system

Abstract

The system according to this embodiment aims to suggest the most suitable book to the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a suggestion unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the user's past reading history, areas of interest, and current concerns based on the information received by the reception unit. The suggestion unit suggests the most suitable book based on the analysis results obtained by the analysis unit. The generation unit generates summaries and reviews of the books suggested by the suggestion unit. The provision unit provides the summaries and reviews generated by the generation 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there is a problem that it is difficult for a user to find the most suitable book for themselves.

[0005] The system according to the embodiment aims to propose the most suitable book for the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the user's past reading history, areas of interest, and current concerns based on the information received by the reception unit. The suggestion unit suggests the most suitable book based on the analysis results obtained by the analysis unit. The generation unit generates summaries and reviews of the books suggested by the suggestion unit. The provision unit provides the summaries and reviews generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable book to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] [[ID=2I]] 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 service according to an embodiment of the present invention is a system that suggests books needed in bookstores and libraries. When a user visits a bookstore or library, the AI ​​agent receives input from the user. Next, the AI ​​agent analyzes data such as the user's past reading history, areas of interest, and current concerns. Based on this analysis of the user's desires and concerns, the AI ​​agent suggests the most suitable books. Furthermore, the AI ​​agent generates summaries and reviews of the recommended books and provides them to the user. This mechanism allows busy people, indecisive people, and those with concerns to find the necessary books in a short amount of time. For example, a user might input, "I want a book to relieve work stress." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes data such as the user's past reading history, areas of interest, and current concerns. The AI ​​agent utilizes natural language processing technology to analyze the user's desires and concerns from the input. For example, it considers data on books the user has read in the past, their current areas of interest, and their concerns to suggest the most suitable books. Furthermore, the AI ​​agent generates summaries and reviews of the recommended books and provides them to the user. For example, an AI agent can generate summaries and reviews of books recommended as "books to relieve work stress" and provide them to the user. This allows the user to understand the content of the recommended books in advance. This system enables busy people, indecisive people, and people with worries to find the books they need in a short amount of time. For example, a user who wants to relieve work stress can find the perfect book by receiving suggestions from an AI agent at a bookstore or library. Also, by understanding the summaries and reviews of the books recommended by the AI ​​agent in advance, the user can choose a book that suits them. In this way, using an AI agent makes it possible to suggest books that fit the user's needs, encouraging reading habits and supporting individual growth. For example, by having an AI agent analyze a user's worries and suggest the most suitable books, it is possible to reduce the user's worries and improve their knowledge and happiness. In this way, AI agent services can efficiently find the books that users need.

[0029] The AI ​​agent service according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a generation 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, if a user inputs "I want a book to relieve work stress," the reception unit receives that information. The analysis unit analyzes the user's past reading history, areas of interest, and current concerns based on the information received by the reception unit. For example, the analysis unit retrieves the user's past reading history from a database and analyzes their areas of interest and current concerns. The analysis unit uses natural language processing technology to analyze the user's input and identify the user's wishes and concerns. The suggestion unit suggests the most suitable book based on the analysis results obtained by the analysis unit. For example, the suggestion unit uses an algorithm to recommend the most suitable book based on the user's areas of interest and past reading history. The suggestion unit can also suggest a book that addresses the user's current concerns. The generation unit generates summaries and reviews of the books suggested by the suggestion unit. The generation unit generates a book summary using, for example, a summarization algorithm, and generates a review based on review evaluation criteria. The generation unit allows the user to understand the content of the suggested book in advance. The delivery unit provides the summary and review generated by the generation unit to the user. The delivery unit, for example, sends a notification to the user's device and displays the generated summary and review. The delivery unit allows the user to confirm the content of the suggested book. As a result, the AI ​​agent service according to the embodiment allows the user to efficiently find the book they need.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, if a user inputs "I want a book to relieve work stress," the reception unit receives that information. Specifically, in the case of text input, it receives the string of characters entered by the user using a keyboard or touchscreen; in the case of voice input, it converts the voice data recorded through the microphone into text using speech recognition technology; in the case of image input, it receives images or screenshots taken by the user and analyzes the content using image recognition technology. The reception unit centrally manages this input data and performs preprocessing to accurately understand the user's intent. For example, in the case of voice input, it performs noise reduction and speech normalization; in the case of text input, it performs spell checking and grammatical analysis. Furthermore, the reception unit converts the user's input content into an appropriate format and prepares it for passing on to the subsequent analysis unit. This allows the reception unit to handle diverse user input formats and receive information accurately and efficiently.

[0031] The analysis department analyzes users' past reading history, areas of interest, and current concerns based on information received by the reception department. For example, the analysis department retrieves users' past reading history from a database and analyzes their areas of interest and current concerns. Specifically, the user's reading history database stores information such as the titles, authors, genres, and ratings of books read in the past, and uses this data to understand the user's reading tendencies. For the analysis of areas of interest, natural language processing technology is used to extract specific keywords and themes from the user's input and past reading history. For example, if a user enters the keyword "stress relief," the analysis department identifies books that deal with similar themes among the books the user has read in the past and recognizes them as the user's area of ​​interest. For the analysis of current concerns, the analysis department analyzes the user's input in detail to identify specific concerns and desires. For example, from the input "I want a book to relieve work stress," the analysis department identifies that the user is experiencing work-related stress and extracts information to suggest books that can help alleviate it. In this way, the analysis department can comprehensively analyze the user's past reading history, areas of interest, and current concerns and provide basic information to make suggestions that are best suited to the user's needs.

