system

Abstract

The system according to this embodiment aims to recommend the most suitable manga based on the user's preferences and psychological tendencies. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a recommendation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit and analyzes the user's psychological tendencies. The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. The recommendation unit recommends the most suitable manga based on the patterns extracted by the extraction unit. The provision unit provides the manga recommended by the recommendation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to recommend an optimal comic based on the user's preferences and psychological tendencies, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend an optimal comic based on the user's preferences and psychological tendencies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a recommendation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit and analyzes the user's psychological tendencies. The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. The recommendation unit recommends the most suitable manga based on the patterns extracted by the extraction unit. The provision unit provides the manga recommended by the recommendation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the most suitable manga based on the user's preferences and psychological tendencies. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls 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) An AI agent system according to an embodiment of the present invention is a system that analyzes the user's input preferences and psychological tendencies and recommends the most suitable manga from a database. This AI agent system analyzes the user's psychological tendencies by having the user input information about their preferences and psychological tendencies. Furthermore, the AI ​​agent system extracts patterns from a large manga database and recommends the most suitable manga to the user. This recommendation is customized for each user, improving user satisfaction. Publishers can also use this system to conduct effective marketing tailored to their target audience. For example, the AI ​​agent system can analyze the user's behavior and choices to determine their psychological tendencies and recommend new manga in a particular genre to users who are interested in that genre. This makes it easier for users to discover new favorite manga, and publishers can increase sales. Furthermore, this system can contribute to acquiring new users and potentially expand the overall market size. For example, by having the AI ​​agent system recommend manga that match the user's preferences, user satisfaction is expected to improve, and the number of new users will increase through word of mouth and reviews. In this way, this innovative recommendation system, which combines psychological analysis and AI, is easy for users to use and is an effective tool for publishers, thanks to its interactive interface design that prioritizes user experience. This allows the AI ​​agent system to analyze the user's preferences and psychological tendencies and recommend the most suitable manga.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a recommendation unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives text data entered by the user. The reception unit can also use speech recognition technology to convert the user's voice input into text data and receive it. Furthermore, the reception unit can use image recognition technology to analyze image data uploaded by the user, extract necessary information, and receive it. For example, the reception unit analyzes the text data entered by the user using natural language processing technology and extracts information about the user's preferences and psychological tendencies. In the case of voice input, the reception unit uses speech recognition technology to convert the voice data into text data and analyzes that text data. In the case of image input, the reception unit uses image recognition technology to analyze the image data and extract information about the user's preferences and psychological tendencies. The analysis unit analyzes the information received by the reception unit and analyzes the user's psychological tendencies. The analysis unit analyzes user input data using, for example, natural language processing technology to estimate the user's emotions and interests. The analysis unit can also analyze user behavior patterns and identify user psychological tendencies using machine learning algorithms. For example, the analysis unit classifies user input data using clustering algorithms to group user interests and concerns. Furthermore, the analysis unit can learn from the user's past input data to predict user psychological tendencies. The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. For example, the extraction unit analyzes manga data in the database and extracts patterns that match the user's psychological tendencies. The extraction unit can also cluster manga data in the database using machine learning algorithms to identify the most suitable manga for the user. For example, the extraction unit extracts manga related to specific genres or themes based on the user's psychological tendencies. Furthermore, the extraction unit can recommend the most suitable manga to the user, taking into account the user's past browsing history and evaluation data. The recommendation unit recommends the most suitable manga based on the patterns extracted by the extraction unit.The recommendation unit, for example, uses a collaborative filtering algorithm to recommend the most suitable manga to the user, taking into account the evaluation data of other users. The recommendation unit can also use a content-based recommendation algorithm to recommend the most suitable manga based on the user's preferences and psychological tendencies. For example, the recommendation unit recommends the most suitable manga to the user based on the user's past browsing history and evaluation data. Furthermore, the recommendation unit can recommend the most suitable manga in real time, taking into account the user's current areas of interest and behavioral patterns. The delivery unit provides the manga recommended by the recommendation unit to the user. The delivery unit displays the recommended manga to the user, for example, through a web application or mobile application. The delivery unit can also notify the user of the recommended manga using email or push notifications. For example, the delivery unit displays the recommended manga on the top page when the user accesses the application. Furthermore, the delivery unit can customize and display the recommended manga according to the user's preferences. As a result, the AI ​​agent system according to this embodiment can analyze the user's psychological tendencies based on the user's input information and recommend the most suitable manga.

[0030] The reception desk accepts user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts text data entered by the user. The reception desk can also use speech recognition technology to convert the user's voice input into text data and accept it. Furthermore, the reception desk can use image recognition technology to analyze image data uploaded by the user, extract necessary information, and accept it. For example, the reception desk analyzes text data entered by the user using natural language processing technology to extract information about the user's preferences and psychological tendencies. In the case of voice input, the reception desk uses speech recognition technology to convert the voice data into text data and then analyzes that text data. In the case of image input, the reception desk uses image recognition technology to analyze the image data and extract information about the user's preferences and psychological tendencies. The reception desk provides multiple interfaces for accepting user input. For example, it allows users to easily input information through a web browser or mobile application. In the case of text input, the user enters text using a keyboard, and in the case of voice input, the user enters voice using a microphone. For image input, users select and upload images from their camera or gallery. The reception desk receives and processes this input data in real time. Speech recognition technology uses deep learning algorithms to convert user speech into text with high accuracy. Image recognition technology uses convolutional neural networks (CNNs) to extract features from user-uploaded images and identify user preferences and psychological tendencies. This allows the reception desk to handle diverse user input formats and receive information accurately and quickly. Furthermore, the reception desk securely manages user input data and implements security measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. This allows the reception desk to gain user trust and provide a system that can be used with peace of mind.

[0031] The analysis department analyzes information received by the reception department to analyze the user's psychological tendencies. For example, the analysis department uses natural language processing technology to analyze user input data and estimate the user's emotions and interests. The analysis department can also use machine learning algorithms to analyze user behavior patterns and identify user psychological tendencies. For example, the analysis department classifies user input data using clustering algorithms to group user interests and concerns. Furthermore, the analysis department can learn from the user's past input data to predict user psychological tendencies. The analysis department uses natural language processing technology to analyze user text data. Specifically, it performs morphological and grammatical analysis to understand the user's input. It uses sentiment analysis algorithms to classify the user's emotions as positive, negative, neutral, etc. It also uses topic modeling technology to identify topics of interest to the user. In the case of audio data, it converts it to text data using speech recognition technology and then applies similar natural language processing technology. In the case of image data, it uses image recognition technology to identify objects and scenes within the image and analyze the user's preferences and psychological tendencies. It uses machine learning algorithms to analyze user behavior patterns. For example, based on users' past input data and behavioral history, clustering algorithms are used to classify users into similar groups. This allows for grouping users' interests and preferences, and providing optimal content to individual users. Furthermore, the analytics department uses recurrent neural network (RNN) and long-term short-term memory (LSTM) models to learn from users' past data and predict future behavior and psychological tendencies. This allows for real-time tracking of changes in users' interests and preferences, and the provision of appropriate content. The analytics department securely manages user data and takes measures to protect privacy. For example, data anonymization and access control are implemented to protect users' personal information. This allows the analytics department to gain users' trust and provide a system that can be used with confidence.

