about manga related to that region. Also, if the user is traveling, the dialogue unit will prioritize conversations about manga related to the travel destination. Furthermore, the dialogue unit can conduct region-specific conversations based on the user's geographical location. This makes it possible to conduct more relevant conversations by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location data into AI and have the AI ​​execute highly relevant conversations.

[0048] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant dialogue content. For example, the dialogue unit can conduct relevant conversations based on comics the user has shared on social media. It can also conduct relevant conversations based on the activity of accounts the user follows. Furthermore, the dialogue unit can analyze the user's interests on social media and provide optimal dialogue content. By analyzing social media activity, it can provide more relevant dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media data into AI and have the AI ​​provide relevant dialogue content.

[0049] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past subscription history. For example, the suggestion unit can make suggestions tailored to the user's preferences based on data of manga the user has read in the past. It can also analyze the user's subscription history to identify their interests in specific genres and make suggestions accordingly. Furthermore, the suggestion unit can analyze the user's past subscription history and provide data to make optimal suggestions. This improves the accuracy of suggestions by referencing past subscription history. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's subscription history data into an AI and have the AI ​​perform the task of improving the accuracy of its suggestions.

[0050] The suggestion unit can apply different suggestion algorithms depending on the user's area of ​​interest when making suggestions. For example, if the user is interested in adventure manga, the suggestion unit can apply a suggestion algorithm specialized for adventure manga. If the user is interested in romance manga, the suggestion unit can apply a suggestion algorithm specialized for romance manga. If the user is interested in mystery manga, the suggestion unit can apply a suggestion algorithm specialized for mystery manga. By applying different suggestion algorithms to each user's area of ​​interest, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's area of ​​interest data into an AI and have the AI ​​apply different suggestion algorithms.

[0051] The suggestion unit can make suggestions while considering the user's geographical location. For example, if the user lives in a specific region, the suggestion unit will prioritize suggesting manga related to that region. Also, if the user is traveling, the suggestion unit can prioritize suggesting manga related to their travel destination. Furthermore, the suggestion unit can make region-specific suggestions based on the user's geographical location. This makes it possible to make more relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​make highly relevant suggestions.

[0052] The suggestion unit can analyze the user's social media activity and provide relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on comics the user has shared on social media. It can also make relevant suggestions based on the activity of accounts the user follows. Furthermore, the suggestion unit can analyze the user's interests on social media and provide optimal suggestions. This allows for more relevant suggestions by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into AI and have the AI ​​provide relevant suggestions.

[0053] The contracts department can propose the optimal contract plan at the time of contract by referring to the user's past contract history. For example, the contracts department can propose the optimal contract plan based on the plans the user has contracted in the past. The contracts department can also analyze the user's interest in specific plans from their contract history and make suggestions. Furthermore, the contracts department can analyze the user's past contract history and propose the optimal contract plan. In this way, the optimal contract plan can be proposed by referring to past contract history. Some or all of the above processes in the contracts department may be performed using AI, for example, or not using AI. For example, the contracts department can input the user's contract history data into AI and have the AI ​​propose the optimal contract plan.

[0054] The contracts department can propose the most suitable contract plan at the time of contracting, taking into account the user's geographical location. For example, if the user lives in a specific region, the contracts department will prioritize proposing contract plans related to that region. Similarly, if the user is traveling, the contracts department can prioritize proposing contract plans related to their travel destination. Furthermore, the contracts department can propose region-specific contract plans based on the user's geographical location. This allows for the proposal of more relevant contract plans by considering the user's geographical location. Some or all of the above processing in the contracts department may be performed using AI, or not. For example, the contracts department can input the user's geographical location data into an AI and have the AI ​​propose the most suitable contract plan.

[0055] A dedicated librarian can make optimal suggestions by referring to the user's past conversation history. For example, the dedicated librarian can analyze patterns in the user's past conversations and make the most effective suggestions. Furthermore, the dedicated librarian can prioritize suggestions related to specific genres based on the user's past conversation history. Additionally, the dedicated librarian can automatically select highly relevant suggestions based on the user's conversation history. This enables optimal suggestions by referring to past conversation history. Some or all of the above processes performed by the dedicated librarian may be carried out using AI, or not. For example, the dedicated librarian can input the user's conversation history data into an AI and have the AI ​​select the optimal suggestions.

[0056] A dedicated librarian can make optimal suggestions by taking into account the user's geographical location. For example, if the user lives in a specific region, the dedicated librarian will prioritize suggesting manga related to that region. Also, if the user is traveling, the dedicated librarian can prioritize suggesting manga related to the travel destination. Furthermore, the dedicated librarian can make region-specific suggestions based on the user's geographical location. This allows for more relevant suggestions by considering the user's geographical location. Some or all of the above processing by the dedicated librarian may be performed using AI, for example, or not. For example, the dedicated librarian can input the user's geographical location data into AI and have the AI ​​select the optimal suggestions.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The suggestion system can consider not only the user's subscription history but also their viewing history to understand their preferences. For example, the reception department can collect data on videos the user has watched in the past, and the analysis department can analyze this data to understand the user's preferences. This allows the system to suggest more appropriate manga based on the genres and themes of videos the user has watched. Furthermore, by considering viewing history, the system can understand changes in the user's interests and make suggestions based on their latest preferences. For example, if a user has recently watched many action movies, the system can prioritize suggesting action manga. Similarly, if a user watches many documentaries, the system can suggest manga based on history and facts.