[0032] The recommendation department suggests the most suitable books based on the analysis results obtained by the analysis department. The recommendation department uses algorithms that recommend the most suitable books based on, for example, the user's areas of interest and past reading history. Specifically, the recommendation algorithm selects books that match the user's preferences using methods such as collaborative filtering and content-based filtering. Collaborative filtering recommends books that have been highly rated by users with similar preferences, based on the reading history and ratings of other users. On the other hand, content-based filtering recommends books with similar content based on the user's past reading history and areas of interest. Furthermore, the recommendation department can also suggest books that address the user's current concerns. For example, if a user enters "I want a book to relieve work stress," it will suggest books on stress relief, self-help books, and relaxation books. Based on this information, the recommendation department lists the most suitable books for the user and presents them to the user. In this way, the recommendation department can efficiently suggest books that are best suited to the user's needs and help the user find the book they need.

[0033] The generation unit generates summaries and reviews of books proposed by the proposal unit. For example, the generation unit generates summaries using a summarization algorithm and reviews based on review evaluation criteria. Specifically, the summarization algorithm extracts key points and summarizes them concisely so that the content of the book can be grasped in a short time. For example, it extracts the main points and main themes of each chapter of the book and combines them to generate an overall summary. In addition, for review generation, it creates reviews according to evaluation criteria based on past reader ratings and comments. For example, it analyzes reader ratings and comments and generates reviews that incorporate a good balance of positive and negative points. Furthermore, the generation unit can also create presentations that incorporate visual elements so that users can understand the content of the proposed books in advance. For example, it provides detailed information including the book cover image, table of contents, and author's biography. In this way, the generation unit can provide information that allows users to understand the content of the proposed books in advance and use it as a reference for their selection.

[0034] The delivery unit provides users with summaries and reviews generated by the generation unit. For example, the delivery unit sends notifications to the user's device to display the generated summaries and reviews. Specifically, it provides information to the user's device, such as a smartphone, tablet, or PC, through push notifications, email, or in-app notifications. The delivery unit provides an interactive interface so that users can check the content of the suggested books. For example, when a user taps a notification, a detailed summary and review are displayed, along with information about related books and purchase links. The delivery unit can also collect user feedback and use it to improve the service. For example, users can leave ratings and comments on suggested books, and this information can be used to improve the accuracy of the suggestion and generation algorithms. This allows the delivery unit to provide users with information quickly and accurately, and to help users efficiently find the books they need.

[0035] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In addition, the reception desk can customize input methods based on the user's past input. This allows for the selection of the optimal input 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, or not. 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 optimal input method.

[0036] The reception unit can filter input based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception unit filters relevant information based on their areas of interest. Furthermore, if the user has a specific area of ​​interest, the reception unit can prioritize displaying information related to that area. In addition, the reception unit can perform appropriate filtering according to the user's current situation and provide the necessary information. This allows for the efficient provision of necessary information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize receiving input that is highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. The reception unit can also filter and prioritize the display of relevant information based on the user's current location. Furthermore, if the user is on the move, the reception unit can prioritize receiving information appropriate to their current location. In this way, by considering the user's geographical location information, 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 information data into a generating AI and have the generating AI prioritize receiving highly relevant input.

[0038] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can analyze the user's areas of interest from their social media activity and prioritize accepting relevant inputs. The reception unit can also suggest relevant inputs based on information the user has shared on social media. Furthermore, the reception unit can analyze the user's social media activity and suggest the optimal input method. This allows for the priority acceptance of relevant inputs 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 prioritize the acceptance of relevant inputs.

[0039] The analysis unit can select the optimal analysis method by referring to the user's past reading history during analysis. For example, the analysis unit can select the optimal analysis method based on data of books the user has read in the past. The analysis unit can also identify areas of interest from the user's past reading history and select the optimal analysis method. Furthermore, the analysis unit can analyze the user's past reading history and select the most effective analysis method. In this way, by referring to the user's past reading history, the optimal analysis method is selected and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past reading history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0040] The analysis unit can improve the accuracy of its analysis based on the user's current concerns and areas of interest. For example, when the user inputs their current concerns, the analysis unit improves the accuracy of the analysis based on those concerns. The analysis unit can also prioritize the analysis of relevant information based on the user's areas of interest. Furthermore, the analysis unit can select the optimal analysis method considering the user's current concerns and areas of interest. This improves the accuracy of the analysis based on the user's current concerns and areas of interest, thereby obtaining more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current concerns and areas of interest data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0041] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. The analysis unit can also filter and analyze relevant information based on the user's current location. Furthermore, if the user is on the move, the analysis unit can prioritize analyzing information relevant to their current location. This allows for the prioritization of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can analyze areas of interest from the user's social media activity to improve accuracy. The analysis unit can also improve the accuracy of its analysis based on information shared by the user on social media. Furthermore, the analysis unit can analyze the user's social media activity and select the optimal analysis method. This allows the analysis to improve accuracy by referring to the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0043] The suggestion unit can select the optimal suggestion method by referring to the user's past reading history when making suggestions. For example, the suggestion unit can select the optimal suggestion method based on data of books the user has read in the past. The suggestion unit can also identify areas of interest from the user's past reading history and select the optimal suggestion method. Furthermore, the suggestion unit can analyze the user's past reading history and select the most effective suggestion method. In this way, by referring to the user's past reading history, the optimal suggestion method is selected and the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past reading history data into a generating AI and have the generating AI select the optimal suggestion method.