[0032] The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. For example, the extraction unit analyzes manga data in the database and extracts patterns that match the user's psychological tendencies. The extraction unit can also use machine learning algorithms to cluster the manga data in the database and identify the manga best suited to the user. For example, the extraction unit extracts manga related to specific genres or themes based on the user's psychological tendencies. Furthermore, the extraction unit can recommend the best manga to the user by considering the user's past browsing history and rating data. The extraction unit uses indexing techniques to efficiently search the manga data in the database and identify patterns that match the user's psychological tendencies. For example, metadata such as genre, theme, author, and rating are indexed for the manga data to speed up the search. Machine learning algorithms are used to cluster the manga data in the database. Specifically, K-means clustering or hierarchical clustering is used to group similar manga. This allows for the efficient extraction of manga related to specific genres or themes based on the user's psychological tendencies. Furthermore, the extraction unit uses a collaborative filtering algorithm to recommend the most suitable manga to the user, taking into account the user's past browsing history and rating data. For example, it compares the user's rating data with that of other users and refers to recommendations from users with similar rating patterns. This allows it to identify and recommend the most suitable manga for the user. The extraction unit regularly updates and maintains the database to reflect the latest manga data. This ensures that recommendations are always based on the most up-to-date information. In addition, the extraction unit collects user feedback and continuously improves the accuracy of the recommendation algorithm. For example, it analyzes the ratings and comments users have made on recommended manga and adjusts the parameters of the recommendation algorithm. This allows the extraction unit to provide the most suitable manga according to the user's preferences and improve user satisfaction.

[0033] The recommendation unit recommends the most suitable manga based on patterns extracted by the extraction unit. For example, the recommendation unit uses a collaborative filtering algorithm to recommend the most suitable manga to the user, referencing evaluation data from other users. The recommendation unit can also use a content-based recommendation algorithm to recommend the most suitable manga based on the user's preferences and psychological tendencies. For example, the recommendation unit recommends the most suitable manga to the user based on the user's past browsing history and evaluation data. Furthermore, the recommendation unit can recommend the most suitable manga in real time, taking into account the user's current areas of interest and behavioral patterns. The recommendation unit uses a collaborative filtering algorithm to reference evaluation data from other users. Specifically, it represents user evaluation data in matrix form and performs similarity calculations. For example, it calculates similarity between users using cosine similarity or Pearson correlation coefficients, and makes recommendations based on the evaluation data of users with high similarity. This allows it to recommend the most suitable manga to the user. It also uses a content-based recommendation algorithm to make recommendations based on the user's preferences and psychological tendencies. Specifically, it represents the user's past browsing history and evaluation data as feature vectors and recommends manga with similar features. For example, using TF-IDF or word embedding, the content of manga is vectorized to identify manga that match the user's preferences. The recommendation system monitors the user's behavior patterns in real time and makes recommendations based on their current areas of interest. For example, if a user frequently views manga of a particular genre, it will recommend new manga related to that genre. Furthermore, if the user's behavior patterns change, the recommendation algorithm is dynamically adjusted to provide recommendations based on their latest areas of interest. The recommendation system collects user feedback and continuously improves the accuracy of the recommendation algorithm. For example, it analyzes the ratings and comments users make on recommended manga and adjusts the parameters of the recommendation algorithm. This allows the recommendation system to provide the most suitable manga according to the user's preferences and improve user satisfaction.

[0034] The provider provides users with manga recommended by the recommendation team. The provider displays recommended manga to users, for example, through web applications and mobile applications. The provider can also notify users of recommended manga via email and push notifications. For example, when a user accesses the service, the provider displays recommended manga on the homepage. Furthermore, the provider can customize the display of recommended manga according to the user's preferences. The provider designs user interfaces for displaying recommended manga to users through web and mobile applications. Specifically, it provides an intuitive interface that displays a list of recommended manga. When a user clicks on a manga they are interested in, they can access detailed information and sample readings. The provider notifies users of recommended manga via email and push notifications. For example, when a user receives a new recommendation, it sends an email notification, allowing them to access the web application by clicking a link. It also uses push notifications to send real-time notifications to mobile applications, allowing users to immediately check recommended manga. The provider customizes the display of recommended manga according to the user's preferences. For example, the system prioritizes displaying manga that users are likely to be interested in, based on their past browsing history and rating data. It also collects user feedback and continuously improves the displayed content. For instance, if a user shows interest in a particular genre or theme, the system prioritizes displaying manga related to that genre or theme. The service provider securely manages user data and takes measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. This allows the service provider to gain user trust and provide a system that users can use with confidence.

[0035] The reception desk can analyze the user's past input history and select the most suitable reception method. For example, the reception desk can prioritize presenting input methods that the user has frequently used in the past. For instance, if the reception desk has frequently used text input in the past, it will prioritize presenting text input. The reception desk can also analyze the user's past input times and prompt them to input at the most suitable time. For example, if the reception desk has previously entered information at night, it will send a notification prompting them to input at night. The reception desk can also automatically complete input fields by referring to the user's past input. For example, the reception desk will automatically complete input fields based on information the user has entered in the past. This allows the reception desk to select the most suitable reception method by analyzing the user's past input history, thereby improving user convenience. 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 past input history data into a generating AI and have the generating AI select the most suitable reception method.