[0059] The suggestion system can analyze users' social media activity to understand their preferences. For example, the reception department collects data on content users share on social media and accounts they follow, and the analysis department analyzes this data to understand users' preferences. This allows the system to suggest more appropriate manga based on the genres and themes users show interest in on social media. Furthermore, by analyzing social media activity, the system can understand users' latest interests and provide optimal suggestions in real time. For example, if a user has recently shared a particular manga, the system can suggest works related to that manga. Also, if a user follows a particular artist, the system can prioritize suggesting works by that artist.

[0060] The suggestion system can consider not only the user's subscription history but also their purchase history to understand their preferences. For example, the reception department can collect data on products the user has purchased in the past, and the analysis department can analyze this data to understand the user's preferences. This allows the system to suggest more appropriate manga based on the genres and themes of products the user has purchased. Furthermore, by considering purchase history, the system can understand changes in the user's interests and make suggestions based on their latest preferences. For example, if a user has recently purchased many fantasy-related products, the system can prioritize suggesting fantasy manga. Similarly, if a user has purchased many science-related products, the system can suggest science-related manga.

[0061] The suggestion system can consider the user's geographical location to understand their preferences. For example, the reception unit can collect the user's current geographical location, and the analysis unit can analyze this data to understand the user's preferences. This allows the system to prioritize suggesting manga related to a specific region if the user lives there. Similarly, if the user is traveling, the system can suggest manga related to their travel destination. For instance, if the user lives in Japan, the system can suggest manga related to Japanese culture and history. If the user is traveling in Europe, the system can suggest manga related to European history and culture.

[0062] The suggestion system can consider not only the user's subscription history but also their reading history to understand their preferences. For example, the reception department can collect data on books the user has read in the past, and the analysis department can analyze this data to understand the user's preferences. This allows the system to suggest more appropriate manga based on the genres and themes of books the user has read. Furthermore, by considering the reading history, the system can understand changes in the user's interests and make suggestions based on their latest preferences. For example, if a user has recently been reading a lot of mystery novels, the system can prioritize suggesting mystery manga. Similarly, if a user has been reading a lot of fantasy novels, the system can suggest fantasy manga.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk understands the user's preferences through pre-questions. For example, the user's preferences can be understood by having them answer pre-questions. Step 2: The analysis unit analyzes the information received by the reception unit to understand the user's preferences. For example, it can use a generation AI to analyze the user's responses and understand their preferences. Step 3: The dialogue unit asks the user, in a conversational format, what kind of manga they want to read, based on the user's preferences identified by the analysis unit. For example, using a generative AI, it can ask the user questions such as, "Do you like adventure stories?" or "Do you want to read romance stories?" to understand the user's specific desires. Step 4: The suggestion unit proposes the most suitable manga from its inventory based on the information obtained by the dialogue unit. For example, it can use a generative AI to make suggestions such as, "How about this adventure manga?" or "I recommend this romance manga." Step 5: The contracts department manages subscription service contracts. For example, it can manage subscription services where users subscribe to purchase 10 or 20 books per month.

[0065] (Example of form 2) The proposed system according to an embodiment of the present invention is a system that grasps the user's preferences through preliminary questions and then proposes recommendations from its inventory through dialogue about what the user wants to read. In this proposal system, the user answers preliminary questions, and a generating AI analyzes that information to grasp the user's preferences. Next, the generating AI asks the user in a dialogue format what kind of manga they want to read based on their preferences. After that, the generating AI proposes the most suitable manga for the user from its inventory, and the user subscribes to a subscription service in units of 10 or 20 books per month. Through this system, the user can enjoy new genres of manga and broaden their world through manga. Furthermore, by utilizing the generating AI, it becomes possible to make optimal suggestions tailored to the user's preferences, and it is expected to gain a dominant market share in the ebook field. For example, the user answers preliminary questions. For example, by answering questions such as "What are your favorite genres?" or "What manga have you read recently?", the system grasps the user's preferences. This information is input into the generating AI and analyzed. Next, the generating AI asks the user in a dialogue format what kind of manga they want to read based on their preferences. For example, it asks questions such as "Do you like adventure stories?" or "Do you want to read romance stories?" to grasp the user's specific desires. The AI ​​then suggests the most suitable manga from its inventory to the user. For example, it might suggest, "How about this adventure manga?" or "I recommend this romance manga." The user selects the suggested manga and subscribes to a monthly subscription service for 10 or 20 titles. This system allows users to enjoy manga in new genres and broaden their horizons through manga. Furthermore, by utilizing the AI, it becomes possible to make optimal suggestions tailored to the user's preferences, and is expected to gain a dominant market share in the ebook sector. In this way, the suggestion system can recommend the most suitable manga based on the user's preferences.