[0044] The suggestion unit can improve the accuracy of its suggestions based on the user's current concerns and areas of interest. For example, when the user inputs their current concerns, the suggestion unit improves the accuracy of its suggestions based on those concerns. The suggestion unit can also prioritize suggesting relevant information based on the user's areas of interest. Furthermore, the suggestion unit can select the optimal suggestion method considering the user's current concerns and areas of interest. This allows for more appropriate suggestions by improving the accuracy of suggestions based on the user's current concerns and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current concerns and areas of interest data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0045] The suggestion unit can make suggestions while considering the user's geographical location information. For example, if the user is in a specific region, the suggestion unit will prioritize suggesting information related to that region. The suggestion unit can also filter and suggest relevant information based on the user's current location. Furthermore, if the user is on the move, the suggestion unit can prioritize suggesting information appropriate to the user's current location. In this way, by considering the user's geographical location information, highly relevant information can be suggested preferentially. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of suggesting highly relevant information.

[0046] The suggestion unit can improve the accuracy of its suggestions by referencing the user's social media activity during the suggestion process. For example, the suggestion unit can analyze the user's areas of interest from their social media activity to improve accuracy. It can also improve the accuracy of its suggestions based on information shared by the user on social media. Furthermore, the suggestion unit can analyze the user's social media activity and select the optimal suggestion method. This allows the suggestion unit to improve the accuracy of its suggestions by referencing the user's social media activity. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0047] The generation unit can select the optimal generation method by referring to the user's past reading history during generation. For example, the generation unit can select the optimal generation method based on data of books the user has read in the past. The generation unit can also identify areas of interest from the user's past reading history and select the optimal generation method. Furthermore, the generation unit can analyze the user's past reading history and select the most effective generation method. In this way, by referring to the user's past reading history, the optimal generation method is selected and the accuracy of generation is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past reading history data into a generation AI and have the generation AI perform the selection of the optimal generation method.

[0048] The generation unit can improve the accuracy of its generation based on the user's current concerns and areas of interest during the generation process. For example, when the user inputs their current concerns, the generation unit improves the accuracy of its generation based on those concerns. The generation unit can also prioritize the generation of relevant information based on the user's areas of interest. Furthermore, the generation unit can select the optimal generation method considering the user's current concerns and areas of interest. This allows for the provision of more appropriate information by improving the accuracy of generation based on the user's current concerns and areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current concerns and areas of interest data into a generation AI and have the generation AI perform the generation accuracy improvement.

[0049] The generation unit can perform generation while considering the user's geographical location information. For example, if the user is in a specific region, the generation unit will prioritize generating information related to that region. The generation unit can also filter and generate relevant information based on the user's current location. Furthermore, if the user is on the move, the generation unit can prioritize generating information according to the user's current location. In this way, by considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the generation of highly relevant information.

[0050] The generation unit can improve the accuracy of its generation by referring to the user's social media activity during the generation process. For example, the generation unit can analyze the user's areas of interest from their social media activity to improve accuracy. It can also improve the accuracy of its generation based on information shared by the user on social media. Furthermore, the generation unit can analyze the user's social media activity and select the optimal generation method. This allows the generation unit to improve accuracy by referring to the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation accuracy improvement.

[0051] The delivery unit can select the optimal delivery method by referring to the user's past reading history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on data of books the user has read in the past. The delivery unit can also identify areas of interest from the user's past reading history and select the optimal delivery method. Furthermore, the delivery unit can analyze the user's past reading history and select the most effective delivery method. In this way, by referring to the user's past reading history, the optimal delivery method can be selected and the accuracy of the delivery can be improved. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past reading history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0052] The information delivery unit can improve the accuracy of its deliveries based on the user's current concerns and areas of interest. For example, when a user inputs their current concerns, the unit improves the accuracy of its deliveries based on those concerns. The unit can also prioritize providing relevant information based on the user's areas of interest. Furthermore, the unit can select the optimal delivery method considering the user's current concerns and areas of interest. This allows the unit to provide more appropriate information by improving the accuracy of its deliveries based on the user's current concerns and areas of interest. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input data on the user's current concerns and areas of interest into a generating AI and have the generating AI perform the accuracy improvement of the deliveries.

[0053] The service provider can provide information while considering the user's geographical location. 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 filter and provide relevant information based on the user's current location. Furthermore, if the user is on the move, the service provider can prioritize providing information appropriate to their current location. This allows the service provider to prioritize providing highly relevant information by considering the user's geographical location. 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 perform the task of providing highly relevant information.

[0054] The service provider can improve the accuracy of its deliveries by referring to the user's social media activity at the time of delivery. For example, the service provider can analyze the user's areas of interest from their social media activity to improve accuracy. It can also improve the accuracy of its deliveries based on information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and select the optimal delivery method. This allows the service provider to improve the accuracy of its deliveries by referring to the user's social media activity. 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 perform the task of improving the accuracy of its deliveries.

[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 analytics department can include information about events and seminars that users have attended in the past, in addition to their past reading history. For example, if a user has attended a particular seminar, it can suggest books related to the seminar's theme. It can also suggest books related to events the user has attended in the past, taking into account the content of those events. Furthermore, it can analyze feedback from past events and seminars to more accurately identify the user's areas of interest. By including information about the user's past events and seminars in the analysis, it is possible to provide more accurate recommendations.

[0057] The generation unit can analyze not only the user's past reading history but also reviews and comments the user has written in the past. For example, it can analyze the content of reviews and comments the user has written in the past to identify the user's preferences and areas of interest. It can also suggest relevant books by considering the ratings of reviews and comments the user has written in the past. Furthermore, it can analyze the tone and sentiment of reviews and comments the user has written in the past to estimate the user's emotional state. As a result, by including the user's past reviews and comments in the analysis, it is possible to make more accurate suggestions.