[0036] The reception unit can filter input based on the user's current areas of interest. For example, the reception unit can prioritize displaying input items related to genres the user has recently been interested in. For example, the reception unit can display input items related to genres the user has recently been interested in at the top. The reception unit can also customize input items based on the user's current areas of interest. For example, the reception unit can customize the order and content of input items based on the user's areas of interest. The reception unit can also automatically retrieve information related to the user's areas of interest and simplify input. For example, the reception unit can automatically retrieve information related to the user's areas of interest and automatically complete input items. This allows the reception unit to prioritize receiving highly relevant information by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. For example, when the user is in a specific region, the reception unit will display information related to that region. The reception unit can also receive region-specific information based on the user's location. For example, the reception unit will display region-specific information based on the user's location. The reception unit can also prioritize receiving information related to the user's travel destination if the user is traveling. For example, when the user is traveling, the reception unit will display information related to the user's travel destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0038] The reception unit can analyze the user's social media activity and receive relevant information when receiving input. For example, the reception unit can present relevant input fields based on information the user has shared on social media. The reception unit can also customize input fields by referring to the activities of the user's social media followers and friends. The reception unit can also analyze the content of the user's social media posts and automatically retrieve relevant information. This allows the reception unit to prioritize receiving relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0039] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on important information. For example, the analysis unit can provide detailed analysis results for important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit can provide concise analysis results for general information. The analysis unit can also adjust the level of detail of its analysis according to the user's level of interest. For example, the analysis unit can adjust the level of detail of its analysis according to the user's level of interest. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information. 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 information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms to different genres of manga depending on the user's preferences. The analysis unit can also select an appropriate analysis algorithm based on the user's psychological tendencies. The analysis unit can also apply the optimal analysis algorithm depending on the category of information. By applying different analysis algorithms depending on the category of information, the optimal analysis results can be provided. 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 information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the timing of information submission. For example, the analysis unit can prioritize the analysis of the latest information. The analysis unit can also postpone the analysis of older information. The analysis unit can also adjust the priority of analysis according to the timing of information submission. This allows the analysis unit to prioritize the analysis of the latest information by determining the priority of analysis based on the timing of information submission. 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 information submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, highly relevant information can be prioritized. 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 information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The extraction unit can improve the accuracy of extraction by considering the interrelationships of information during the extraction process. For example, the extraction unit can group related information together and extract it. For example, the extraction unit can group related information together and extract it. The extraction unit can also analyze the interrelationships of information and perform highly accurate extraction. For example, the extraction unit can analyze the interrelationships of information and perform highly accurate extraction. The extraction unit can also perform optimal extraction based on the relationships between the information. For example, the extraction unit can perform optimal extraction based on the relationships between the information. In this way, by considering the interrelationships of information, highly accurate information can be extracted. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input interrelationship data of information into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0044] The extraction unit can perform extraction while considering the attribute information of the information submitter. For example, the extraction unit can extract highly reliable information based on the submitter's expertise. The extraction unit can also extract important information by considering the submitter's past performance. The extraction unit can also extract optimal information based on the submitter's attribute information. This makes it possible to extract highly reliable information by considering the attribute information of the information submitter. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the extraction.

[0045] The extraction unit can perform extraction while considering the geographical distribution of information. For example, the extraction unit can prioritize extracting information that is geographically close. The extraction unit can also extract highly relevant information based on geographical distribution. The extraction unit can also extract the most relevant information by considering geographical factors. This allows for the extraction of highly relevant information by considering the geographical distribution of information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data of information into a generating AI and have the generating AI perform the extraction.

[0046] The extraction unit can improve the accuracy of its extraction by referring to related literature during the extraction process. For example, the extraction unit can extract highly reliable information by referring to related literature. The extraction unit can also improve the accuracy of the information based on related literature. The extraction unit can also extract the most suitable information by considering related literature. This allows for the extraction of highly reliable information by referring to related literature. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input related literature data into a generating AI and have the generating AI perform the extraction.

[0047] The recommendation system can adjust the level of detail of recommendations based on the importance of the information. For example, the recommendation system can provide detailed recommendations for important information. For example, the recommendation system can display detailed recommendations for important information. The recommendation system can also provide concise recommendations for general information. For example, the recommendation system can display concise recommendations for general information. The recommendation system can also adjust the level of detail of recommendations according to the user's level of interest. For example, the recommendation system can adjust the level of detail of recommendations according to the user's level of interest. This allows the recommendation system to provide detailed recommendations for important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of recommendations.

[0048] The recommendation system can apply different recommendation algorithms depending on the category of information during the recommendation process. For example, the recommendation system can apply different recommendation algorithms to different genres of manga depending on the user's preferences. The recommendation system can also select an appropriate recommendation algorithm based on the user's psychological tendencies. The recommendation system can also apply the optimal recommendation algorithm depending on the category of information. By applying different recommendation algorithms depending on the category of information, the system can provide optimal recommendation results. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input information category data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0049] The recommendation department can determine the priority of recommendations based on the information submission date. For example, the recommendation department will prioritize the most recent information. The recommendation department can also postpone recommending older information. The recommendation department can also adjust the priority of recommendations according to the information submission date. By determining the priority of recommendations based on the information submission date, the recommendation department can prioritize recommending the most recent information. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input information submission date data into a generating AI and have the generating AI determine the priority of recommendations.

[0050] The recommendation unit can adjust the order of recommendations based on the relevance of the information. For example, the recommendation unit can prioritize recommending highly relevant information. The recommendation unit can also postpone recommending less relevant information. The recommendation unit can also adjust the order of recommendations according to the relevance of the information. By adjusting the order of recommendations based on the relevance of the information, highly relevant information can be recommended preferentially. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0051] The service delivery unit can analyze the user's past behavior history at the time of delivery to select the optimal delivery method. For example, the service delivery unit can prioritize selecting delivery methods that the user has used in the past. The service delivery unit can also analyze the user's past behavior history and propose the optimal delivery method. The service delivery unit can also customize the delivery method based on the user's behavior patterns. By analyzing the user's past behavior history, the service delivery unit can select the optimal delivery method and improve user convenience. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal delivery method.

[0052] The service provider can customize the means of delivery based on the user's current areas of interest at the time of delivery. For example, the service provider can customize the delivery method based on the user's current areas of interest. The service provider can also prioritize providing information related to the user's areas of interest. For example, the service provider can prioritize providing information related to the user's areas of interest. The service provider can also select the means of delivery according to the user's areas of interest. For example, the service provider can select the means of delivery according to the user's areas of interest. By customizing the means of delivery based on the user's current areas of interest, the service provider can provide the user with the most suitable information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0053] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing information related to that region. The service provider can also provide region-specific information based on the user's location information. The service provider can also provide information related to the user's travel destination if the user is traveling. By considering the user's geographical location information, the service provider can provide highly relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal service delivery method.

[0054] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can propose a relevant means of delivery based on information shared by the user on social media. The service provider can also customize the means of delivery by referring to the activities of the user's social media followers and friends. The service provider can also analyze the content of the user's social media posts and propose a relevant means of delivery. In this way, by analyzing the user's social media activity, a relevant means of delivery can be proposed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of means of delivery.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past input history when receiving user input and select the most suitable reception method. For example, it can prioritize presenting input methods that the user has frequently used in the past. The reception desk can also analyze the user's past input times and prompt them to input at the most optimal time. Furthermore, the reception desk can automatically complete input fields by referring to the user's past input content. This allows the reception desk to select the most suitable reception method by analyzing the user's past input history, thereby improving user convenience.