[0066] The proposal system according to this embodiment comprises a reception unit, an analysis unit, a dialogue unit, a proposal unit, and a contract unit. The reception unit understands the user's preferences through pre-questions. For example, the reception unit can understand the user's preferences when the user answers the pre-questions. The analysis unit analyzes the information received by the reception unit to understand the user's preferences. For example, the analysis unit can analyze the user's answers using a generative AI to understand the user's preferences. The dialogue unit asks the user in a dialogue format what kind of manga they would like to read, based on the user's preferences understood by the analysis unit. For example, the dialogue unit can ask the user questions such as "Do you like adventure stories?" or "Do you want to read romance stories?" using a generative AI to understand the user's specific desires. The proposal unit proposes the most suitable manga from its inventory based on the information obtained by the dialogue unit. For example, the proposal unit can make suggestions such as "How about this adventure manga?" or "This romance manga is highly recommended" using a generative AI. The contract unit manages the subscription service contract. The contracts section can manage subscription services where users contract for, for example, 10 or 20 books per month. This allows the proposal system according to the embodiment to suggest the most suitable manga based on the user's preferences.

[0067] The reception department understands user preferences through pre-questions. Specifically, when a user accesses the system, they are first presented with pre-questions. These questions are designed to understand the user's preferences and interests in detail, and include questions such as, "What are your favorite genres?", "What manga have you read recently?", and "What kind of stories do you like?". By answering these questions, users can communicate their preferences and interests to the system. Furthermore, the reception department collects user responses in real time and stores them in a database. This data is used by the subsequent analysis and dialogue departments, so it is important that it is collected accurately and in detail. The reception department processes user responses quickly and prepares them for the next step. In addition, the reception department creates individual profiles based on user responses, building a foundation for providing a customized experience for each user. Thus, the reception department plays a crucial role in accurately understanding user preferences and achieving personalization throughout the entire system.

[0068] The analysis unit analyzes the information received by the reception unit to understand user preferences. Specifically, it uses generative AI to analyze user responses. The generative AI utilizes natural language processing technology to analyze user responses as text data and extract user preferences and interests. For example, if a user responds, "I like adventure stories," the generative AI extracts the keyword "adventure," understanding that the user is interested in the adventure genre. It also analyzes the emotions and nuances contained in the user's responses to understand more detailed preferences. Based on this information, the analysis unit updates the user profile and provides data for use in the next step. Furthermore, the analysis unit can also predict user preferences by referring to past data and the trends of other users. For example, it can list works that the user might be interested in by referring to manga that other users who gave similar answers have liked. In this way, the analysis unit can accurately understand user preferences and improve the accuracy and effectiveness of the entire system.

[0069] The dialogue unit, based on the user's preferences identified by the analysis unit, asks the user in a conversational format what kind of manga they would like to read. Specifically, it uses a generative AI to ask the user questions such as, "Do you like adventure stories?" or "Do you want to read romance stories?" The generative AI dynamically generates the next question in response to the user's answer, and the conversation continues. For example, if the user answers, "I like adventure stories," the generative AI will continue with more specific questions such as, "What kind of adventure stories do you like?" or "Do you like adventure stories that feature a specific character?" This allows the dialogue unit to understand the user's specific wishes and interests in detail. Furthermore, the dialogue unit also analyzes the user's reactions, response speed, and emotional changes to improve the quality of the conversation. For example, it asks more in-depth questions about themes the user shows interest in, and switches to different questions about themes the user shows no interest in. In this way, the dialogue unit can understand more detailed preferences through conversation with the user and improve the accuracy of the suggestions made by the subsequent suggestion unit.

[0070] The suggestion department, based on information obtained by the dialogue department, proposes the most suitable manga from its inventory. Specifically, it uses generative AI to make suggestions such as, "How about this adventure manga?" or "This romance manga is highly recommended." The generative AI selects and proposes the most suitable manga based on the user's preferences and interests. For example, if the user likes adventure stories, the generative AI searches for adventure manga in its inventory and proposes them to the user. The suggestion department also considers the user's past browsing history and ratings to provide more personalized suggestions. For example, it can increase user satisfaction by suggesting works similar to manga that the user has previously given high ratings to. Furthermore, the suggestion department can update its suggestions in real time and adjust them according to the user's response. For example, if the user is not interested in a suggested manga, it will suggest a different work. The suggestion department also collects user feedback and continuously improves the accuracy of its suggestion algorithm. As a result, the suggestion department can propose the most suitable manga to the user and increase their satisfaction.

[0071] The Contracts Department manages subscription service contracts. Specifically, it manages subscription services where users subscribe to services in units of 10 or 20 books per month. The Contracts Department stores user contract information in a database and updates contract details in real time. For example, if a user applies for a new contract, the Contracts Department immediately reflects that information and updates the services available to the user. The Contracts Department also monitors users' contract status and supports contract renewal and cancellation procedures. For example, it sends renewal notices to users whose contracts are nearing expiration to ensure a smooth continuation of their contracts. Furthermore, the Contracts Department can analyze user usage patterns and propose the most suitable plan. For example, if a user subscribed to a plan of 10 books per month frequently makes additional purchases, the Contracts Department will propose a plan that includes a larger number of books. In this way, the Contracts Department can provide flexible services tailored to user needs and improve user satisfaction.