[0058] The reception desk can analyze the user's device usage history when receiving user input and suggest the most suitable input method. For example, if a user has frequently used a smartphone in the past, it will prioritize suggesting smartphone input. Similarly, if a user has used a tablet in the past, it can suggest tablet input. Furthermore, if a user has used a desktop computer in the past, it can suggest desktop input. By analyzing the user's device usage history, the system can suggest the most suitable input method and improve user convenience.

[0059] The recommendation system can analyze not only the user's past reading history but also information about movies and TV shows the user has watched in the past. For example, if a user has watched a particular movie or TV show, it can suggest books related to that theme. It can also suggest related books considering the content of movies and TV shows the user has watched in the past. Furthermore, it can analyze feedback on movies and TV shows the user has watched in the past to more accurately identify the user's areas of interest. By including the user's past viewing history in the analysis, it is possible to make more accurate recommendations.

[0060] The service provider can analyze not only the user's past reading history but also information about online forums and discussions the user has previously participated in. For example, if a user has a history of participating in a particular online forum, the service can suggest books related to the forum's theme. It can also suggest books related to discussions the user has previously participated in, taking into account the content of those discussions. Furthermore, by analyzing feedback from online forums and discussions the user has previously participated in, the service can more accurately identify the user's areas of interest. This allows for more accurate recommendations by including the user's past online activities in the analysis.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives user input. User input includes text input, voice input, image input, etc. For example, if a user inputs "I want a book to relieve work stress," the reception desk receives that information. Step 2: The analysis department analyzes the user's past reading history, areas of interest, and current concerns based on the information received by the reception department. The analysis department retrieves the user's past reading history from the database and analyzes their areas of interest and current concerns. Using natural language processing technology, it analyzes the user's input to identify the user's wishes and concerns. Step 3: The recommendation unit suggests the most suitable books based on the analysis results obtained by the analysis unit. The recommendation unit uses an algorithm to recommend the most suitable books based on the user's areas of interest and past reading history. It can also suggest books that address the user's current concerns. Step 4: The generation unit generates summaries and reviews of the books proposed by the proposal unit. The generation unit generates summaries using a summarization algorithm and reviews based on review evaluation criteria. This allows users to understand the content of the proposed books in advance. Step 5: The provider unit provides the user with the summaries and reviews generated by the generator unit. The provider unit sends a notification to the user's device displaying the generated summaries and reviews, allowing the user to review the content of the suggested books.

[0063] (Example of form 2) An AI agent service according to an embodiment of the present invention is a system that suggests books needed in bookstores and libraries. When a user visits a bookstore or library, the AI ​​agent receives input from the user. Next, the AI ​​agent analyzes data such as the user's past reading history, areas of interest, and current concerns. Based on this analysis of the user's desires and concerns, the AI ​​agent suggests the most suitable books. Furthermore, the AI ​​agent generates summaries and reviews of the recommended books and provides them to the user. This mechanism allows busy people, indecisive people, and those with concerns to find the necessary books in a short amount of time. For example, a user might input, "I want a book to relieve work stress." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes data such as the user's past reading history, areas of interest, and current concerns. The AI ​​agent utilizes natural language processing technology to analyze the user's desires and concerns from the input. For example, it considers data on books the user has read in the past, their current areas of interest, and their concerns to suggest the most suitable books. Furthermore, the AI ​​agent generates summaries and reviews of the recommended books and provides them to the user. For example, an AI agent can generate summaries and reviews of books recommended as "books to relieve work stress" and provide them to the user. This allows the user to understand the content of the recommended books in advance. This system enables busy people, indecisive people, and people with worries to find the books they need in a short amount of time. For example, a user who wants to relieve work stress can find the perfect book by receiving suggestions from an AI agent at a bookstore or library. Also, by understanding the summaries and reviews of the books recommended by the AI ​​agent in advance, the user can choose a book that suits them. In this way, using an AI agent makes it possible to suggest books that fit the user's needs, encouraging reading habits and supporting individual growth. For example, by having an AI agent analyze a user's worries and suggest the most suitable books, it is possible to reduce the user's worries and improve their knowledge and happiness. In this way, AI agent services can efficiently find the books that users need.

[0064] The AI ​​agent service according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a generation 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, if a user inputs "I want a book to relieve work stress," the reception unit receives that information. The analysis unit analyzes the user's past reading history, areas of interest, and current concerns based on the information received by the reception unit. For example, the analysis unit retrieves the user's past reading history from a database and analyzes their areas of interest and current concerns. The analysis unit uses natural language processing technology to analyze the user's input and identify the user's wishes and concerns. The suggestion unit suggests the most suitable book based on the analysis results obtained by the analysis unit. For example, the suggestion unit uses an algorithm to recommend the most suitable book based on the user's areas of interest and past reading history. The suggestion unit can also suggest a book that addresses the user's current concerns. The generation unit generates summaries and reviews of the books suggested by the suggestion unit. The generation unit generates a book summary using, for example, a summarization algorithm, and generates a review based on review evaluation criteria. The generation unit allows the user to understand the content of the suggested book in advance. The delivery unit provides the summary and review generated by the generation unit to the user. The delivery unit, for example, sends a notification to the user's device and displays the generated summary and review. The delivery unit allows the user to confirm the content of the suggested book. As a result, the AI ​​agent service according to the embodiment allows the user to efficiently find the book they need.