[0057] The reception system can filter input based on the user's current areas of interest. For example, it can prioritize displaying input fields related to genres the user has recently been interested in. It can also customize input fields based on the user's current areas of interest. Furthermore, it can automatically retrieve information related to the user's areas of interest and simplify the input process. This allows the system to prioritize receiving highly relevant information by filtering based on the user's current areas of interest.

[0058] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, it will prioritize receiving information related to that region. It can also prioritize receiving region-specific information based on the user's location. Furthermore, if the user is traveling, it can prioritize receiving information related to their travel destination. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant information.

[0059] The analysis unit can adjust the level of detail in its analysis based on the importance of the information. For example, it can perform a detailed analysis on important information, and a concise analysis on general information. Furthermore, it can adjust the level of detail in its analysis according to the user's level of interest. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information.

[0060] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, it can apply different analysis algorithms to different genres of manga depending on the user's preferences. It can also select an appropriate analysis algorithm based on the user's psychological tendencies. Furthermore, it can apply the optimal analysis algorithm depending on the category of information. By applying different analysis algorithms depending on the category of information, it is possible to provide optimal analysis results.

[0061] The analysis department can prioritize analysis based on when the information was submitted. For example, the most recent information can be analyzed first, while older information can be postponed. Furthermore, the analysis priority can be adjusted according to when the information was submitted. This allows for prioritizing the analysis of the most recent information by determining the analysis priority based on when the information was submitted.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk receives text data entered by the user. It can also use speech recognition technology to convert the user's voice input into text data and accept it. Furthermore, it can use image recognition technology to analyze image data uploaded by the user, extract the necessary information, and accept it. Step 2: The analysis department analyzes the information received by the reception department to analyze the user's psychological tendencies. For example, natural language processing technology is used to analyze the user's input data and estimate the user's emotions and interests. Machine learning algorithms can also be used to analyze the user's behavior patterns and identify the user's psychological tendencies. Step 3: The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. For example, it analyzes manga data in the database and extracts patterns that match the user's psychological tendencies. It can also use machine learning algorithms to cluster the manga data in the database and identify the manga that is best suited to the user. Step 4: The recommendation unit recommends the most suitable manga based on the patterns extracted by the extraction unit. For example, it can use a collaborative filtering algorithm to recommend the most suitable manga to the user by referring to evaluation data from other users. Alternatively, it can use a content-based recommendation algorithm to recommend the most suitable manga based on the user's preferences and psychological tendencies. Step 5: The distribution team provides users with the manga recommended by the recommendation team. For example, they display the recommended manga to users through web applications or mobile applications. They can also notify users of the recommended manga via email or push notifications.

[0064] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes the user's input preferences and psychological tendencies and recommends the most suitable manga from a database. This AI agent system analyzes the user's psychological tendencies by having the user input information about their preferences and psychological tendencies. Furthermore, the AI ​​agent system extracts patterns from a large manga database and recommends the most suitable manga to the user. This recommendation is customized for each user, improving user satisfaction. Publishers can also use this system to conduct effective marketing tailored to their target audience. For example, the AI ​​agent system can analyze the user's behavior and choices to determine their psychological tendencies and recommend new manga in a particular genre to users who are interested in that genre. This makes it easier for users to discover new favorite manga, and publishers can increase sales. Furthermore, this system can contribute to acquiring new users and potentially expand the overall market size. For example, by having the AI ​​agent system recommend manga that match the user's preferences, user satisfaction is expected to improve, and the number of new users will increase through word of mouth and reviews. In this way, this innovative recommendation system, which combines psychological analysis and AI, is easy for users to use and is an effective tool for publishers, thanks to its interactive interface design that prioritizes user experience. This allows the AI ​​agent system to analyze the user's preferences and psychological tendencies and recommend the most suitable manga.

[0065] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a recommendation unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives text data entered by the user. The reception unit can also use speech recognition technology to convert the user's voice input into text data and receive it. Furthermore, the reception unit can use image recognition technology to analyze image data uploaded by the user, extract necessary information, and receive it. For example, the reception unit analyzes the text data entered by the user using natural language processing technology and extracts information about the user's preferences and psychological tendencies. In the case of voice input, the reception unit uses speech recognition technology to convert the voice data into text data and analyzes that text data. In the case of image input, the reception unit uses image recognition technology to analyze the image data and extract information about the user's preferences and psychological tendencies. The analysis unit analyzes the information received by the reception unit and analyzes the user's psychological tendencies. The analysis unit analyzes user input data using, for example, natural language processing technology to estimate the user's emotions and interests. The analysis unit can also analyze user behavior patterns and identify user psychological tendencies using machine learning algorithms. For example, the analysis unit classifies user input data using clustering algorithms to group user interests and concerns. Furthermore, the analysis unit can learn from the user's past input data to predict user psychological tendencies. The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. For example, the extraction unit analyzes manga data in the database and extracts patterns that match the user's psychological tendencies. The extraction unit can also cluster manga data in the database using machine learning algorithms to identify the most suitable manga for the user. For example, the extraction unit extracts manga related to specific genres or themes based on the user's psychological tendencies. Furthermore, the extraction unit can recommend the most suitable manga to the user, taking into account the user's past browsing history and evaluation data. The recommendation unit recommends the most suitable manga based on the patterns extracted by the extraction unit.The recommendation unit, for example, uses a collaborative filtering algorithm to recommend the most suitable manga to the user, taking into account the evaluation data of other users. The recommendation unit can also use a content-based recommendation algorithm to recommend the most suitable manga based on the user's preferences and psychological tendencies. For example, the recommendation unit recommends the most suitable manga to the user based on the user's past browsing history and evaluation data. Furthermore, the recommendation unit can recommend the most suitable manga in real time, taking into account the user's current areas of interest and behavioral patterns. The delivery unit provides the manga recommended by the recommendation unit to the user. The delivery unit displays the recommended manga to the user, for example, through a web application or mobile application. The delivery unit can also notify the user of the recommended manga using email or push notifications. For example, the delivery unit displays the recommended manga on the top page when the user accesses the application. Furthermore, the delivery unit can customize and display the recommended manga according to the user's preferences. As a result, the AI ​​agent system according to this embodiment can analyze the user's psychological tendencies based on the user's input information and recommend the most suitable manga.