[0072] The suggestion unit can use generative AI to suggest the most suitable manga based on the user's preferences. For example, the suggestion unit can use generative AI to make suggestions such as "How about this adventure manga?" or "This romance manga is highly recommended" based on the user's preferences. This makes it possible to make optimal suggestions based on the user's preferences by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the suggestion unit may be performed using generative AI, or it may be performed without using generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's preferences as input and outputs the most suitable manga.

[0073] The dialogue unit can interact with the user using a generative AI and ask what kind of manga the user wants to read. For example, the dialogue unit can use a generative AI to ask the user questions such as, "Do you like adventure stories?" or "Do you want to read romance stories?" to understand the user's specific preferences. Thus, by using a generative AI, the user's specific preferences can be understood. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can conduct a dialogue using a generative AI model that takes the user's preferences as input and outputs the dialogue content.

[0074] The reception desk can estimate the user's emotions and dynamically change the content of pre-interview questions based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize simple questions and postpone more detailed questions. If the user is relaxed, the reception desk can include more detailed questions to understand their deeper preferences. If the user is in a hurry, the reception desk can select only the most important questions and provide quick answers. This allows for the collection of more relevant information by adjusting the questions according 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 reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past response history and select the optimal question order. For example, the reception desk can analyze patterns in questions the user has answered in the past and present questions in the most effective order. The reception desk can also prioritize questions related to specific genres based on the user's past response history. Furthermore, the reception desk can automatically select highly relevant questions based on the user's response history. This allows for efficient information collection by selecting the optimal question order based on past response history. 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 response history data into AI and have the AI ​​select the optimal question order.

[0076] The reception desk can filter questions based on the user's current interests when they answer pre-submitted questions. For example, the reception desk can prioritize relevant questions based on the genre of manga the user has recently read. It can also select questions related to a specific theme if the user has shown interest in that theme. Furthermore, the reception desk can dynamically change the content of questions based on the user's current interests. This allows for the collection of more relevant information by filtering questions based on the user's current interests. 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 user interest data into an AI and have the AI ​​perform the question filtering.

[0077] The reception desk can estimate the user's emotions and prioritize questions based on those emotions. For example, if the user is nervous, the reception desk will prioritize questions designed to help them relax. If the user is enjoying themselves, the reception desk can prioritize questions that pique their interest. If the user is tired, the reception desk can prioritize simple questions. By prioritizing questions according to the user's emotions, more appropriate questions can be presented. 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 reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize presenting highly relevant questions by considering the user's geographical location when they answer pre-submitted questions. For example, if the user lives in a specific region, the reception desk can prioritize presenting questions about manga related to that region. Similarly, if the user is traveling, the reception desk can prioritize presenting questions about manga related to their travel destination. Furthermore, the reception desk can select region-specific questions based on the user's geographical location. This allows for the presentation of more relevant questions by considering 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 data into an AI and have the AI ​​select highly relevant questions.

[0079] The reception desk can analyze the user's social media activity when they answer pre-submitted questions and suggest relevant questions. For example, the reception desk can suggest relevant questions based on comics the user has shared on social media. It can also suggest relevant questions based on the activity of accounts the user follows. Furthermore, the reception desk can analyze the user's interests on social media and select the most relevant questions. This allows for the suggestion of more relevant questions by analyzing 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 data into an AI and have the AI ​​select relevant questions.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. If the user is in a hurry, the analysis unit can perform a concise analysis and provide results quickly. If the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the user's past subscription history. For example, the analysis unit can analyze the user's preferences based on data of manga the user has read in the past. The analysis unit can also analyze the user's interest in specific genres from their subscription history. Furthermore, the analysis unit can analyze the user's past subscription history and provide data to make optimal suggestions. This improves the accuracy of the analysis by referring to past subscription history. 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 user's subscription history data into AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0082] The analysis unit can apply different analysis methods depending on the user's area of ​​interest during analysis. For example, if the user is interested in adventure manga, the analysis unit can apply an analysis method specialized for adventure manga. Similarly, if the user is interested in romance manga, the analysis unit can apply an analysis method specialized for romance manga. Furthermore, if the user is interested in mystery manga, the analysis unit can apply an analysis method specialized for mystery manga. By applying different analysis methods to each user's area of ​​interest, more accurate analysis becomes possible. 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 user's area of ​​interest data into the AI ​​and have the AI ​​execute the application of different analysis methods.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user lives in a specific region, the analysis unit will prioritize analyzing manga data related to that region. Also, if the user is traveling, the analysis unit can prioritize analyzing manga data related to the travel destination. Furthermore, the analysis unit can apply region-specific analysis methods based on the user's geographical location information. This makes it possible to perform more relevant analysis by considering the user's geographical location information. 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 user's geographical location information data into AI and have the AI ​​perform highly relevant analysis.