[0065] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, if a user inputs "I want a book to relieve work stress," the reception unit receives that information. Specifically, in the case of text input, it receives the string of characters entered by the user using a keyboard or touchscreen; in the case of voice input, it converts the voice data recorded through the microphone into text using speech recognition technology; in the case of image input, it receives images or screenshots taken by the user and analyzes the content using image recognition technology. The reception unit centrally manages this input data and performs preprocessing to accurately understand the user's intent. For example, in the case of voice input, it performs noise reduction and speech normalization; in the case of text input, it performs spell checking and grammatical analysis. Furthermore, the reception unit converts the user's input content into an appropriate format and prepares it for passing on to the subsequent analysis unit. This allows the reception unit to handle diverse user input formats and receive information accurately and efficiently.

[0066] The analysis department analyzes users' past reading history, areas of interest, and current concerns based on information received by the reception department. For example, the analysis department retrieves users' past reading history from a database and analyzes their areas of interest and current concerns. Specifically, the user's reading history database stores information such as the titles, authors, genres, and ratings of books read in the past, and uses this data to understand the user's reading tendencies. For the analysis of areas of interest, natural language processing technology is used to extract specific keywords and themes from the user's input and past reading history. For example, if a user enters the keyword "stress relief," the analysis department identifies books that deal with similar themes among the books the user has read in the past and recognizes them as the user's area of ​​interest. For the analysis of current concerns, the analysis department analyzes the user's input in detail to identify specific concerns and desires. For example, from the input "I want a book to relieve work stress," the analysis department identifies that the user is experiencing work-related stress and extracts information to suggest books that can help alleviate it. In this way, the analysis department can comprehensively analyze the user's past reading history, areas of interest, and current concerns and provide basic information to make suggestions that are best suited to the user's needs.

[0067] The recommendation department suggests the most suitable books based on the analysis results obtained by the analysis department. The recommendation department uses algorithms that recommend the most suitable books based on, for example, the user's areas of interest and past reading history. Specifically, the recommendation algorithm selects books that match the user's preferences using methods such as collaborative filtering and content-based filtering. Collaborative filtering recommends books that have been highly rated by users with similar preferences, based on the reading history and ratings of other users. On the other hand, content-based filtering recommends books with similar content based on the user's past reading history and areas of interest. Furthermore, the recommendation department can also suggest books that address the user's current concerns. For example, if a user enters "I want a book to relieve work stress," it will suggest books on stress relief, self-help books, and relaxation books. Based on this information, the recommendation department lists the most suitable books for the user and presents them to the user. In this way, the recommendation department can efficiently suggest books that are best suited to the user's needs and help the user find the book they need.

[0068] The generation unit generates summaries and reviews of books proposed by the proposal unit. For example, the generation unit generates summaries using a summarization algorithm and reviews based on review evaluation criteria. Specifically, the summarization algorithm extracts key points and summarizes them concisely so that the content of the book can be grasped in a short time. For example, it extracts the main points and main themes of each chapter of the book and combines them to generate an overall summary. In addition, for review generation, it creates reviews according to evaluation criteria based on past reader ratings and comments. For example, it analyzes reader ratings and comments and generates reviews that incorporate a good balance of positive and negative points. Furthermore, the generation unit can also create presentations that incorporate visual elements so that users can understand the content of the proposed books in advance. For example, it provides detailed information including the book cover image, table of contents, and author's biography. In this way, the generation unit can provide information that allows users to understand the content of the proposed books in advance and use it as a reference for their selection.

[0069] The delivery unit provides users with summaries and reviews generated by the generation unit. For example, the delivery unit sends notifications to the user's device to display the generated summaries and reviews. Specifically, it provides information to the user's device, such as a smartphone, tablet, or PC, through push notifications, email, or in-app notifications. The delivery unit provides an interactive interface so that users can check the content of the suggested books. For example, when a user taps a notification, a detailed summary and review are displayed, along with information about related books and purchase links. The delivery unit can also collect user feedback and use it to improve the service. For example, users can leave ratings and comments on suggested books, and this information can be used to improve the accuracy of the suggestion and generation algorithms. This allows the delivery unit to provide users with information quickly and accurately, and to help users efficiently find the books they need.

[0070] 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 delay the timing of input acceptance to help them relax. Conversely, if the user is excited, the reception unit can speed up the timing of input acceptance to provide a quicker response. Furthermore, if the user is tired, the reception unit can adjust the timing of input acceptance to encourage a break. This allows for more appropriate timing of input acceptance by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In addition, the reception desk can customize input methods based on the user's past input. This allows for the selection of the optimal input 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, or not. 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 optimal input method.

[0072] The reception unit can filter input based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception unit filters relevant information based on their areas of interest. Furthermore, if the user has a specific area of ​​interest, the reception unit can prioritize displaying information related to that area. In addition, the reception unit can perform appropriate filtering according to the user's current situation and provide the necessary information. This allows for the efficient provision of necessary information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0073] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception desk will prioritize important input. If the user is relaxed, the reception desk may also prioritize detailed input. Furthermore, if the user is in a hurry, the reception desk can adjust the priority of input to ensure a quick response. This allows for prioritizing important information by determining input priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of input.

[0074] The reception unit can prioritize receiving input that is highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. The reception unit can also filter and prioritize the display of relevant information based on the user's current location. Furthermore, if the user is on the move, the reception unit can prioritize receiving information appropriate to their current location. In this way, by considering the user's geographical location information, 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 information data into a generating AI and have the generating AI prioritize receiving highly relevant input.