[0066] The reception desk accepts user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts text data entered by the user. The reception desk can also use speech recognition technology to convert the user's voice input into text data and accept it. Furthermore, the reception desk can use image recognition technology to analyze image data uploaded by the user, extract necessary information, and accept it. For example, the reception desk analyzes text data entered by the user using natural language processing technology to extract information about the user's preferences and psychological tendencies. In the case of voice input, the reception desk uses speech recognition technology to convert the voice data into text data and then analyzes that text data. In the case of image input, the reception desk uses image recognition technology to analyze the image data and extract information about the user's preferences and psychological tendencies. The reception desk provides multiple interfaces for accepting user input. For example, it allows users to easily input information through a web browser or mobile application. In the case of text input, the user enters text using a keyboard, and in the case of voice input, the user enters voice using a microphone. For image input, users select and upload images from their camera or gallery. The reception desk receives and processes this input data in real time. Speech recognition technology uses deep learning algorithms to convert user speech into text with high accuracy. Image recognition technology uses convolutional neural networks (CNNs) to extract features from user-uploaded images and identify user preferences and psychological tendencies. This allows the reception desk to handle diverse user input formats and receive information accurately and quickly. Furthermore, the reception desk securely manages user input data and implements security measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. This allows the reception desk to gain user trust and provide a system that can be used with peace of mind.

[0067] The analysis department analyzes information received by the reception department to analyze the user's psychological tendencies. For example, the analysis department uses natural language processing technology to analyze user input data and estimate the user's emotions and interests. The analysis department can also use machine learning algorithms to analyze user behavior patterns and identify user psychological tendencies. For example, the analysis department classifies user input data using clustering algorithms to group user interests and concerns. Furthermore, the analysis department can learn from the user's past input data to predict user psychological tendencies. The analysis department uses natural language processing technology to analyze user text data. Specifically, it performs morphological and grammatical analysis to understand the user's input. It uses sentiment analysis algorithms to classify the user's emotions as positive, negative, neutral, etc. It also uses topic modeling technology to identify topics of interest to the user. In the case of audio data, it converts it to text data using speech recognition technology and then applies similar natural language processing technology. In the case of image data, it uses image recognition technology to identify objects and scenes within the image and analyze the user's preferences and psychological tendencies. It uses machine learning algorithms to analyze user behavior patterns. For example, based on users' past input data and behavioral history, clustering algorithms are used to classify users into similar groups. This allows for grouping users' interests and preferences, and providing optimal content to individual users. Furthermore, the analytics department uses recurrent neural network (RNN) and long-term short-term memory (LSTM) models to learn from users' past data and predict future behavior and psychological tendencies. This allows for real-time tracking of changes in users' interests and preferences, and the provision of appropriate content. The analytics department securely manages user data and takes measures to protect privacy. For example, data anonymization and access control are implemented to protect users' personal information. This allows the analytics department to gain users' trust and provide a system that can be used with confidence.

[0068] The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. For example, the extraction unit analyzes manga data in the database and extracts patterns that match the user's psychological tendencies. The extraction unit can also use machine learning algorithms to cluster the manga data in the database and identify the manga best suited to the user. For example, the extraction unit extracts manga related to specific genres or themes based on the user's psychological tendencies. Furthermore, the extraction unit can recommend the best manga to the user by considering the user's past browsing history and rating data. The extraction unit uses indexing techniques to efficiently search the manga data in the database and identify patterns that match the user's psychological tendencies. For example, metadata such as genre, theme, author, and rating are indexed for the manga data to speed up the search. Machine learning algorithms are used to cluster the manga data in the database. Specifically, K-means clustering or hierarchical clustering is used to group similar manga. This allows for the efficient extraction of manga related to specific genres or themes based on the user's psychological tendencies. Furthermore, the extraction unit uses a collaborative filtering algorithm to recommend the most suitable manga to the user, taking into account the user's past browsing history and rating data. For example, it compares the user's rating data with that of other users and refers to recommendations from users with similar rating patterns. This allows it to identify and recommend the most suitable manga for the user. The extraction unit regularly updates and maintains the database to reflect the latest manga data. This ensures that recommendations are always based on the most up-to-date information. In addition, the extraction unit collects user feedback and continuously improves the accuracy of the recommendation algorithm. For example, it analyzes the ratings and comments users have made on recommended manga and adjusts the parameters of the recommendation algorithm. This allows the extraction unit to provide the most suitable manga according to the user's preferences and improve user satisfaction.

[0069] The recommendation unit recommends the most suitable manga based on patterns extracted by the extraction unit. For example, the recommendation unit uses a collaborative filtering algorithm to recommend the most suitable manga to the user, referencing evaluation data from other users. The recommendation unit can also use a content-based recommendation algorithm to recommend the most suitable manga based on the user's preferences and psychological tendencies. For example, the recommendation unit recommends the most suitable manga to the user based on the user's past browsing history and evaluation data. Furthermore, the recommendation unit can recommend the most suitable manga in real time, taking into account the user's current areas of interest and behavioral patterns. The recommendation unit uses a collaborative filtering algorithm to reference evaluation data from other users. Specifically, it represents user evaluation data in matrix form and performs similarity calculations. For example, it calculates similarity between users using cosine similarity or Pearson correlation coefficients, and makes recommendations based on the evaluation data of users with high similarity. This allows it to recommend the most suitable manga to the user. It also uses a content-based recommendation algorithm to make recommendations based on the user's preferences and psychological tendencies. Specifically, it represents the user's past browsing history and evaluation data as feature vectors and recommends manga with similar features. For example, using TF-IDF or word embedding, the content of manga is vectorized to identify manga that match the user's preferences. The recommendation system monitors the user's behavior patterns in real time and makes recommendations based on their current areas of interest. For example, if a user frequently views manga of a particular genre, it will recommend new manga related to that genre. Furthermore, if the user's behavior patterns change, the recommendation algorithm is dynamically adjusted to provide recommendations based on their latest areas of interest. The recommendation system collects user feedback and continuously improves the accuracy of the recommendation algorithm. For example, it analyzes the ratings and comments users make on recommended manga and adjusts the parameters of the recommendation algorithm. This allows the recommendation system to provide the most suitable manga according to the user's preferences and improve user satisfaction.

[0070] The provider provides users with manga recommended by the recommendation team. The provider displays recommended manga to users, for example, through web applications and mobile applications. The provider can also notify users of recommended manga via email and push notifications. For example, when a user accesses the service, the provider displays recommended manga on the homepage. Furthermore, the provider can customize the display of recommended manga according to the user's preferences. The provider designs user interfaces for displaying recommended manga to users through web and mobile applications. Specifically, it provides an intuitive interface that displays a list of recommended manga. When a user clicks on a manga they are interested in, they can access detailed information and sample readings. The provider notifies users of recommended manga via email and push notifications. For example, when a user receives a new recommendation, it sends an email notification, allowing them to access the web application by clicking a link. It also uses push notifications to send real-time notifications to mobile applications, allowing users to immediately check recommended manga. The provider customizes the display of recommended manga according to the user's preferences. For example, the system prioritizes displaying manga that users are likely to be interested in, based on their past browsing history and rating data. It also collects user feedback and continuously improves the displayed content. For instance, if a user shows interest in a particular genre or theme, the system prioritizes displaying manga related to that genre or theme. The service provider securely manages user data and takes measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. This allows the service provider to gain user trust and provide a system that users can use with confidence.