[0085] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can analyze the user's preferences based on relevant literature the user has read in the past. The analysis unit can also analyze the user's interests in specific genres from the user's relevant literature. Furthermore, the analysis unit can analyze the user's relevant literature and provide data to make optimal suggestions. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's relevant literature data into AI and have the AI ​​perform the analysis to improve accuracy.

[0086] The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated emotions. For example, if the user is tense, the dialogue unit can engage in conversation to help them relax. If the user is enjoying themselves, the dialogue unit can engage in conversation that is engaging. If the user is tired, the dialogue unit can engage in simple conversation. By adjusting the way the dialogue proceeds according to the user's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or not using a generative AI. For example, the dialogue unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The dialogue unit can select the optimal dialogue scenario during a conversation by referring to the user's past dialogue history. For example, the dialogue unit can analyze patterns of past conversations with the user and select the most effective dialogue scenario. Furthermore, the dialogue unit can prioritize conversations related to specific genres based on the user's past dialogue history. The dialogue unit can also automatically select highly relevant dialogue scenarios based on the user's dialogue history. This allows the optimal dialogue scenario to be selected by referring to past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's dialogue history data into an AI and have the AI ​​select the optimal dialogue scenario.

[0088] The dialogue unit can apply different dialogue algorithms to the user's areas of interest during a conversation. For example, if the user is interested in adventure manga, the dialogue unit can apply a dialogue algorithm specifically for adventure manga. If the user is interested in romance manga, the dialogue unit can apply a dialogue algorithm specifically for romance manga. If the user is interested in mystery manga, the dialogue unit can apply a dialogue algorithm specifically for mystery manga. By applying different dialogue algorithms to each user's areas of interest, more appropriate conversations become possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's areas of interest data into the AI ​​and have the AI ​​apply different dialogue algorithms.

[0089] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is tense, the dialogue unit will prioritize dialogue to help them relax. If the user is enjoying themselves, the dialogue unit will prioritize engaging dialogue. If the user is tired, the dialogue unit will prioritize simple dialogue. By prioritizing dialogue according to the user's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or not. For example, the dialogue unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The dialogue unit can conduct conversations while considering the user's geographical location. For example, if the user lives in a specific region, the dialogue unit will prioritize conversations about manga related to that region. Also, if the user is traveling, the dialogue unit will prioritize conversations about manga related to the travel destination. Furthermore, the dialogue unit can conduct region-specific conversations based on the user's geographical location. This makes it possible to conduct more relevant conversations by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location data into AI and have the AI ​​execute highly relevant conversations.

[0091] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant dialogue content. For example, the dialogue unit can conduct relevant conversations based on comics the user has shared on social media. It can also conduct relevant conversations based on the activity of accounts the user follows. Furthermore, the dialogue unit can analyze the user's interests on social media and provide optimal dialogue content. By analyzing social media activity, it can provide more relevant dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media data into AI and have the AI ​​provide relevant dialogue content.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and more information. If the user is in a hurry, the suggestion unit can provide concise suggestions and deliver results quickly. If the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. 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 suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past subscription history. For example, the suggestion unit can make suggestions tailored to the user's preferences based on data of manga the user has read in the past. It can also analyze the user's subscription history to identify their interests in specific genres and make suggestions accordingly. Furthermore, the suggestion unit can analyze the user's past subscription history and provide data to make optimal suggestions. This improves the accuracy of suggestions by referencing past subscription history. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's subscription history data into an AI and have the AI ​​perform the task of improving the accuracy of its suggestions.

[0094] The suggestion unit can apply different suggestion algorithms depending on the user's area of ​​interest when making suggestions. For example, if the user is interested in adventure manga, the suggestion unit can apply a suggestion algorithm specialized for adventure manga. If the user is interested in romance manga, the suggestion unit can apply a suggestion algorithm specialized for romance manga. If the user is interested in mystery manga, the suggestion unit can apply a suggestion algorithm specialized for mystery manga. By applying different suggestion algorithms to each user's area of ​​interest, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's area of ​​interest data into an AI and have the AI ​​apply different suggestion algorithms.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is tense, the suggestion unit will prioritize suggestions to help them relax. If the user is enjoying themselves, the suggestion unit will prioritize suggestions that will pique their interest. If the user is tired, the suggestion unit will prioritize simple suggestions. By prioritizing suggestions according to the user's emotions, more appropriate suggestions can be made. 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 suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The suggestion unit can make suggestions while considering the user's geographical location. For example, if the user lives in a specific region, the suggestion unit will prioritize suggesting manga related to that region. Also, if the user is traveling, the suggestion unit can prioritize suggesting manga related to their travel destination. Furthermore, the suggestion unit can make region-specific suggestions based on the user's geographical location. This makes it possible to make more relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​make highly relevant suggestions.

[0097] The suggestion unit can analyze the user's social media activity and provide relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on comics the user has shared on social media. It can also make relevant suggestions based on the activity of accounts the user follows. Furthermore, the suggestion unit can analyze the user's interests on social media and provide optimal suggestions. This allows for more relevant suggestions by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into AI and have the AI ​​provide relevant suggestions.