[0075] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can analyze the user's areas of interest from their social media activity and prioritize accepting relevant inputs. The reception unit can also suggest relevant inputs based on information the user has shared on social media. Furthermore, the reception unit can analyze the user's social media activity and suggest the optimal input method. This allows for the priority acceptance of relevant inputs 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 prioritize the acceptance of relevant inputs.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit may select a simpler analysis method. If the user is relaxed, the analysis unit may select a more detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit may select a method for rapid analysis. By adjusting the analysis method according to the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0077] The analysis unit can select the optimal analysis method by referring to the user's past reading history during analysis. For example, the analysis unit can select the optimal analysis method based on data of books the user has read in the past. The analysis unit can also identify areas of interest from the user's past reading history and select the optimal analysis method. Furthermore, the analysis unit can analyze the user's past reading history and select the most effective analysis method. In this way, by referring to the user's past reading history, the optimal analysis method is selected and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past reading history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0078] The analysis unit can improve the accuracy of its analysis based on the user's current concerns and areas of interest. For example, when the user inputs their current concerns, the analysis unit improves the accuracy of the analysis based on those concerns. The analysis unit can also prioritize the analysis of relevant information based on the user's areas of interest. Furthermore, the analysis unit can select the optimal analysis method considering the user's current concerns and areas of interest. This improves the accuracy of the analysis based on the user's current concerns and areas of interest, thereby obtaining more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current concerns and areas of interest data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0079] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. It can also prioritize displaying detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can adjust the priority of the analysis results to provide a quick response. This allows for the prioritization of important results by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of the analysis results.

[0080] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. The analysis unit can also filter and analyze relevant information based on the user's current location. Furthermore, if the user is on the move, the analysis unit can prioritize analyzing information relevant to their current location. This allows for the prioritization of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can analyze areas of interest from the user's social media activity to improve accuracy. The analysis unit can also improve the accuracy of its analysis based on information shared by the user on social media. Furthermore, the analysis unit can analyze the user's social media activity and select the optimal analysis method. This allows the analysis to improve accuracy by referring to the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0082] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is stressed, the suggestion unit will select a simple and easy-to-understand presentation. If the user is relaxed, the suggestion unit may select a presentation that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit may select a presentation that allows for a quick response. By adjusting the presentation of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the suggestion.

[0083] The suggestion unit can select the optimal suggestion method by referring to the user's past reading history when making suggestions. For example, the suggestion unit can select the optimal suggestion method based on data of books the user has read in the past. The suggestion unit can also identify areas of interest from the user's past reading history and select the optimal suggestion method. Furthermore, the suggestion unit can analyze the user's past reading history and select the most effective suggestion method. In this way, by referring to the user's past reading history, the optimal suggestion method is selected and the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past reading history data into a generating AI and have the generating AI select the optimal suggestion method.

[0084] The suggestion unit can improve the accuracy of its suggestions based on the user's current concerns and areas of interest. For example, when the user inputs their current concerns, the suggestion unit improves the accuracy of its suggestions based on those concerns. The suggestion unit can also prioritize suggesting relevant information based on the user's areas of interest. Furthermore, the suggestion unit can select the optimal suggestion method considering the user's current concerns and areas of interest. This allows for more appropriate suggestions by improving the accuracy of suggestions based on the user's current concerns and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current concerns and areas of interest data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0085] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying important suggestions. It can also prioritize displaying detailed suggestions if the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can adjust the priority of suggestions to ensure a quick response. This allows for prioritizing important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0086] The suggestion unit can make suggestions while considering the user's geographical location information. For example, if the user is in a specific region, the suggestion unit will prioritize suggesting information related to that region. The suggestion unit can also filter and suggest relevant information based on the user's current location. Furthermore, if the user is on the move, the suggestion unit can prioritize suggesting information appropriate to the user's current location. In this way, by considering the user's geographical location information, highly relevant information can be suggested preferentially. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of suggesting highly relevant information.

[0087] The suggestion unit can improve the accuracy of its suggestions by referencing the user's social media activity during the suggestion process. For example, the suggestion unit can analyze the user's areas of interest from their social media activity to improve accuracy. It can also improve the accuracy of its suggestions based on information shared by the user on social media. Furthermore, the suggestion unit can analyze the user's social media activity and select the optimal suggestion method. This allows the suggestion unit to improve the accuracy of its suggestions by referencing the user's social media activity. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0088] The generation unit can estimate the user's emotions and adjust the way the generated summaries and reviews are presented based on the estimated emotions. For example, if the user is stressed, the generation unit will select a simple and easy-to-understand presentation. If the user is relaxed, the generation unit may select a presentation that includes detailed information. Furthermore, if the user is in a hurry, the generation unit may select a presentation that allows for quick response. By adjusting the presentation of the generated summaries and reviews according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the summaries and reviews.

[0089] The generation unit can select the optimal generation method by referring to the user's past reading history during generation. For example, the generation unit can select the optimal generation method based on data of books the user has read in the past. The generation unit can also identify areas of interest from the user's past reading history and select the optimal generation method. Furthermore, the generation unit can analyze the user's past reading history and select the most effective generation method. In this way, by referring to the user's past reading history, the optimal generation method is selected and the accuracy of generation is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past reading history data into a generation AI and have the generation AI perform the selection of the optimal generation method.

[0090] The generation unit can improve the accuracy of its generation based on the user's current concerns and areas of interest during the generation process. For example, when the user inputs their current concerns, the generation unit improves the accuracy of its generation based on those concerns. The generation unit can also prioritize the generation of relevant information based on the user's areas of interest. Furthermore, the generation unit can select the optimal generation method considering the user's current concerns and areas of interest. This allows for the provision of more appropriate information by improving the accuracy of generation based on the user's current concerns and areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current concerns and areas of interest data into a generation AI and have the generation AI perform the generation accuracy improvement.