[0071] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can prompt input at a time when the user can relax. For example, the reception unit can send a notification prompting input when the user is relaxed. The reception unit can also accept input immediately if the user is excited. For example, the reception unit can immediately display an input form when the user is excited. The reception unit can also prompt input after the user has taken a break if the user is tired. For example, the reception unit can display a message prompting the user to take a break when the user is tired and send a notification prompting input again after a certain period of time. By adjusting the timing of input acceptance according to the user's emotions, it is possible to reduce user stress and provide a comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0072] The reception desk can analyze the user's past input history and select the most suitable reception method. For example, the reception desk can prioritize presenting input methods that the user has frequently used in the past. For instance, if the reception desk has frequently used text input in the past, it will prioritize presenting text input. The reception desk can also analyze the user's past input times and prompt them to input at the most suitable time. For example, if the reception desk has previously entered information at night, it will send a notification prompting them to input at night. The reception desk can also automatically complete input fields by referring to the user's past input. For example, the reception desk will automatically complete input fields based on information the user has entered in the past. This allows the reception desk to select the most suitable reception method by analyzing the user's past input history, thereby improving user convenience. 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 past input history data into a generating AI and have the generating AI select the most suitable reception method.

[0073] The reception unit can filter input based on the user's current areas of interest. For example, the reception unit can prioritize displaying input items related to genres the user has recently been interested in. For example, the reception unit can display input items related to genres the user has recently been interested in at the top. The reception unit can also customize input items based on the user's current areas of interest. For example, the reception unit can customize the order and content of input items based on the user's areas of interest. The reception unit can also automatically retrieve information related to the user's areas of interest and simplify input. For example, the reception unit can automatically retrieve information related to the user's areas of interest and automatically complete input items. This allows the reception unit to prioritize receiving highly relevant information by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0074] The reception desk can estimate the user's emotions and determine the priority of the information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. For example, when the user is stressed, the reception desk will prioritize displaying important information. The reception desk can also prioritize receiving detailed information if the user is relaxed. For example, when the user is relaxed, the reception desk will prioritize displaying detailed information. The reception desk can also prioritize receiving information that can be processed quickly if the user is in a hurry. For example, when the user is in a hurry, the reception desk will prioritize displaying information that can be processed quickly. In this way, by determining the priority of the information to receive according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0075] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. For example, when the user is in a specific region, the reception unit will display information related to that region. The reception unit can also receive region-specific information based on the user's location. For example, the reception unit will display region-specific information based on the user's location. The reception unit can also prioritize receiving information related to the user's travel destination if the user is traveling. For example, when the user is traveling, the reception unit will display information related to the user's travel destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0076] The reception unit can analyze the user's social media activity and receive relevant information when receiving input. For example, the reception unit can present relevant input fields based on information the user has shared on social media. The reception unit can also customize input fields by referring to the activities of the user's social media followers and friends. The reception unit can also analyze the content of the user's social media posts and automatically retrieve relevant information. This allows the reception unit to prioritize receiving relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is relaxed, the analysis unit can display detailed analysis results. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can display concise analysis results that get straight to the point if the user is excited. The analysis unit can also provide visually appealing analysis results if the user is excited. For example, if the analysis unit is excited, the analysis unit can display visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0078] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on important information. For example, the analysis unit can provide detailed analysis results for important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit can provide concise analysis results for general information. The analysis unit can also adjust the level of detail of its analysis according to the user's level of interest. For example, the analysis unit can adjust the level of detail of its analysis according to the user's level of interest. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information. 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 information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms to different genres of manga depending on the user's preferences. The analysis unit can also select an appropriate analysis algorithm based on the user's psychological tendencies. The analysis unit can also apply the optimal analysis algorithm depending on the category of information. By applying different analysis algorithms depending on the category of information, the optimal analysis results can be provided. 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 information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, when the user is in a hurry, the analysis unit displays a short, concise analysis result. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, when the user is relaxed, the analysis unit displays a detailed analysis result. The analysis unit can also provide a visually appealing analysis if the user is excited. For example, when the user is excited, the analysis unit displays a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0081] The analysis unit can determine the priority of analysis based on the timing of information submission. For example, the analysis unit can prioritize the analysis of the latest information. The analysis unit can also postpone the analysis of older information. The analysis unit can also adjust the priority of analysis according to the timing of information submission. This allows the analysis unit to prioritize the analysis of the latest information by determining the priority of analysis based on the timing of information submission. 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 information submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, highly relevant information can be prioritized. 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 information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The extraction unit can estimate the user's emotions and adjust the extraction criteria based on the estimated emotions. For example, if the user is relaxed, the extraction unit can extract detailed information. For example, if the user is in a hurry, the extraction unit can extract concise information. For example, if the user is in a hurry, the extraction unit can extract concise information. For example, if the user is excited, the extraction unit can extract visually appealing information. For example, if the user is excited, the extraction unit can extract visually appealing information. In this way, by adjusting the extraction criteria according to the user's emotions, information appropriate for the user can be extracted. Emotion estimation is achieved using an emotion estimation function, for example, 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 extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's facial expression data into a generating AI, allowing the generating AI to estimate the user's emotions.

[0084] The extraction unit can improve the accuracy of extraction by considering the interrelationships of information during the extraction process. For example, the extraction unit can group related information together and extract it. For example, the extraction unit can group related information together and extract it. The extraction unit can also analyze the interrelationships of information and perform highly accurate extraction. For example, the extraction unit can analyze the interrelationships of information and perform highly accurate extraction. The extraction unit can also perform optimal extraction based on the relationships between the information. For example, the extraction unit can perform optimal extraction based on the relationships between the information. In this way, by considering the interrelationships of information, highly accurate information can be extracted. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input interrelationship data of information into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0085] The extraction unit can perform extraction while considering the attribute information of the information submitter. For example, the extraction unit can extract highly reliable information based on the submitter's expertise. The extraction unit can also extract important information by considering the submitter's past performance. The extraction unit can also extract optimal information based on the submitter's attribute information. This makes it possible to extract highly reliable information by considering the attribute information of the information submitter. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the extraction.