[0098] The contracts unit can estimate the user's emotions and adjust how the contract terms are presented based on those emotions. For example, if the user is relaxed, the contracts unit can present detailed contract terms and provide more information. If the user is in a hurry, the contracts unit can present concise contract terms to expedite the process. If the user is excited, the contracts unit can present visually appealing contract terms. By adjusting how the contract terms are presented according to the user's emotions, a more appropriate contract can be made. 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 contracts unit may be performed using, for example, generative AI, or not. For example, the contracts unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The contracts department can propose the optimal contract plan at the time of contract by referring to the user's past contract history. For example, the contracts department can propose the optimal contract plan based on the plans the user has contracted in the past. The contracts department can also analyze the user's interest in specific plans from their contract history and make suggestions. Furthermore, the contracts department can analyze the user's past contract history and propose the optimal contract plan. In this way, the optimal contract plan can be proposed by referring to past contract history. Some or all of the above processes in the contracts department may be performed using AI, for example, or not using AI. For example, the contracts department can input the user's contract history data into AI and have the AI ​​propose the optimal contract plan.

[0100] The contracts unit can estimate the user's emotions and determine contract priorities based on those emotions. For example, if the user is tense, the contracts unit will prioritize presenting contract terms that promote relaxation. If the user is enjoying themselves, the contracts unit can prioritize presenting contract terms that pique their interest. If the user is tired, the contracts unit can prioritize presenting simple contract terms. By prioritizing contracts according to the user's emotions, more appropriate contracts can be made. 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 contracts unit may be performed using, for example, generative AI, or not using generative AI. For example, the contracts unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The contracts department can propose the most suitable contract plan at the time of contracting, taking into account the user's geographical location. For example, if the user lives in a specific region, the contracts department will prioritize proposing contract plans related to that region. Similarly, if the user is traveling, the contracts department can prioritize proposing contract plans related to their travel destination. Furthermore, the contracts department can propose region-specific contract plans based on the user's geographical location. This allows for the proposal of more relevant contract plans by considering the user's geographical location. Some or all of the above processing in the contracts department may be performed using AI, or not. For example, the contracts department can input the user's geographical location data into an AI and have the AI ​​propose the most suitable contract plan.

[0102] A dedicated librarian can estimate the user's emotions and adjust the way the conversation progresses based on those emotions. For example, if the user is tense, the librarian can engage in conversation to help them relax. If the user is enjoying themselves, the librarian can engage in conversation that is engaging. If the user is tired, the librarian can engage in simple conversation. By adjusting the way the conversation progresses according to the user's emotions, a more appropriate conversation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the dedicated librarian may be performed using a generative AI, or not using a generative AI. For example, the dedicated librarian can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] A dedicated librarian can make optimal suggestions by referring to the user's past conversation history. For example, the dedicated librarian can analyze patterns in the user's past conversations and make the most effective suggestions. Furthermore, the dedicated librarian can prioritize suggestions related to specific genres based on the user's past conversation history. Additionally, the dedicated librarian can automatically select highly relevant suggestions based on the user's conversation history. This enables optimal suggestions by referring to past conversation history. Some or all of the above processes performed by the dedicated librarian may be carried out using AI, or not. For example, the dedicated librarian can input the user's conversation history data into an AI and have the AI ​​select the optimal suggestions.

[0104] A dedicated librarian can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is tense, the librarian will prioritize suggestions to help them relax. If the user is enjoying themselves, the librarian will prioritize suggestions that will pique their interest. If the user is tired, the librarian will prioritize simple suggestions. By prioritizing suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dedicated librarian may be performed using generative AI, or not. For example, the dedicated librarian can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] A dedicated librarian can make optimal suggestions by taking into account the user's geographical location. For example, if the user lives in a specific region, the dedicated librarian will prioritize suggesting manga related to that region. Also, if the user is traveling, the dedicated librarian can prioritize suggesting manga related to the travel destination. Furthermore, the dedicated librarian can make region-specific suggestions based on the user's geographical location. This allows for more relevant suggestions by considering the user's geographical location. Some or all of the above processing by the dedicated librarian may be performed using AI, for example, or not. For example, the dedicated librarian can input the user's geographical location data into AI and have the AI ​​select the optimal suggestions.

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

[0107] The suggestion system can analyze user voice data to understand user preferences. For example, the reception department collects voice data when users answer pre-questions, and the analysis department analyzes this voice data to understand the user's emotions and preferences. This allows preferences to be understood through voice input even if the user is unfamiliar with text input. Furthermore, by analyzing voice data, the system can more accurately estimate the user's emotions and improve the accuracy of suggestions. For example, if a user is excited, the system can analyze voice patterns indicating excitement and make suggestions that will interest the user. Conversely, if a user is relaxed, the system can analyze voice patterns indicating relaxation and make more detailed suggestions.