[0091] The generation unit can estimate the user's emotions and determine the priority of the content to be generated based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating important content. It can also prioritize generating detailed content if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can adjust the content priority to provide a quick response. This allows for the prioritization of important content by determining the priority of the generated content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the content priority.

[0092] The generation unit can perform generation while considering the user's geographical location information. For example, if the user is in a specific region, the generation unit will prioritize generating information related to that region. The generation unit can also filter and generate relevant information based on the user's current location. Furthermore, if the user is on the move, the generation unit can prioritize generating information according to the user's current location. In this way, by considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the generation of highly relevant information.

[0093] The generation unit can improve the accuracy of its generation by referring to the user's social media activity during the generation process. For example, the generation unit can analyze the user's areas of interest from their social media activity to improve accuracy. It can also improve the accuracy of its generation based on information shared by the user on social media. Furthermore, the generation unit can analyze the user's social media activity and select the optimal generation method. This allows the generation unit to improve accuracy by referring to the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation accuracy improvement.

[0094] The service provider can estimate the user's emotions and adjust the timing of service delivery based on the estimated emotions. For example, if the user is stressed, the service provider can delay the delivery to help them relax. If the user is excited, the service provider can also speed up the delivery to provide a quicker response. Furthermore, if the user is tired, the service provider can adjust the delivery timing to encourage them to take a break. This allows for information to be delivered at a more appropriate time by adjusting the delivery timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the delivery timing.

[0095] The delivery unit can select the optimal delivery method by referring to the user's past reading history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on data of books the user has read in the past. The delivery unit can also identify areas of interest from the user's past reading history and select the optimal delivery method. Furthermore, the delivery unit can analyze the user's past reading history and select the most effective delivery method. In this way, by referring to the user's past reading history, the optimal delivery method can be selected and the accuracy of the delivery can be improved. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past reading history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0096] The information delivery unit can improve the accuracy of its deliveries based on the user's current concerns and areas of interest. For example, when a user inputs their current concerns, the unit improves the accuracy of its deliveries based on those concerns. The unit can also prioritize providing relevant information based on the user's areas of interest. Furthermore, the unit can select the optimal delivery method considering the user's current concerns and areas of interest. This allows the unit to provide more appropriate information by improving the accuracy of its deliveries based on the user's current concerns and areas of interest. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input data on the user's current concerns and areas of interest into a generating AI and have the generating AI perform the accuracy improvement of the deliveries.

[0097] The service provider can estimate the user's emotions and prioritize the content offered based on those emotions. For example, if the user is stressed, the service provider will prioritize displaying important content. It can also prioritize displaying detailed content if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can adjust the priority of the content to ensure a quick response. This allows for the prioritization of important content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of the content offered.

[0098] The service provider can provide information while considering the user's geographical location. 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 filter and provide relevant information based on the user's current location. Furthermore, if the user is on the move, the service provider can prioritize providing information appropriate to their current location. This allows the service provider to prioritize providing highly relevant information by considering the user's geographical location. 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 perform the task of providing highly relevant information.

[0099] The service provider can improve the accuracy of its deliveries by referring to the user's social media activity at the time of delivery. For example, the service provider can analyze the user's areas of interest from their social media activity to improve accuracy. It can also improve the accuracy of its deliveries based on information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and select the optimal delivery method. This allows the service provider to improve the accuracy of its deliveries by referring to the user's social media activity. 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 perform the task of improving the accuracy of its deliveries.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can analyze the user's tone and speed of voice when receiving user input, and estimate the user's emotional state. For example, if the user is speaking quickly, it can estimate that they are excited and offer suggestions to help them relax. If the user is speaking slowly, it can estimate that they are calm and provide more detailed information. Furthermore, if the user's tone of voice is low, it can estimate that they are stressed and prioritize suggesting books that can help relieve stress. In this way, by analyzing the user's tone and speed of voice, more appropriate suggestions can be made.

[0102] The analytics department can include information about events and seminars that users have attended in the past, in addition to their past reading history. For example, if a user has attended a particular seminar, it can suggest books related to the seminar's theme. It can also suggest books related to events the user has attended in the past, taking into account the content of those events. Furthermore, it can analyze feedback from past events and seminars to more accurately identify the user's areas of interest. By including information about the user's past events and seminars in the analysis, it is possible to provide more accurate recommendations.

[0103] The suggestion function can estimate the user's emotions and adjust the genre of books suggested based on those emotions. For example, if the user is stressed, it can suggest relaxing fiction or entertainment books. If the user is relaxed, it can suggest self-help or business-related books. Furthermore, if the user is excited, it can suggest action or adventure books. By adjusting the genre of books suggested according to the user's emotions, it can provide more appropriate recommendations.

[0104] The generation unit can analyze not only the user's past reading history but also reviews and comments the user has written in the past. For example, it can analyze the content of reviews and comments the user has written in the past to identify the user's preferences and areas of interest. It can also suggest relevant books by considering the ratings of reviews and comments the user has written in the past. Furthermore, it can analyze the tone and sentiment of reviews and comments the user has written in the past to estimate the user's emotional state. As a result, by including the user's past reviews and comments in the analysis, it is possible to make more accurate suggestions.

[0105] The information provider can estimate the user's emotions and adjust the format of the information provided based on those emotions. For example, if the user is stressed, simple, visual information can be provided. If the user is relaxed, detailed text information can be provided. Furthermore, if the user is in a hurry, summarized information can be provided. This allows for the provision of more relevant information by adjusting the format of the information provided according to the user's emotions.