[0086] The extraction unit can estimate the user's emotions and adjust the order in which the extraction results are displayed based on the estimated emotions. For example, if the user is relaxed, the extraction unit may prioritize displaying detailed information. For example, if the user is relaxed, the extraction unit may prioritize displaying detailed information. For example, if the user is in a hurry, the extraction unit may prioritize displaying concise information. For example, if the user is excited, the extraction unit may prioritize displaying visually appealing information. For example, if the user is excited, the extraction unit may prioritize displaying visually appealing information. In this way, by adjusting the order in which the extraction results are displayed according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's facial expression data into a generating AI, allowing the generating AI to estimate the user's emotions.

[0087] The extraction unit can perform extraction while considering the geographical distribution of information. For example, the extraction unit can prioritize extracting information that is geographically close. The extraction unit can also extract highly relevant information based on geographical distribution. The extraction unit can also extract the most relevant information by considering geographical factors. This allows for the extraction of highly relevant information by considering the geographical distribution of information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data of information into a generating AI and have the generating AI perform the extraction.

[0088] The extraction unit can improve the accuracy of its extraction by referring to related literature during the extraction process. For example, the extraction unit can extract highly reliable information by referring to related literature. The extraction unit can also improve the accuracy of the information based on related literature. The extraction unit can also extract the most suitable information by considering related literature. This allows for the extraction of highly reliable information by referring to related literature. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input related literature data into a generating AI and have the generating AI perform the extraction.

[0089] The recommendation section can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is relaxed, the recommendation section can provide detailed recommendations. For example, if the user is relaxed, the recommendation section can display detailed recommendations. The recommendation section can also provide concise recommendations that get straight to the point if the user is in a hurry. For example, if the user is in a hurry, the recommendation section can display concise recommendations that get straight to the point if the user is excited. The recommendation section can also provide visually appealing recommendations if the user is excited. For example, if the recommendation section is excited, the recommendation section can display visually appealing recommendations. By adjusting the way recommendations are presented according to the user's emotions, recommendations that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 recommendation section may be performed using AI, for example, or without AI. For example, the recommendation system can input user facial expression data into a generative AI and have the AI ​​estimate the user's emotions.

[0090] The recommendation system can adjust the level of detail of recommendations based on the importance of the information. For example, the recommendation system can provide detailed recommendations for important information. For example, the recommendation system can display detailed recommendations for important information. The recommendation system can also provide concise recommendations for general information. For example, the recommendation system can display concise recommendations for general information. The recommendation system can also adjust the level of detail of recommendations according to the user's level of interest. For example, the recommendation system can adjust the level of detail of recommendations according to the user's level of interest. This allows the recommendation system to provide detailed recommendations for important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of recommendations.

[0091] The recommendation system can apply different recommendation algorithms depending on the category of information during the recommendation process. For example, the recommendation system can apply different recommendation algorithms to different genres of manga depending on the user's preferences. The recommendation system can also select an appropriate recommendation algorithm based on the user's psychological tendencies. The recommendation system can also apply the optimal recommendation algorithm depending on the category of information. By applying different recommendation algorithms depending on the category of information, the system can provide optimal recommendation results. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input information category data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0092] The recommendation section can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation section can provide short, concise recommendations. For example, when the user is in a hurry, the recommendation section displays short, concise recommendations. The recommendation section can also provide detailed recommendations if the user is relaxed. For example, when the user is relaxed, the recommendation section displays detailed recommendations. The recommendation section can also provide visually appealing recommendations if the user is excited. For example, when the user is excited, the recommendation section displays visually appealing recommendations. By adjusting the length of recommendations according to the user's emotions, the recommendation section can provide recommendations of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 recommendation section may be performed using AI, for example, or without AI. For example, the recommendation system can input user facial expression data into a generative AI and have the AI ​​estimate the user's emotions.

[0093] The recommendation department can determine the priority of recommendations based on the information submission date. For example, the recommendation department will prioritize the most recent information. The recommendation department can also postpone recommending older information. The recommendation department can also adjust the priority of recommendations according to the information submission date. By determining the priority of recommendations based on the information submission date, the recommendation department can prioritize recommending the most recent information. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input information submission date data into a generating AI and have the generating AI determine the priority of recommendations.

[0094] The recommendation unit can adjust the order of recommendations based on the relevance of the information. For example, the recommendation unit can prioritize recommending highly relevant information. The recommendation unit can also postpone recommending less relevant information. The recommendation unit can also adjust the order of recommendations according to the relevance of the information. By adjusting the order of recommendations based on the relevance of the information, highly relevant information can be recommended preferentially. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0095] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the delivery unit can select a detailed delivery method. For example, if the user is relaxed, the delivery unit can select a detailed delivery method. The delivery unit can also select a concise delivery method if the user is in a hurry. For example, if the user is in a hurry, the delivery unit can select a concise delivery method. The delivery unit can also select a visually appealing delivery method if the user is excited. For example, if the user is excited, the delivery unit can select a visually appealing delivery method. In this way, by adjusting the delivery method according to the user's emotions, the optimal delivery method for the user can be selected. Emotion estimation is achieved using an emotion estimation function, for example, 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 delivery unit may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0096] The service delivery unit can analyze the user's past behavior history at the time of delivery to select the optimal delivery method. For example, the service delivery unit can prioritize selecting delivery methods that the user has used in the past. The service delivery unit can also analyze the user's past behavior history and propose the optimal delivery method. The service delivery unit can also customize the delivery method based on the user's behavior patterns. By analyzing the user's past behavior history, the service delivery unit can select the optimal delivery method and improve user convenience. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal delivery method.

[0097] The service provider can customize the means of delivery based on the user's current areas of interest at the time of delivery. For example, the service provider can customize the delivery method based on the user's current areas of interest. The service provider can also prioritize providing information related to the user's areas of interest. For example, the service provider can prioritize providing information related to the user's areas of interest. The service provider can also select the means of delivery according to the user's areas of interest. For example, the service provider can select the means of delivery according to the user's areas of interest. By customizing the means of delivery based on the user's current areas of interest, the service provider can provide the user with the most suitable information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0098] The service provider can estimate the user's emotions and determine the priority of its offerings based on those emotions. For example, if the user is relaxed, the service provider may prioritize providing detailed information. For example, if the user is relaxed, the service provider may prioritize providing detailed information. The service provider may also prioritize providing concise information if the user is in a hurry. For example, if the user is in a hurry, the service provider may prioritize providing concise information. The service provider may also prioritize providing visually appealing information if the user is excited. For example, if the user is excited, the service provider may prioritize providing visually appealing information. In this way, by determining the priority of offerings according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, 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 these examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0099] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing information related to that region. The service provider can also provide region-specific information based on the user's location information. The service provider can also provide information related to the user's travel destination if the user is traveling. By considering the user's geographical location information, the service provider can provide highly relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal service delivery method.