[0108] The suggestion system can consider not only the user's subscription history but also their viewing history to understand their preferences. For example, the reception department can collect data on videos the user has watched in the past, and the analysis department can analyze this data to understand the user's preferences. This allows the system to suggest more appropriate manga based on the genres and themes of videos the user has watched. Furthermore, by considering viewing history, the system can understand changes in the user's interests and make suggestions based on their latest preferences. For example, if a user has recently watched many action movies, the system can prioritize suggesting action manga. Similarly, if a user watches many documentaries, the system can suggest manga based on history and facts.

[0109] The suggestion system can analyze users' social media activity to understand their preferences. For example, the reception department collects data on content users share on social media and accounts they follow, and the analysis department analyzes this data to understand users' preferences. This allows the system to suggest more appropriate manga based on the genres and themes users show interest in on social media. Furthermore, by analyzing social media activity, the system can understand users' latest interests and provide optimal suggestions in real time. For example, if a user has recently shared a particular manga, the system can suggest works related to that manga. Also, if a user follows a particular artist, the system can prioritize suggesting works by that artist.

[0110] The suggestion system can analyze the user's biometric information to understand their preferences. For example, the reception unit collects biometric information such as the user's heart rate and skin electrical activity, and the analysis unit analyzes this data to understand the user's emotions and preferences. This allows the system to accurately grasp the user's emotional state, such as whether they are relaxed or excited, and improve the accuracy of its suggestions. For example, if the user's heart rate is elevated, the system can determine that they are excited and suggest action-packed or thrilling comics. Conversely, if the user's heart rate is stable, the system can determine that they are relaxed and suggest comics with relaxing content.

[0111] The suggestion system can consider not only the user's subscription history but also their purchase history to understand their preferences. For example, the reception department can collect data on products the user has purchased in the past, and the analysis department can analyze this data to understand the user's preferences. This allows the system to suggest more appropriate manga based on the genres and themes of products the user has purchased. Furthermore, by considering purchase history, the system can understand changes in the user's interests and make suggestions based on their latest preferences. For example, if a user has recently purchased many fantasy-related products, the system can prioritize suggesting fantasy manga. Similarly, if a user has purchased many science-related products, the system can suggest science-related manga.

[0112] The suggestion system can understand user preferences by estimating user emotions and adjusting the content of suggestions based on those emotions. For example, the reception department can collect facial expression data when the user answers pre-questions, and the analysis department can analyze this data to estimate the user's emotions. This allows the system to accurately grasp the user's emotional state, such as whether they are relaxed or excited, and adjust the content of suggestions accordingly. For example, if the user is relaxed, detailed suggestions can be made, providing more information. If the user is in a hurry, concise suggestions can be made, providing results quickly. If the user is excited, visually appealing suggestions can be made.

[0113] The suggestion system can consider the user's geographical location to understand their preferences. For example, the reception unit can collect the user's current geographical location, and the analysis unit can analyze this data to understand the user's preferences. This allows the system to prioritize suggesting manga related to a specific region if the user lives there. Similarly, if the user is traveling, the system can suggest manga related to their travel destination. For instance, if the user lives in Japan, the system can suggest manga related to Japanese culture and history. If the user is traveling in Europe, the system can suggest manga related to European history and culture.

[0114] The suggestion system can understand user preferences by estimating user emotions and prioritizing suggestions based on those emotions. For example, the reception department can collect facial expression data when users answer pre-questions, and the analysis department can analyze this data to estimate the user's emotions. This allows the system to accurately understand the user's emotional state, such as whether they are relaxed or excited, and to prioritize suggestions accordingly. For instance, if a user is tense, suggestions to help them relax can be prioritized. If a user is enjoying themselves, suggestions that pique their interest can be prioritized. If a user is tired, simple suggestions can be prioritized.

[0115] The suggestion system can consider not only the user's subscription history but also their reading history to understand their preferences. For example, the reception department can collect data on books the user has read in the past, and the analysis department can analyze this data to understand the user's preferences. This allows the system to suggest more appropriate manga based on the genres and themes of books the user has read. Furthermore, by considering the reading history, the system can understand changes in the user's interests and make suggestions based on their latest preferences. For example, if a user has recently been reading a lot of mystery novels, the system can prioritize suggesting mystery manga. Similarly, if a user has been reading a lot of fantasy novels, the system can suggest fantasy manga.

[0116] The suggestion system can understand user preferences by estimating user emotions and adjusting the way suggestions are presented based on those estimated emotions. For example, the reception department can collect facial expression data when users answer pre-questions, and the analysis department can analyze this data to estimate the user's emotions. This allows the system to accurately grasp the user's emotional state, such as whether they are relaxed or excited, and adjust the way suggestions are presented accordingly. For instance, if the user is relaxed, detailed suggestions can be made, providing more information. If the user is in a hurry, concise suggestions can be made, providing results quickly. If the user is excited, visually appealing suggestions can be made.