[0106] The reception desk can analyze the user's device usage history when receiving user input and suggest the most suitable input method. For example, if a user has frequently used a smartphone in the past, it will prioritize suggesting smartphone input. Similarly, if a user has used a tablet in the past, it can suggest tablet input. Furthermore, if a user has used a desktop computer in the past, it can suggest desktop input. By analyzing the user's device usage history, the system can suggest the most suitable input method and improve user convenience.

[0107] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is stressed, a simple, visual display method can be selected. If the user is relaxed, a display method including detailed text information can be selected. Furthermore, if the user is in a hurry, a method displaying summarized information can be selected. By adjusting how the analysis results are displayed according to the user's emotions, more relevant information can be provided.

[0108] The recommendation system can analyze not only the user's past reading history but also information about movies and TV shows the user has watched in the past. For example, if a user has watched a particular movie or TV show, it can suggest books related to that theme. It can also suggest related books considering the content of movies and TV shows the user has watched in the past. Furthermore, it can analyze feedback on movies and TV shows the user has watched in the past to more accurately identify the user's areas of interest. By including the user's past viewing history in the analysis, it is possible to make more accurate recommendations.

[0109] The generation unit can estimate the user's emotions and adjust the amount of information generated based on those emotions. For example, if the user is stressed, it can generate short, concise information. If the user is relaxed, it can generate detailed information. Furthermore, if the user is in a hurry, it can generate summarized information that can be quickly understood. By adjusting the amount of information generated according to the user's emotions, it is possible to provide more relevant information.

[0110] The service provider can analyze not only the user's past reading history but also information about online forums and discussions the user has previously participated in. For example, if a user has a history of participating in a particular online forum, the service can suggest books related to the forum's theme. It can also suggest books related to discussions the user has previously participated in, taking into account the content of those discussions. Furthermore, by analyzing feedback from online forums and discussions the user has previously participated in, the service can more accurately identify the user's areas of interest. This allows for more accurate recommendations by including the user's past online activities in the analysis.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives user input. User input includes text input, voice input, image input, etc. For example, if a user inputs "I want a book to relieve work stress," the reception desk receives that information. Step 2: The analysis department analyzes the user's past reading history, areas of interest, and current concerns based on the information received by the reception department. The analysis department retrieves the user's past reading history from the database and analyzes their areas of interest and current concerns. Using natural language processing technology, it analyzes the user's input to identify the user's wishes and concerns. Step 3: The recommendation unit suggests the most suitable books based on the analysis results obtained by the analysis unit. The recommendation unit uses an algorithm to recommend the most suitable books based on the user's areas of interest and past reading history. It can also suggest books that address the user's current concerns. Step 4: The generation unit generates summaries and reviews of the books proposed by the proposal unit. The generation unit generates summaries using a summarization algorithm and reviews based on review evaluation criteria. This allows users to understand the content of the proposed books in advance. Step 5: The provider unit provides the user with the summaries and reviews generated by the generator unit. The provider unit sends a notification to the user's device displaying the generated summaries and reviews, allowing the user to review the content of the suggested books.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, generation 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 reception device 38 of the smart device 14 and accepts text input or voice input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past reading history, areas of interest, and current concerns. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable book. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates summaries and reviews of the proposed books. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated summaries and reviews 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 modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, generation 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 microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past reading history, areas of interest, and current concerns. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable book. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates summaries and reviews of the proposed books. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated summaries and reviews 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 modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's past reading history, areas of interest, and current concerns. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable book. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates summaries and reviews of the proposed books. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides the generated summaries and reviews 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 modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

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

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

[0160] In 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.

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

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0164] The data processing system 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.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, generation 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 microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's past reading history, areas of interest, and current concerns. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable book. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an outline and review of the proposed book. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated outline and review 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception area that receives user input, Based on the information received by the reception department, an analysis department analyzes the user's past reading history, areas of interest, and current concerns. Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable book, A generation unit that generates a book summary and review proposed by the aforementioned proposal unit, The system includes a providing unit that provides the user with summaries and reviews generated by the generation 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 and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and 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 prioritizes input content based on those estimated 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 accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. 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 analysis method 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, the optimal analysis method is selected by referring to the user's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis based on the user's current concerns and areas of interest. 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 prioritizes the analysis results 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 analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, we refer to users' social media activity to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a suggestion, the system selects the most suitable suggestion method by referring to the user's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we improve the accuracy of the proposal based on the user's current concerns and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we will take the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, we refer to users' social media activity to improve the accuracy of those suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates user sentiment and adjusts the way summaries and reviews are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the system selects the optimal generation method by referring to the user's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the accuracy of the generation is improved based on the user's current concerns and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and determines the priority of the content to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, we refer to the user's social media activity to improve the accuracy of the generation. 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 timing of deliveries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing content, the system will refer to the user's past reading history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing a service, we improve the accuracy of the service based on the user's current concerns and 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 the content offered 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 user's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We improve the accuracy of deliveries by referencing users' social media activity during the delivery process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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, Based on the information received by the reception department, an analysis department analyzes the user's past reading history, areas of interest, and current concerns. Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable book, A generation unit that generates a book summary and review proposed by the aforementioned proposal unit, The system includes a providing unit that provides the user with summaries and reviews generated by the generation 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 and select the optimal input method. The system according to feature 1.

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

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are 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 accepts relevant input. The system according to feature 1.

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

9. The aforementioned analysis unit is During analysis, the optimal analysis method is selected by referring to the user's past reading history. The system according to feature 1.

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