[0100] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can propose a relevant means of delivery based on information shared by the user on social media. The service provider can also customize the means of delivery by referring to the activities of the user's social media followers and friends. The service provider can also analyze the content of the user's social media posts and propose a relevant means of delivery. In this way, by analyzing the user's social media activity, a relevant means of delivery can be proposed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of means of delivery.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can analyze a user's past input history when receiving user input and select the most suitable reception method. For example, it can prioritize presenting input methods that the user has frequently used in the past. The reception desk can also analyze the user's past input times and prompt them to input at the most optimal time. Furthermore, the reception desk can automatically complete input fields by referring to the user's past input content. This allows the reception desk to select the most suitable reception method by analyzing the user's past input history, thereby improving user convenience.

[0103] The reception system can estimate the user's emotions and adjust the timing of input requests based on those estimates. For example, if the user is stressed, it can prompt for input at a time when they can relax. Conversely, if the user is excited, it can prompt for input immediately. Furthermore, if the user is tired, it can prompt for input after a break. By adjusting the timing of input requests according to the user's emotions, it is possible to reduce user stress and provide a comfortable input experience.

[0104] The reception system can filter input based on the user's current areas of interest. For example, it can prioritize displaying input fields related to genres the user has recently been interested in. It can also customize input fields based on the user's current areas of interest. Furthermore, it can automatically retrieve information related to the user's areas of interest and simplify the input process. This allows the system to prioritize receiving highly relevant information by filtering based on the user's current areas of interest.

[0105] The reception desk can estimate the user's emotions and prioritize the information to be received based on those emotions. For example, if the user is stressed, important information will be prioritized. If the user is relaxed, detailed information may be prioritized. Furthermore, if the user is in a hurry, information that can be processed quickly may be prioritized. In this way, by prioritizing the information received according to the user's emotions, important information can be prioritized.

[0106] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, it will prioritize receiving information related to that region. It can also prioritize receiving region-specific information based on the user's location. Furthermore, if the user is traveling, it can prioritize receiving information related to their travel destination. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant information.

[0107] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0108] The analysis unit can adjust the level of detail in its analysis based on the importance of the information. For example, it can perform a detailed analysis on important information, and a concise analysis on general information. Furthermore, it can adjust the level of detail in its analysis according to the user's level of interest. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information.

[0109] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, it can apply different analysis algorithms to different genres of manga depending on the user's preferences. It can also select an appropriate analysis algorithm based on the user's psychological tendencies. Furthermore, it can apply the optimal analysis algorithm depending on the category of information. By applying different analysis algorithms depending on the category of information, it is possible to provide optimal analysis results.

[0110] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, it can provide a short, to-the-point analysis. If the user is relaxed, it can provide a detailed analysis. Furthermore, if the user is excited, it can provide a visually engaging analysis. By adjusting the length of the analysis according to the user's emotions, it is possible to provide analysis results of an appropriate length for the user.

[0111] The analysis department can prioritize analysis based on when the information was submitted. For example, the most recent information can be analyzed first, while older information can be postponed. Furthermore, the analysis priority can be adjusted according to when the information was submitted. This allows for prioritizing the analysis of the most recent information by determining the analysis priority based on when the information was submitted.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk receives text data entered by the user. It can also use speech recognition technology to convert the user's voice input into text data and accept it. Furthermore, it can use image recognition technology to analyze image data uploaded by the user, extract the necessary information, and accept it. Step 2: The analysis department analyzes the information received by the reception department to analyze the user's psychological tendencies. For example, natural language processing technology is used to analyze the user's input data and estimate the user's emotions and interests. Machine learning algorithms can also be used to analyze the user's behavior patterns and identify the user's psychological tendencies. Step 3: The extraction unit extracts patterns from the database based on the information analyzed by the analysis unit. For example, it analyzes manga data in the database and extracts patterns that match the user's psychological tendencies. It can also use machine learning algorithms to cluster the manga data in the database and identify the manga that is best suited to the user. Step 4: The recommendation unit recommends the most suitable manga based on the patterns extracted by the extraction unit. For example, it can use a collaborative filtering algorithm to recommend the most suitable manga to the user by referring to evaluation data from other users. Alternatively, it can use a content-based recommendation algorithm to recommend the most suitable manga based on the user's preferences and psychological tendencies. Step 5: The distribution team provides users with the manga recommended by the recommendation team. For example, they display the recommended manga to users through web applications or mobile applications. They can also notify users of the recommended manga via email or push notifications.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, recommendation unit, and provision 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 control unit 46A of the smart device 14 and receives user input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's psychological tendencies. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts patterns from the database. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable manga. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the recommended manga to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, recommendation unit, and provision 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 control unit 46A of the smart glasses 214 and receives user input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's psychological tendencies. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts patterns from the database. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable manga. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the recommended manga to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

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

[0137] The 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.

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

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

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

[0141] Figure 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.

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

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

[0144] In the 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.

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

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0148] The data processing system 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.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, recommendation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's psychological tendencies. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts patterns from the database. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable manga. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the recommended manga to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

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

[0153] The 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.

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

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, recommendation unit, and provision 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 control unit 46A of the robot 414 and receives user input. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the user's psychological tendencies. The extraction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts patterns from the database. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and recommends the most suitable manga. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the recommended manga to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception area that receives user input, An analysis unit analyzes the information received by the reception unit and analyzes the user's psychological tendencies, Based on the information analyzed by the aforementioned analysis unit, an extraction unit extracts patterns from the database, Based on the patterns extracted by the extraction unit, a recommendation unit recommends the most suitable manga, The system comprises a provisioning unit that provides users with manga recommended by the aforementioned recommendation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving input, the system filters it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The extraction unit is We estimate the user's emotions and adjust the extraction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The extraction unit is During extraction, the interrelationships between pieces of information are taken into consideration to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 16) The extraction unit is During extraction, the attribute information of the information submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is It estimates the user's sentiment and adjusts the order in which the extraction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The extraction unit is During extraction, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is During extraction, we improve the accuracy of the extraction by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making a recommendation, priority will be determined based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's past behavior history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit analyzes the information received by the reception unit and analyzes the user's psychological tendencies, Based on the information analyzed by the aforementioned analysis unit, an extraction unit extracts patterns from the database, Based on the patterns extracted by the extraction unit, a recommendation unit recommends the most suitable manga, The system comprises a provisioning unit that provides users with manga recommended by the aforementioned recommendation unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

4. The aforementioned reception unit is When receiving input, the system filters it based on the user's current areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the information. The system according to feature 1.

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