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

[0118] Step 1: The reception desk understands the user's preferences through pre-questions. For example, the user's preferences can be understood by having them answer pre-questions. Step 2: The analysis unit analyzes the information received by the reception unit to understand the user's preferences. For example, it can use a generation AI to analyze the user's responses and understand their preferences. Step 3: The dialogue unit asks the user, in a conversational format, what kind of manga they want to read, based on the user's preferences identified by the analysis unit. For example, using a generative AI, it can ask the user questions such as, "Do you like adventure stories?" or "Do you want to read romance stories?" to understand the user's specific desires. Step 4: The suggestion unit proposes the most suitable manga from its inventory based on the information obtained by the dialogue unit. For example, it can use a generative AI to make suggestions such as, "How about this adventure manga?" or "I recommend this romance manga." Step 5: The contracts department manages subscription service contracts. For example, it can manage subscription services where users subscribe to purchase 10 or 20 books per month.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, dialogue unit, proposal unit, and contract 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, which understands the user's preferences by having the user answer pre-questions. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's answers using generating AI to understand the user's preferences. The dialogue unit is implemented by the control unit 46A of the smart device 14, which asks the user questions using generating AI to understand the user's specific wishes. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable manga from the inventory. The contract unit is implemented by the control unit 46A of the smart device 14, which manages the contract for the subscription service. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, dialogue unit, proposal unit, and contract 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 the user's preferences are understood by the user's answers to pre-assigned questions. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and the user's preferences are understood by analyzing the user's answers using generating AI. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, and the user's specific wishes are understood by asking questions using generating AI. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, and the optimal manga is suggested from the inventory. The contract unit is implemented by the control unit 46A of the smart glasses 214, and the contract for the subscription service is managed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, dialogue unit, proposal unit, and contract unit, is implemented by 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 the user's preferences are understood by the user's answers to pre-assigned questions. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and the user's preferences are understood by analyzing the user's answers using generating AI. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, and the user's specific wishes are understood by asking questions using generating AI. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, and the optimal manga is suggested from the inventory. The contract unit is implemented by the control unit 46A of the headset terminal 314, and the subscription service contract is managed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the reception unit, analysis unit, dialogue unit, proposal unit, and contract 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 the user's preferences are understood by the user answering pre-assigned questions. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and the user's preferences are understood by analyzing the user's answers using a generating AI. The dialogue unit is implemented by, for example, the control unit 46A of the robot 414, and the user's specific wishes are understood by asking questions using a generating AI. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and the optimal manga is suggested from the inventory. The contract unit is implemented by, for example, the control unit 46A of the robot 414, and the contract for the subscription service is managed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) The reception desk understands the user's preferences through pre-questions, An analysis unit analyzes the information received by the reception unit to understand the user's preferences, Based on the user's preferences as determined by the analysis unit, a dialogue unit asks in a conversational format what kind of manga the user wants to read, Based on the information obtained by the aforementioned dialogue unit, the proposal unit suggests the most suitable manga from the inventory, It comprises a contracts department that manages subscription service contracts, A system characterized by the following features. (Note 2) The aforementioned proposal section is, The AI ​​generates recommendations for the best manga based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, The AI ​​generates conversations with the user and asks what kind of manga they want to read. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the content of pre-questions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past response history to select the optimal question order. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users answer pre-submitted questions, the questions are filtered based on their current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users answer pre-submitted questions, the system prioritizes presenting highly relevant questions based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users answer pre-submitted questions, their social media activity is analyzed, and relevant questions are presented. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past subscription history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the user's area of ​​interest. 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the conversation progresses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation scenario by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned dialogue unit, During conversations, different dialogue algorithms are applied depending on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dialogue unit, During conversations, the system takes the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and provides relevant conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, we refer to the user's past subscription history to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will take the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and provide relevant recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned contracts department, The system estimates the user's emotions and adjusts the way contract terms are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned contracts department, When a contract is signed, we refer to the user's past contract history to propose the most suitable contract plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned contracts department, It estimates user sentiment and determines contract priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned contracts department, When signing a contract, we propose the optimal contract plan considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) A full-time librarian is, It estimates the user's emotions and adjusts the way the conversation progresses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) A full-time librarian is, The system provides optimal suggestions by referencing the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) A full-time librarian is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) A full-time librarian is, We make optimal suggestions by taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk understands the user's preferences through pre-questions, An analysis unit analyzes the information received by the reception unit to understand the user's preferences, Based on the user's preferences as determined by the analysis unit, a dialogue unit asks in a conversational format what kind of manga the user wants to read, Based on the information obtained by the aforementioned dialogue unit, the proposal unit suggests the most suitable manga from the inventory, It comprises a contracts department that manages subscription service contracts, A system characterized by the following features.

2. The aforementioned proposal section is, The AI ​​generates suggestions for the best manga based on the user's preferences. The system according to feature 1.

3. The aforementioned dialogue unit, The AI ​​generates conversations with the user and asks what kind of manga they want to read. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the content of pre-questions based on the estimated user emotions. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past response history to select the optimal question order. The system according to feature 1.

6. The aforementioned reception unit is When users answer pre-submitted questions, the questions are filtered based on their current interests and concerns. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When users answer pre-submitted questions, the system prioritizes presenting highly relevant questions based on their geographical location. The system according to feature 1.

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